publications.yml

# publications.yml lists both publication types and publications

# Types of publication (in order of listing on page)
publication_types:
  - type_id: pre-pub
    pretty: Pre-publication
  - type_id: peer-reviewed-paper
    pretty: Peer-reviewed papers
  - type_id: peer-reviewed-abstract
    pretty: Peer-reviewed abstracts
    overview: |
      These are short (usually 2-page) summaries of research recently published or
      to be published elsewhere.
  - type_id: book-chapter
    pretty: Book chapters
  - type_id: misc
    pretty: Miscellaneous
    overview: |
      Miscellaneous publications that are not peer reviewed.

################################
# Listing of publications
################################
# Valid fields
# - citation
# - year
# - venue
# - type
# - badges
# - presentation_link
# - link
# - pdf
# - bibtex_id
# - bibtex


publications:

  - citation: |
      Matthew Andres Moreno, Alexander Lalejini, and Charles Ofria. (2023).
      Tag Affinity Criteria Influence Adaptive Evolution.
      In Proceedings of the Companion Conference on Genetic and Evolutionary Computation (GECCO '23 Companion).
      Association for Computing Machinery, New York, NY, USA, 35–36.
      <https://doi.org/10.1145/3583133.3595834>
    year: 2023
    type: peer-reviewed-abstract
    venue: conference
    badges:
      - article summary
    bibtex_id: moreno_tag_2023
    bibtex: |
      ```
      @inproceedings{moreno_tag_2023,
          author = {Moreno, Matthew Andres and Lalejini, Alexander and Ofria, Charles},
          title = {Tag Affinity Criteria Influence Adaptive Evolution},
          year = {2023},
          isbn = {9798400701207},
          publisher = {Association for Computing Machinery},
          address = {New York, NY, USA},
          url = {https://doi.org/10.1145/3583133.3595834},
          doi = {10.1145/3583133.3595834},
          abstract = {This Hot-off-the-Press paper summarizes our recently published work, "Matchmaker, Matchmaker, Make Me a Match: Geometric, Variational, and Evolutionary Implications of Criteria for Tag Affinity" [8]. This work appeared in Genetic Programming and Evolvable Machines. Genetic programming systems commonly use tag matching to decide interactions between system components. However, the implications of criteria used to determine affinity between tags with respect evolutionary dynamics have not been directly studied. We investigate differences between tag-matching criteria with respect to geometric constraint and variation generated under mutation. In experiments, we find that tag-matching criteria can influence the rate of adaptive evolution and the quality of evolved solutions. Better understanding of the geometric, variational, and evolutionary properties of tag-matching criteria will facilitate more effective incorporation of tag matching into genetic programming systems. By showing that tag-matching criteria influence connectivity patterns and evolutionary dynamics, our findings also raise fundamental questions about the properties of tag-matching systems in nature.},
          booktitle = {Proceedings of the Companion Conference on Genetic and Evolutionary Computation},
          pages = {35–36},
          numpages = {2},
          keywords = {tag-based referencing, genetic programming, artificial gene regulatory networks, module-based genetic programming, event-driven genetic programming},
          location = {Lisbon, Portugal},
          series = {GECCO '23 Companion}
        }
      ```

  - citation: |
      Alexander Lalejini, Matthew Andres Moreno, Jose Guadalupe Hernandez, and Emily Dolson (2023).
      Phylogeny-informed fitness evaluation.
      arXiv. [arXiv:2306.03970](https://doi.org/10.48550/arXiv.2306.03970).
    year: 2023
    type: pre-pub
    venue: preprint
    bibtex_id: lalejini_phylogeny_informed_2023
    bibtex: |
      ```
      @misc{lalejini_phylogeny_informed_2023,
        title={Phylogeny-informed fitness estimation},
        author={Alexander Lalejini and Matthew Andres Moreno and Jose Guadalupe Hernandez and Emily Dolson},
        year={2023},
        eprint={2306.03970},
        archivePrefix={arXiv},
        primaryClass={cs.NE}
      }
      ```

  - citation: |
      Ryan Boldi, Ashley Bao, Martin Briesch, Thomas Helmuth, Dominik Sobania, Lee Spector, and Alexander Lalejini. (2023).
      The Problem Solving Benefits of Down-sampling Vary by Selection Scheme.
      In Proceedings of the Companion Conference on Genetic and Evolutionary Computation (GECCO '23 Companion).
      Association for Computing Machinery, New York, NY, USA, 527–530.
      <https://doi.org/10.1145/3583133.3590713>
    year: 2023
    type: peer-reviewed-paper
    venue: conference
    bibtex_id: boldi_problem_2023
    bibtex: |
      ```
      @inproceedings{boldi_problem_2023,
        author = {Boldi, Ryan and Bao, Ashley and Briesch, Martin and Helmuth, Thomas and Sobania, Dominik and Spector, Lee and Lalejini, Alexander},
        title = {The Problem Solving Benefits of Down-Sampling Vary by Selection Scheme},
        year = {2023},
        isbn = {9798400701207},
        publisher = {Association for Computing Machinery},
        address = {New York, NY, USA},
        url = {https://doi.org/10.1145/3583133.3590713},
        doi = {10.1145/3583133.3590713},
        abstract = {Genetic programming systems often use large training sets to evaluate candidate solutions, which can be computationally expensive. Down-sampling training sets has long been used to decrease the computational cost of evaluation in a wide range of application domains. Indeed, recent studies have shown that both random and informed down-sampling can substantially improve problem-solving success for GP systems that use lexicase parent selection. We use the PushGP framework to experimentally test whether these down-sampling techniques can also improve problem-solving success in the context of two other commonly used selection methods, fitness-proportionate and tournament selection, across eight GP problems (four program synthesis and four symbolic regression). We verified that down-sampling can benefit the problem-solving success of both fitness-proportionate and tournament selection. However, the number of problems wherein down-sampling improved problem-solving success varied by selection scheme, suggesting that the impact of down-sampling depends both on the problem and choice of selection scheme. Surprisingly, we found that down-sampling was most consistently beneficial when combined with lexicase selection as compared to tournament and fitness-proportionate selection. Overall, our results suggest that down-sampling should be considered more often when solving test-based GP problems.},
        booktitle = {Proceedings of the Companion Conference on Genetic and Evolutionary Computation},
        pages = {527–530},
        numpages = {4},
        keywords = {genetic programming, regression, program synthesis, down-sampling, selection},
        location = {Lisbon, Portugal},
        series = {GECCO '23 Companion}
      }
      ```

  - citation: |
      Ryan Boldi, Alexander Lalejini, Thomas Helmuth,and Lee Spector (2023).
      A Static Analysis of Informed Down-Samples.
      In Proceedings of the Companion Conference on Genetic and Evolutionary Computation (GECCO '23 Companion).
      Association for Computing Machinery, New York, NY, USA, 531–534.
      <https://doi.org/10.1145/3583133.3590751>
    year: 2023
    type: peer-reviewed-paper
    venue: conference
    bibtex_id: boldi_static_2023
    bibtex: |
      ```
      @inproceedings{boldi_static_2023,
        address = {Lisbon Portugal},
        title = {A {Static} {Analysis} of {Informed} {Down}-{Samples}},
        copyright = {All rights reserved},
        isbn = {9798400701207},
        url = {https://dl.acm.org/doi/10.1145/3583133.3590751},
        doi = {10.1145/3583133.3590751},
        language = {en},
        urldate = {2023-09-12},
        booktitle = {Proceedings of the {Companion} {Conference} on {Genetic} and {Evolutionary} {Computation}},
        publisher = {ACM},
        author = {Boldi, Ryan and Lalejini, Alexander and Helmuth, Thomas and Spector, Lee},
        month = jul,
        year = {2023},
        pages = {531--534}
      }
      ```

  - citation: |
      Bhaskar Kumawat, Alexander Lalejini, Monica Acosta, and Luis Zaman. (2023).
      Fluctuating environments promote evolvability by shaping adaptive variation accessible to populations.
      bioRxiv. <https://doi.org/10.1101/2023.01.04.520634>
    year: 2023
    type: pre-pub
    venue: preprint
    bibtex_id: kumawat_fluctuating_2023
    bibtex: |
      ```
      @article {kumawat_fluctuating_2023,
        author = {Kumawat, Bhaskar and Lalejini, Alexander and Acosta, Monica and Zaman, Luis},
        title = {Fluctuating environments promote evolvability by shaping adaptive variation accessible to populations},
        elocation-id = {2023.01.04.520634},
        year = {2023},
        doi = {10.1101/2023.01.04.520634},
        publisher = {Cold Spring Harbor Laboratory},
        URL = {https://www.biorxiv.org/content/early/2023/01/04/2023.01.04.520634},
        eprint = {https://www.biorxiv.org/content/early/2023/01/04/2023.01.04.520634.full.pdf},
        journal = {bioRxiv}
      }
      ```

  - citation: |
      Ryan Boldi, Martin Briesch, Dominik Sobania, Alexander Lalejini, Thomas Helmuth, Franz Rothlauf, Charles Ofria, and Lee Spector (2023).
      Informed Down-Sampled Lexicase Selection: Identifying productive training cases for efficient problem solving
      arXiv. <https://doi.org/10.48550/arXiv.2301.01488>
    year: 2023
    type: pre-pub
    venue: preprint
    bibtex_id: boldi_informed_2023
    bibtex: |
      ```
      @misc{bold_informed_2023,
        doi = {10.48550/ARXIV.2301.01488},
        url = {https://arxiv.org/abs/2301.01488},
        author = {Boldi, Ryan and Briesch, Martin and Sobania, Dominik and Lalejini, Alexander and Helmuth, Thomas and Rothlauf, Franz and Ofria, Charles and Spector, Lee},
        keywords = {Neural and Evolutionary Computing (cs.NE), FOS: Computer and information sciences, FOS: Computer and information sciences},
        title = {Informed Down-Sampled Lexicase Selection: Identifying productive training cases for efficient problem solving},
        publisher = {arXiv},
        year = {2023},
        copyright = {arXiv.org perpetual, non-exclusive license}
      }
      ```

  - citation: |
      Alexander Lalejini, Matthew Andres Moreno, and Charles Ofria. 2022.
      Tag-based module regulation for genetic programming.
      In Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO '22).
      Association for Computing Machinery, New York, NY, USA, 25–26.
      <https://doi.org/10.1145/3520304.3534060>
    year: 2022
    venue: conference
    type: peer-reviewed-abstract
    pdf: /pubs/2022-07-tag-based-regulation-ext-abstract.pdf
    badges:
      - article summary
    bibtex: |
      ```
      @inproceedings{10.1145/3520304.3534060,
        author = {Lalejini, Alexander and Moreno, Matthew Andres and Ofria, Charles},
        title = {Tag-Based Module Regulation for Genetic Programming},
        year = {2022},
        isbn = {9781450392686},
        publisher = {Association for Computing Machinery},
        address = {New York, NY, USA},
        url = {https://doi.org/10.1145/3520304.3534060},
        doi = {10.1145/3520304.3534060},
        abstract = {This Hot-off-the-Press paper summarizes our recently published work, "Tag-based regulation of modules in genetic programming improves context-dependent problem solving," published in Genetic Programming and Evolvable Machines [1]. We introduce and experimentally demonstrate tag-based genetic regulation, a genetic programming (GP) technique that allows programs to dynamically adjust which code modules to express. Tags are evolvable labels that provide a flexible naming scheme for referencing code modules. Tag-based regulation extends tag-based naming schemes to allow programs to "promote" and "repress" code modules to alter module execution patterns. We find that tag-based regulation improves problem-solving success on problems where programs must adjust how they respond to current inputs based on prior inputs; indeed, some of these problems could not be solved until regulation was added. We also identify scenarios where the correct response to an input does not change over time, rendering tag-based regulation an unnecessary functionality that can sometimes impede evolution. Broadly, tag-based regulation adds to our repertoire of techniques for evolving more dynamic computer programs and can easily be incorporated into existing tag-enabled GP systems.},
        booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference Companion},
        pages = {25–26},
        numpages = {2},
        keywords = {SignalGP, gene regulation, automatic program synthesis, tag-based referencing, genetic programming},
        location = {Boston, Massachusetts},
        series = {GECCO '22}
      }
      ```

  - citation: |
      Jose Guadalupe Hernandez, Alexander Lalejini, and Emily Dolson. 2022.
      Phylogenetic diversity predicts future success in evolutionary computation.
      In Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO '22). Association for Computing Machinery, New York, NY, USA, 23–24.
      <https://doi.org/10.1145/3520304.3534079>
    year: 2022
    venue: conference
    type: peer-reviewed-abstract
    badges:
      - article summary
    bibtex_id: 10.1145/3520304.3534079
    bibtex: |
      ```
      @inproceedings{10.1145/3520304.3534079,
        author = {Hernandez, Jose Guadalupe and Lalejini, Alexander and Dolson, Emily},
        title = {Phylogenetic Diversity Predicts Future Success in Evolutionary Computation},
        year = {2022},
        isbn = {9781450392686},
        publisher = {Association for Computing Machinery},
        address = {New York, NY, USA},
        url = {https://doi.org/10.1145/3520304.3534079},
        doi = {10.1145/3520304.3534079},
        abstract = {In our recent paper, "What can phylogenetic metrics tell us about useful diversity in evolutionary algorithms" [3], we explore the relationship between metrics of diversity based on phylogenetic topology and fitness. Our contribution is two-fold. First, we identify a technique for quantifying the relationship between diversity and fitness. Previous efforts to draw hard conclusions about this relationship had been stymied by the fact that these two properties are locked in a tight feedback loop. Second, we use this technique to assess the extent to which phylogenetic diversity leads to high-fitness solutions. We find that phylogenetic diversity is often more informative of future success in evolutionary algorithms than more commonly used diversity metrics, suggesting that is an underutilized tool in evolutionary computation research.},
        booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference Companion},
        pages = {23–24},
        numpages = {2},
        keywords = {transfer entropy, causality analysis, diversity, phylogenetic diversity},
        location = {Boston, Massachusetts},
        series = {GECCO '22}
      }
      ```

  - citation: |
      Jose Guadalupe Hernandez, Alexander Lalejini, and Charles Ofria. 2022.
      Measuring the ability of lexicase selection to find obscure pathways to optimality.
      In Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO '22). Association for Computing Machinery, New York, NY, USA, 21–22.
      <https://doi.org/10.1145/3520304.3534061>
    year: 2022
    venue: conference
    type: peer-reviewed-abstract
    # pdf: TODO
    badges:
      - article summary
    bibtex_id: 10.1145/3520304.3534061
    bibtex: |
      ```
      @inproceedings{10.1145/3520304.3534061,
        author = {Hernandez, Jose Guadalupe and Lalejini, Alexander and Ofria, Charles},
        title = {Measuring the Ability of Lexicase Selection to Find Obscure Pathways to Optimality},
        year = {2022},
        isbn = {9781450392686},
        publisher = {Association for Computing Machinery},
        address = {New York, NY, USA},
        url = {https://doi.org/10.1145/3520304.3534061},
        doi = {10.1145/3520304.3534061},
        abstract = {This Hot-off-the-Press paper summarizes our recently published work, "An Exploration of Exploration: Measuring the Ability of Lexicase Selection to Find Obscure Pathways to Optimality," published as a chapter in Genetic Programming Theory and Practice XVIII [3]. In evolutionary search, selection schemes drive populations through a problem's search space, often trading off exploitation with exploration. Indeed, problem-solving success depends on how a selection scheme balances search space exploitation with exploration. In [3], we introduce an "exploration diagnostic" that measures a selection scheme's ability to explore different pathways in a search space. We use our exploration diagnostic to investigate the exploratory capacity of lexicase selection and several of its variants: epsilon lexicase, down-sampled lexicase, cohort lexicase, and novelty lexicase. We verify that lexicase selection out-explores tournament selection, and we demonstrate that lexicase selection's ability to explore a search space is sensitive to the ratio between population size and the number of test cases used for evaluating candidate solutions. We find that relaxing lexicase selection's elitism with epsilon lexicase can further improve search space exploration. Additionally, we find that both down-sampled and cohort lexicase---two methods of applying random subsampling to test cases---substantially degrade lexicase's exploratory capacity; however, cohort partitioning better preserves exploration than down-sampling. Finally, we find evidence that the addition of novelty-based test cases can degrade lexicase selection's exploratory capacity.},
        booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference Companion},
        pages = {21–22},
        numpages = {2},
        keywords = {fitness landscapes, diagnostics, selection schemes, lexicase selection},
        location = {Boston, Massachusetts},
        series = {GECCO '22}
      }
      ```

  - citation: |
      Alexander Lalejini, Emily Dolson, Anya E. Vostinar, and Luis Zaman. 2022.
      Selection schemes from evolutionary computing show promise for directed evolution of microbes.
      In Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO '22).
      Association for Computing Machinery, New York, NY, USA, 723–726.
      <https://doi.org/10.1145/3520304.3528900>
    year: 2022
    venue: conference
    type: peer-reviewed-abstract
    # pdf: TODO
    badges:
      - article summary
    bibtex_id: 10.1145/3520304.35289
    bibtex: |
      ```
      @inproceedings{10.1145/3520304.3528900,
        author = {Lalejini, Alexander and Dolson, Emily and Vostinar, Anya E. and Zaman, Luis},
        title = {Selection Schemes from Evolutionary Computing Show Promise for Directed Evolution of Microbes},
        year = {2022},
        isbn = {9781450392686},
        publisher = {Association for Computing Machinery},
        address = {New York, NY, USA},
        url = {https://doi.org/10.1145/3520304.3528900},
        doi = {10.1145/3520304.3528900},
        abstract = {Directed microbial evolution harnesses evolutionary processes in the laboratory to construct microorganisms with enhanced or novel functional traits. Directing evolutionary processes for applied goals is fundamental to evolutionary computation, which harnesses the principles of Darwinian evolution as a general purpose search engine for solutions to computational problems. Despite overlapping aims, artificial selection methods from evolutionary computing are not commonly applied to living systems in the laboratory. Here, we summarize recent work wherein we ask if parent selection algorithms from evolutionary computation might be useful for directing the evolution of microbial populations when selecting for multiple functional traits. To do so, we developed an agent-based model of directed microbial evolution, which we used to evaluate how well three selection schemes from evolutionary computing (tournament selection, lexicase selection, and non-dominated elite selection) performed relative to schemes used in the laboratory (elite and top-10% selection). We found that lexicase selection and non-dominated elite selection generally outperformed the commonly used directed evolution approaches. Our results are informing ongoing work to transfer these techniques into the laboratory and motivate future work testing more sophisticated selection schemes from evolutionary computation in a directed evolution context.},
        booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference Companion},
        pages = {723–726},
        numpages = {4},
        keywords = {selection schemes, agent-based modeling, directed evolution, artificial selection, digital organisms},
        location = {Boston, Massachusetts},
        series = {GECCO '22}
      }
      ```

  - citation: |
      Anya E. Vostinar, Katherine G. Skocelas, Alexander Lalejini, and Luis Zaman (2022).
      Symbiosis in Digital Evolution: A Review and Future Directions.
      In Proceedings of the 2022 Conference on Artificial Life (ALIFE).
      <https://doi.org/10.1162/isal_a_00481>
    year: 2022
    venue: conference
    type: peer-reviewed-abstract
    # pdf: TODO
    badges:
      - article summary
    bibtex_id: 10.1162/isal_a_00481
    bibtex: |
      ```
        @proceedings{10.1162/isal_a_00481,
            author = {Vostinar, Anya E. and Skocelas, Katherine G. and Lalejini, Alexander and Zaman, Luis},
            title = "{Symbiosis in Digital Evolution: A Review and Future Directions}",
            volume = {ALIFE 2022: The 2022 Conference on Artificial Life},
            series = {ALIFE 2022: The 2022 Conference on Artificial Life},
            year = {2022},
            month = {07},
            doi = {10.1162/isal_a_00481},
            url = {https://doi.org/10.1162/isal\_a\_00481},
            note = {4},
            eprint = {https://direct.mit.edu/isal/proceedings-pdf/isal/34/4/2035391/isal\_a\_00481.pdf},
        }
      ```

  - citation: |
      Matthew Andres Moreno, Santiago Rodriguez Papa, Alexander Lalejini, and Charles Ofria (2021).
      SignalGP-Lite: Event-driven Genetic Programming Library for Large-Scale Artificial Life Applications.
      arXiv. <https://doi.org/10.48550/arXiv.2108.00382>
    year: 2021
    type: pre-pub
    venue: preprint
    bibtex_id: moreno_sgplite_2021
    github_repository_link: https://github.com/mmore500/signalgp-lite
    bibtex: |
      ```
        misc{moreno_sgplite_2021,
          doi = {10.48550/ARXIV.2108.00382},
          url = {https://arxiv.org/abs/2108.00382},
          author = {Moreno, Matthew Andres and Papa, Santiago Rodriguez and Lalejini, Alexander and Ofria, Charles},
          keywords = {Neural and Evolutionary Computing (cs.NE), FOS: Computer and information sciences, FOS: Computer and information sciences},
          title = {SignalGP-Lite: Event Driven Genetic Programming Library for Large-Scale Artificial Life Applications},
          publisher = {arXiv},
          year = {2021},
          copyright = {arXiv.org perpetual, non-exclusive license}
        }
      ```

  - citation: |
      Shakiba Shahbandegan, Jose Guadalupe Hernandez, Alexander Lalejini, and Emily Dolson (2022).
      Untangling phylogenetic diversity's role in evolutionary computation using a suite of diagnostic fitness landscapes.
      In Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO '22).
      Association for Computing Machinery, New York, NY, USA, 2322–2325.
      <https://doi.org/10.1145/3520304.3534028>
    year: 2022
    type: peer-reviewed-paper
    venue: workshop
    badges:
      - Awarded best student paper
    bibtex_id: 10.1145/3520304.3534028
    bibtex: |
      ```
      @inproceedings{10.1145/3520304.3534028,
        author = {Shahbandegan, Shakiba and Hernandez, Jose Guadalupe and Lalejini, Alexander and Dolson, Emily},
        title = {Untangling Phylogenetic Diversity's Role in Evolutionary Computation Using a Suite of Diagnostic Fitness Landscapes},
        year = {2022},
        isbn = {9781450392686},
        publisher = {Association for Computing Machinery},
        address = {New York, NY, USA},
        url = {https://doi.org/10.1145/3520304.3534028},
        doi = {10.1145/3520304.3534028},
        abstract = {Diversity is associated with success in evolutionary algorithms. To date, diversity in evolutionary computation research has mostly been measured by counting the number of distinct candidate solutions in the population at a given time point. Here, we aim to investigate the value of phylogenetic diversity, which takes into account the evolutionary history of a population. To understand how informative phylogenetic diversity is, we run experiments on a set of diagnostic fitness landscapes under a range of different selection schemes. We find that phylogenetic diversity can be more predictive of future success than traditional diversity metrics under some conditions, particularly for fitness landscapes with a single, challenging-to-find global optimum. Our results suggest that phylogenetic diversity metrics should be more widely incorporated into research on diversity in evolutionary computation.},
        booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference Companion},
        pages = {2322–2325},
        numpages = {4},
        keywords = {transfer entropy, phylogenetic diversity, causality analysis, diversity},
        location = {Boston, Massachusetts},
        series = {GECCO '22}
      }
      ```

  - citation: |
      Jose Guadalupe Hernandez, Alexander Lalejini, Charles Ofria (2022).
      A suite of diagnostic metrics for characterizing selection schemes.
      arXiv. <https://doi.org/10.48550/arXiv.2204.13839>
    year: 2022
    type: pre-pub
    venue: preprint
    github_repository_link: https://github.com/jgh9094/Selection-Scheme-Diagnotics-Part-I
    bibtex_id: hernandez_diagnostics_2022
    bibtex: |
      ```
      @misc{https://doi.org/10.48550/arxiv.2204.13839,
        doi = {10.48550/ARXIV.2204.13839},
        url = {https://arxiv.org/abs/2204.13839},
        author = {Hernandez, Jose Guadalupe and Lalejini, Alexander and Ofria, Charles},
        title = {A suite of diagnostic metrics for characterizing selection schemes},
        publisher = {arXiv},
        year = {2022},
        copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International}
      }
      ```

  - citation: |
      Alexander Lalejini, Austin J. Ferguson, Nkrumah A. Grant, and Charles Ofria (2022).
      The evolution of adaptive phenotypic plasticity stabilizes populations against environmental fluctuations.
      In Proceedings of the 2022 Conference on Artificial Life (ALIFE).
      <https://doi.org/10.1162/isal_a_00499>
    year: 2022
    venue: conference
    type: peer-reviewed-abstract
    pdf: /pubs/2022-04-28-consequences-of-plasticity-extended-abstract.pdf
    badges:
      - article summary
    bibtex_id: 10.1162/isal_a_00499
    bibtex: |
      ```
      @proceedings{10.1162/isal_a_00499,
        author = {Lalejini, Alexander and Ferguson, Austin J. and Grant, Nkrumah A. and Ofria, Charles},
        title = "{The evolution of adaptive phenotypic plasticity stabilizes populations against environmental fluctuations}",
        volume = {ALIFE 2022: The 2022 Conference on Artificial Life},
        series = {ALIFE 2022: The 2022 Conference on Artificial Life},
        year = {2022},
        month = {07},
        doi = {10.1162/isal_a_00499},
        url = {https://doi.org/10.1162/isal\_a\_00499},
        note = {21},
        eprint = {https://direct.mit.edu/isal/proceedings-pdf/isal/34/21/2035429/isal\_a\_00499.pdf},
      }
      ```

  - citation: |
      Alexander Lalejini, Emily Dolson, Anya E Vostinar, and Luis Zaman (2022).
      Artificial selection methods from evolutionary computing show promise for directed evolution of microbes
      eLife 11:e79665. <https://doi.org/10.7554/eLife.79665>
    year: 2022
    type: peer-reviewed-paper
    venue: journal
    github_repository_link: https://github.com/amlalejini/directed-digital-evolution
    bibtex_id: lalejini_selection_2022
    bibtex: |
      ```
      @article {10.7554/eLife.79665,
        article_type = {journal},
        title = {Artificial selection methods from evolutionary computing show promise for directed evolution of microbes},
        author = {Lalejini, Alexander and Dolson, Emily and Vostinar, Anya E and Zaman, Luis},
        editor = {Ogbunugafor, C Brandon},
        volume = 11,
        year = 2022,
        month = {aug},
        pub_date = {2022-08-02},
        pages = {e79665},
        citation = {eLife 2022;11:e79665},
        doi = {10.7554/eLife.79665},
        url = {https://doi.org/10.7554/eLife.79665},
        abstract = {Directed microbial evolution harnesses evolutionary processes in the laboratory to construct microorganisms with enhanced or novel functional traits. Attempting to direct evolutionary processes for applied goals is fundamental to evolutionary computation, which harnesses the principles of Darwinian evolution as a general purpose search engine for solutions to challenging computational problems. Despite their overlapping approaches, artificial selection methods from evolutionary computing are not commonly applied to living systems in the laboratory. In this work, we ask if parent selection algorithms-procedures for choosing promising progenitors-from evolutionary computation might be useful for directing the evolution of microbial populations when selecting for multiple functional traits. To do so, we introduce an agent-based model of directed microbial evolution, which we used to evaluate how well three selection algorithms from evolutionary computing (tournament selection, lexicase selection, and non-dominated elite selection) performed relative to methods commonly used in the laboratory (elite and top-10\% selection). We found that multi-objective selection techniques from evolutionary computing (lexicase and non-dominated elite) generally outperformed the commonly used directed evolution approaches when selecting for multiple traits of interest. Our results motivate ongoing work transferring these multi-objective selection procedures into the laboratory and a continued evaluation of more sophisticated artificial selection methods.},
        journal = {eLife},
        issn = {2050-084X},
        publisher = {eLife Sciences Publications, Ltd},
      }
      ```

  - citation: |
      Anya E. Vostinar, Katherine G. Skocelas, Alexander Lalejini, and Luis Zaman (2021).
      Symbiosis in Digital Evolution: Past, Present, and Future.
      Front. Ecol. Evol. 9:739047. doi: [10.3389/fevo.2021.739047](https://doi.org/10.3389/fevo.2021.739047)
    year: 2021
    type: peer-reviewed-paper
    venue: journal
    bibtex_id: vostinar_symbiosis_2021
    bibtex: |
      ```
      @ARTICLE{vostinar_symbiosis_2021,
        AUTHOR={Vostinar, Anya E. and Skocelas, Katherine G. and Lalejini, Alexander and Zaman, Luis},
        TITLE={Symbiosis in Digital Evolution: Past, Present, and Future},
        JOURNAL={Frontiers in Ecology and Evolution},
        VOLUME={9},
        PAGES={748},
        YEAR={2021},
        URL={https://www.frontiersin.org/article/10.3389/fevo.2021.739047},
        DOI={10.3389/fevo.2021.739047},
        ISSN={2296-701X},
        ABSTRACT={Symbiosis, the living together of unlike organisms as symbionts, is ubiquitous in the natural world. Symbioses occur within and across all scales of life, from microbial to macro-faunal systems. Further, the interactions between symbionts are multimodal in both strength and type, can span from parasitic to mutualistic within one partnership, and persist over generations. Studying the ecological and evolutionary dynamics of symbiosis in natural or laboratory systems poses a wide range of challenges, including the long time scales at which symbioses evolve de novo, the limited capacity to experimentally control symbiotic interactions, the weak resolution at which we can quantify interactions, and the idiosyncrasies of current model systems. These issues are especially challenging when seeking to understand the ecological effects and evolutionary pressures on and of a symbiosis, such as how a symbiosis may shift between parasitic and mutualistic modes and how that shift impacts the dynamics of the partner population. In digital evolution, populations of computational organisms compete, mutate, and evolve in a virtual environment. Digital evolution features perfect data tracking and allows for experimental manipulations that are impractical or impossible in natural systems. Furthermore, modern computational power allows experimenters to observe thousands of generations of evolution in minutes (as opposed to several months or years), which greatly expands the range of possible studies. As such, digital evolution is poised to become a keystone technique in our methodological repertoire for studying the ecological and evolutionary dynamics of symbioses. Here, we review how digital evolution has been used to study symbiosis, and we propose a series of open questions that digital evolution is well-positioned to answer.}
      }
      ```

  - citation: |
      Jose Guadalupe Hernandez, Alexander Lalejini, and Emily Dolson (2022)
      What Can Phylogenetic Metrics Tell us About Useful Diversity in Evolutionary Algorithms?.
      In: Banzhaf W., Trujillo L., Winkler S., Worzel B. (eds) Genetic Programming Theory and Practice XVIII.
      Genetic and Evolutionary Computation.
      Springer, Singapore. <https://doi.org/10.1007/978-981-16-8113-4_4>
    year: 2022
    venue: book
    type: book-chapter
    github_repository_link: https://github.com/emilydolson/phylodiversity-metrics-in-EC-GPTP-2021
    bibtex_id: hernandez2021phylogenetic
    bibtex: |
      ```
      @Inbook{hernandez2021phylogenetic,
        author="Hernandez, Jose Guadalupe
        and Lalejini, Alexander
        and Dolson, Emily",
        editor="Banzhaf, Wolfgang
        and Trujillo, Leonardo
        and Winkler, Stephan
        and Worzel, Bill",
        title="What Can Phylogenetic Metrics Tell us About Useful Diversity in Evolutionary Algorithms?",
        bookTitle="Genetic Programming Theory and Practice XVIII",
        year="2022",
        publisher="Springer Singapore",
        address="Singapore",
        pages="63--82",
        isbn="978-981-16-8113-4",
        doi="10.1007/978-981-16-8113-4_4",
        url="https://doi.org/10.1007/978-981-16-8113-4_4"
      }
      ```

  - citation: |
      Matthew Andres Moreno, Alexander Lalejini, and Charles Ofria (2023).
      Matchmaker, Matchmaker, Make Me a Match: Geometric, Variational, and Evolutionary Implications of Criteria for Tag Affinity
      Genetic Programming and Evolvable Machines 24, 4. <https://doi.org/10.1007/s10710-023-09448-0>
    year: 2023
    type: peer-reviewed-paper
    venue: journal
    github_repository_link: https://github.com/mmore500/tag-olympics
    bibtex_id: moreno2023matchmaker
    bibtex: |
      ```
      @article{moreno2023matchmaker,
        title = {Matchmaker, matchmaker, make me a match: geometric, variational, and evolutionary implications of criteria for tag affinity},
        volume = {24},
        issn = {1389-2576, 1573-7632},
        shorttitle = {Matchmaker, matchmaker, make me a match},
        url = {https://link.springer.com/10.1007/s10710-023-09448-0},
        doi = {10.1007/s10710-023-09448-0},
        language = {en},
        number = {1},
        urldate = {2023-04-14},
        journal = {Genetic Programming and Evolvable Machines},
        author = {Moreno, Matthew Andres and Lalejini, Alexander and Ofria, Charles},
        month = jun,
        year = {2023},
        pages = {4}
      }
      ```

  - citation: |
      Jose Guadalupe Hernandez, Alexander Lalejini, and Charles Ofria (2022)
      An Exploration of Exploration: Measuring the Ability of Lexicase Selection to Find Obscure Pathways to Optimality.
      In: Banzhaf W., Trujillo L., Winkler S., Worzel B. (eds)
      Genetic Programming Theory and Practice XVIII. Genetic and Evolutionary Computation.
      Springer, Singapore. <https://doi.org/10.1007/978-981-16-8113-4_5>
    year: 2022
    venue: book
    type: book-chapter
    github_repository_link: https://github.com/jgh9094/GPTP-2021-Exploration-Of-Exploration
    bibtex_id: hernandez2021exploration
    bibtex: |
      ```
      @Inbook{hernandez2022exploration,
        author="Hernandez, Jose Guadalupe
        and Lalejini, Alexander
        and Ofria, Charles",
        editor="Banzhaf, Wolfgang
        and Trujillo, Leonardo
        and Winkler, Stephan
        and Worzel, Bill",
        title="An Exploration of Exploration: Measuring the Ability of Lexicase Selection to Find Obscure Pathways to Optimality",
        bookTitle="Genetic Programming Theory and Practice XVIII",
        year="2022",
        publisher="Springer Singapore",
        address="Singapore",
        pages="83--107",
        isbn="978-981-16-8113-4",
        doi="10.1007/978-981-16-8113-4_5",
        url="https://doi.org/10.1007/978-981-16-8113-4_5"
      }
      ```

  - citation: |
      Alexander Lalejini, Austin J. Ferguson, Nkrumah A. Grant, and Charles Ofria. (2021).
      Adaptive Phenotypic Plasticity Stabilizes Evolution in Fluctuating Environments.
      Front. Ecol. Evol. 9:715381. doi: [10.3389/fevo.2021.715381](https://doi.org/10.3389/fevo.2021.715381)
    year: 2021
    type: peer-reviewed-paper
    venue: journal
    github_repository_link: https://github.com/amlalejini/evolutionary-consequences-of-plasticity
    bibtex_id: lalejini_adaptive_2021
    bibtex: |
      ```
      @ARTICLE{lalejini_adaptive_2021,
        AUTHOR={Lalejini, Alexander and Ferguson, Austin J. and Grant, Nkrumah A. and Ofria, Charles},
        TITLE={Adaptive Phenotypic Plasticity Stabilizes Evolution in Fluctuating Environments},
        JOURNAL={Frontiers in Ecology and Evolution},
        VOLUME={9},
        PAGES={550},
        YEAR={2021},
        URL={https://www.frontiersin.org/article/10.3389/fevo.2021.715381},
        DOI={10.3389/fevo.2021.715381},
        ISSN={2296-701X},
        ABSTRACT={Fluctuating environmental conditions are ubiquitous in natural systems, and populations have evolved various strategies to cope with such fluctuations. The particular mechanisms that evolve profoundly influence subsequent evolutionary dynamics. One such mechanism is phenotypic plasticity, which is the ability of a single genotype to produce alternate phenotypes in an environmentally dependent context. Here, we use digital organisms (self-replicating computer programs) to investigate how adaptive phenotypic plasticity alters evolutionary dynamics and influences evolutionary outcomes in cyclically changing environments. Specifically, we examined the evolutionary histories of both plastic populations and non-plastic populations to ask: (1) Does adaptive plasticity promote or constrain evolutionary change? (2) Are plastic populations better able to evolve and then maintain novel traits? And (3), how does adaptive plasticity affect the potential for maladaptive alleles to accumulate in evolving genomes? We find that populations with adaptive phenotypic plasticity undergo less evolutionary change than non-plastic populations, which must rely on genetic variation from de novo mutations to continuously readapt to environmental fluctuations. Indeed, the non-plastic populations undergo more frequent selective sweeps and accumulate many more genetic changes. We find that the repeated selective sweeps in non-plastic populations drive the loss of beneficial traits and accumulation of maladaptive alleles, whereas phenotypic plasticity can stabilize populations against environmental fluctuations. This stabilization allows plastic populations to more easily retain novel adaptive traits than their non-plastic counterparts. In general, the evolution of adaptive phenotypic plasticity shifted evolutionary dynamics to be more similar to that of populations evolving in a static environment than to non-plastic populations evolving in an identical fluctuating environment. All natural environments subject populations to some form of change; our findings suggest that the stabilizing effect of phenotypic plasticity plays an important role in subsequent adaptive evolution.}
      }
      ```
    badges:
      - 2022 ISAL Award for Outstanding Student Publication

  - citation: |
      Alexander Lalejini, Matthew Andres Moreno, and Charles Ofria.
      Tag-based regulation of modules in genetic programming improves context-dependent problem solving.
      Genet Program Evolvable Mach (2021).
      <https://doi.org/10.1007/s10710-021-09406-8>
    year: 2021
    venue: journal
    type: peer-reviewed-paper
    pdf: /pubs/lalejini-et-al-2021-tag-based-genetic-regulation.pdf
    github_repository_link: https://github.com/amlalejini/Tag-based-Genetic-Regulation-for-LinearGP
    bibtex_id: lalejini_tag-based_2021
    bibtex: |
      ```bibtex
        @article{lalejini_tag-based_2021,
          title = {Tag-based regulation of modules in genetic programming improves context-dependent problem solving},
          copyright = {All rights reserved},
          issn = {1389-2576, 1573-7632},
          url = {https://link.springer.com/10.1007/s10710-021-09406-8},
          doi = {10.1007/s10710-021-09406-8},
          language = {en},
          urldate = {2021-07-10},
          journal = {Genetic Programming and Evolvable Machines},
          volume = {22},
          number = {3},
          pages = {325--355},
          author = {Lalejini, Alexander and Moreno, Matthew Andres and Ofria, Charles},
          month = jul,
          year = {2021},
        }
      ```

  - citation: |
      Nadine Strasser, Alexander Lalejini, and Charles Ofria (2020).
      Detecting the genetic signature of mutualisms from lineages.
      [The 2020 Workshop on Symbiosis in Artificial Life](https://anyaevostinar.github.io/sal-workshop/) at the 2020 Conference on Artificial Life.
    year: 2020
    venue: workshop
    type: misc
    pdf: /pubs/strasser-2020-alife-2020-sal-workshop.pdf

  - citation: |
      Lauren Gillespie, Emily Dolson, Alexander Lalejini, and Charles Ofria (2018).
      Changing Environments Drive the Separation of Genes and Increased Evolvability in NK-Inspired Landscapes.
      Late breaking abstract at The 2018 Conference on Artificial Life.
    year: 2018
    venue: conference
    type: misc
    github_repository_link: https://github.com/leg2015/Aagos
    pdf: /pubs/gillespie-2018-changing-environments-drive-gene-separation.pdf

  - citation: |
        Austin J. Ferguson, Jose G. Hernandez, Daniel Junghans, Alexander Lalejini, Emily Dolson, Charles Ofria (2020).
        [Characterizing the Effects of Random Subsampling on Lexicase Selection.](https://link.springer.com/chapter/10.1007/978-3-030-39958-0_1)
        In: Banzhaf W., Goodman E., Sheneman L., Trujillo L., Worzel B. (eds)
        Genetic Programming Theory and Practice XVII. Genetic and Evolutionary Computation.
        Springer, Cham
    year: 2020
    venue: book
    type: book-chapter
    github_repository_link: https://github.com/FergusonAJ/gptp-2019-subsampled-lexicase
    pdf: /pubs/Ferguson_et_al_2020_Characterizing_the_Effects_of_Random_Subsampling_on_Lexicase_Selection.pdf
    bibtex_id: Ferguson-GPTP-2020
    bibtex: |
      ```bibtex
      @Inbook{Ferguson-GPTP-2020,
        author="Ferguson, Austin J.
        and Hernandez, Jose Guadalupe
        and Junghans, Daniel
        and Lalejini, Alexander
        and Dolson, Emily
        and Ofria, Charles",
        editor="Banzhaf, Wolfgang
        and Goodman, Erik
        and Sheneman, Leigh
        and Trujillo, Leonardo
        and Worzel, Bill",
        title="Characterizing the Effects of Random Subsampling on Lexicase Selection",
        bookTitle="Genetic Programming Theory and Practice XVII",
        year="2020",
        publisher="Springer International Publishing",
        address="Cham",
        pages="1--23",
        abstract="Lexicase selection is a proven parent-selection algorithm designed for genetic programming problems, especially for uncompromising test-based problems where many distinct test cases must all be passed. Previous work has shown that random subsampling techniques can improve lexicase selection's problem-solving success; here, we investigate why. We test two types of random subsampling lexicase variants: down-sampled lexicase, which uses a random subset of all training cases each generation; and cohort lexicase, which collects candidate solutions and training cases into small groups for testing, reshuffling those groups each generation. We show that both of these subsampling lexicase variants improve problem-solving success by facilitating deeper evolutionary searches; that is, they allow populations to evolve for more generations (relative to standard lexicase) given a fixed number of test-case evaluations. We also demonstrate that the subsampled variants require less computational effort to find solutions, even though subsampling hinders lexicase's ability to preserve specialists. Contrary to our expectations, we did not find any evidence of systematic loss of phenotypic diversity maintenance due to subsampling, though we did find evidence that cohort lexicase is significantly better at preserving phylogenetic diversity than down-sampled lexicase.",
        isbn="978-3-030-39958-0",
        doi="10.1007/978-3-030-39958-0_1",
        url="https://doi.org/10.1007/978-3-030-39958-0_1"
      }
      ```

  - citation: |
      Alexander Lalejini, Emily Dolson, Clifford Bohm, Austin J. Ferguson, David P. Parsons, Penelope Faulkner Rainford, Paul Richmond, and Charles Ofria (2019).
      [Data Standards for Artificial Life Software.](https://www.mitpressjournals.org/doi/abs/10.1162/isal_a_00213)
      The 2019 Conference on Artificial Life, 507–514. [https://doi.org/10.1162/isal_a_00213](https://doi.org/10.1162/isal_a_00213)
    year: 2019
    venue: conference
    type: peer-reviewed-paper
    github_repository_link: https://github.com/alife-data-standards/alife-data-standards
    presentation_link: https://docs.google.com/presentation/d/19fQMsfnYh154G5ZdcJfsjcap6MKjG57p-VncMsjwbQY/edit?usp=sharing
    pdf: /pubs/Lalejini_et_al_2019_Data_Standards_For_Artificial_Life_Software.pdf
    bibtex_id: Lalejini2019-DataStandards
    bibtex: |
      ```bibtex
        @article{Lalejini2019-DataStandards,
          author = {Lalejini, Alexander and Dolson, Emily and Bohm, Clifford and Ferguson, Austin J. and Parsons, David P. and Rainford, Penelope Faulkner and Richmond, Paul and Ofria, Charles},
          title = {Data Standards for Artificial Life Software},
          journal = {The 2019 Conference on Artificial Life},
          volume = {},
          number = {31},
          pages = {507-514},
          year = {2019},
          doi = {10.1162/isal\_a\_00213},
          URL = { https://www.mitpressjournals.org/doi/abs/10.1162/isal_a_00213 },
          eprint = { https://www.mitpressjournals.org/doi/pdf/10.1162/isal_a_00213 }
        }
      ```

  - citation: |
      Jose Guadalupe Hernandez, Alexander Lalejini, Emily Dolson, and Charles Ofria. (2019).
      [Random subsampling improves performance in lexicase selection.](https://dl.acm.org/citation.cfm?id=3326900)
      In Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO '19), Manuel López-Ibáñez (Ed.).
      ACM, New York, NY, USA, 2028-2031. DOI: [https://doi.org/10.1145/3319619.3326900](https://doi.org/10.1145/3319619.3326900)
    year: 2019
    venue: workshop
    type: peer-reviewed-paper
    github_repository_link: https://github.com/amlalejini/GECCO-2019-cohort-lexicase
    presentation_link: https://docs.google.com/presentation/d/1E4rjdK9v5MEKPKhKlosPb-M_ldYQP1Q_VEz2LLOOjdc/edit?usp=sharing
    pdf: /pubs/Hernandez_et_al_2019_Random_Subsampling_Improves_Performance_in_Lexicase_Selection.pdf
    bibtex_id: Hernandez2019-SubsamplingLexicase
    bibtex: |
      ```bibtex
      @inproceedings{Hernandez2019-SubsamplingLexicase,
        author = {Hernandez, Jose Guadalupe and Lalejini, Alexander and Dolson, Emily and Ofria, Charles},
        title = {Random Subsampling Improves Performance in Lexicase Selection},
        booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference Companion},
        series = {GECCO '19},
        year = {2019},
        isbn = {978-1-4503-6748-6},
        location = {Prague, Czech Republic},
        pages = {2028--2031},
        numpages = {4},
        url = {http://doi.acm.org/10.1145/3319619.3326900},
        doi = {10.1145/3319619.3326900},
        acmid = {3326900},
        publisher = {ACM},
        address = {New York, NY, USA},
        keywords = {cohort lexicase, cohorts, down-sampled lexicase, genetic programming, lexicase selection, parent selection, program synthesis},
      }
      ```

  - citation: |
      Emily Dolson, Alexander Lalejini, Steven Jorgensen, and Charles Ofria. (2020).
      [Interpreting the Tape of Life: Ancestry-based metrics and visualizations](https://doi.org/10.1162/artl_a_00313)
      provide insights and intuition about evolutionary dynamics. Artificial Life.
      MIT Press. DOI: [10.1162/artl_a_00313](https://doi.org/10.1162/artl_a_00313)
    year: 2020
    venue: journal
    type: peer-reviewed-paper
    github_repository_link: https://github.com/emilydolson/interpreting_the_tape_of_life
    pdf: /pubs/Dolson_et_al_2020_Interpreting_the_Tape_of_Life.pdf
    bibtex_id: dolson-2020-interpreting-the-tape-of-life
    bibtex: |
      ```
      @article{dolson-2020-interpreting-the-tape-of-life,
        author = {Dolson, Emily and Lalejini, Alexander and Jorgensen, Steven and Ofria, Charles},
        title = {Interpreting the Tape of Life: Ancestry-Based Analyses Provide Insights and Intuition about Evolutionary Dynamics},
        journal = {Artificial Life},
        volume = {26},
        number = {1},
        pages = {58-79},
        year = {2020},
        doi = {10.1162/artl\_a\_00313},
        note ={PMID: 32027535},
        URL = { https://doi.org/10.1162/artl_a_00313 },
        eprint = { https://doi.org/10.1162/artl_a_00313 }
      }
      ```

  - citation: |
      Alexander Lalejini and Charles Ofria. (2019).
      [Tag-accessed memory for genetic programming.](https://dl.acm.org/citation.cfm?doid=3319619.3321892)
      In Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO '19), Manuel López-Ibáñez (Ed.).
      ACM, New York, NY, USA, 346-347.
      DOI: [https://doi.org/10.1145/3319619.3321892](https://doi.org/10.1145/3319619.3321892)
    year: 2019
    venue: conference
    type: peer-reviewed-abstract
    github_repository_link: https://github.com/amlalejini/GECCO-2019-tag-accessed-memory
    presentation_link: https://docs.google.com/presentation/d/1j4IusMVaNVOEcxFkIcveNyB_sfaFowQUFcLqLloCa5M/edit?usp=sharing
    pdf: /pubs/Lalejini_Ofria_2019_Tag-accessed_Memory.pdf
    bibtex_id: Lalejini2019-TagMem
    bibtex: |
      ```bibtex
      @inproceedings{Lalejini2019-TagMem,
        author = {Lalejini, Alexander and Ofria, Charles},
        title = {Tag-accessed Memory for Genetic Programming},
        booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference Companion},
        series = {GECCO '19},
        year = {2019},
        isbn = {978-1-4503-6748-6},
        location = {Prague, Czech Republic},
        pages = {346--347},
        numpages = {2},
        url = {http://doi.acm.org/10.1145/3319619.3321892},
        doi = {10.1145/3319619.3321892},
        acmid = {3321892},
        publisher = {ACM},
        address = {New York, NY, USA},
        keywords = {genetic programming, linear genetic programming, memory access, tag-based referencing, tags},
      }
      ```

  - citation: |
      Clifford Bohm, Alexander Lalejini, Jory Schossau, and Charles Ofria. (2019).
      [MABE 2.0: an introduction to MABE and a road map for the future of MABE development.](https://dl.acm.org/citation.cfm?id=3326825)
      In Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO '19), Manuel López-Ibáñez (Ed.).
      ACM, New York, NY, USA, 1349-1356. DOI: [https://doi.org/10.1145/3319619.3326825](https://doi.org/10.1145/3319619.3326825)
    year: 2019
    venue: workshop
    type: peer-reviewed-paper
    pdf: /pubs/Bohm_et_al_2019_MABE2.pdf
    bibtex_id: Bohm2019-MABE2
    bibtex: |
      ```bibtex
      @inproceedings{Bohm2019-MABE2,
        author = {Bohm, Clifford and Lalejini, Alexander and Schossau, Jory and Ofria, Charles},
        title = {MABE 2.0: An Introduction to MABE and a Road Map for the Future of MABE Development},
        booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference Companion},
        series = {GECCO '19},
        year = {2019},
        isbn = {978-1-4503-6748-6},
        location = {Prague, Czech Republic},
        pages = {1349--1356},
        numpages = {8},
        url = {http://doi.acm.org/10.1145/3319619.3326825},
        doi = {10.1145/3319619.3326825},
        acmid = {3326825},
        publisher = {ACM},
        address = {New York, NY, USA},
        keywords = {MABE, artificial life, empirical library, evolution, evolutionary computation, modular agent-based evolver, open source, software development},
      }
      ```

  - citation: |
      Alexander Lalejini and Charles Ofria. (2018).
      [Evolving reactive agents with SignalGP](https://doi.org/10.1162/isal_a_00069).
      In Proceedings of the 2018 Conference on Artificial Life (ALIFE).
      Edited by Takashi Ikegami, Nathaniel Virgo, Olaf Witkowski, Mizuki Oka, Reiji Suzuki, and Hiroyuki Iizuka.
      pp. 368-369. DOI: [10.1162/isal_a_00069](https://doi.org/10.1162/isal_a_00069). MIT Press.
    year: 2018
    venue: conference
    type: peer-reviewed-abstract
    presentation_link: https://docs.google.com/presentation/d/1iMp2ndNikf_VMVkdxW3WUCtuaE2mmGr16p8Z-RixenQ/edit?usp=sharing
    github_repository_link: https://github.com/amlalejini/GECCO-2018-Evolving-Event-driven-Programs-with-SignalGP
    pdf: /pubs/evolving-reactive-agents-with-signalgp.pdf
    badges:
      - article summary
    bibtex_id: Lalejini2018-ALIFE
    bibtex: |
      ```bibtex
      @inproceedings{Lalejini2018-ALIFE,
        address = {Cambridge, MA},
        author = {Lalejini, Alexander and Ofria, Charles},
        booktitle = {The 2018 Conference on Artificial Life},
        doi = {10.1162/isal_a_00069},
        pages = {368--369},
        publisher = {MIT Press},
        title = {{Evolving Reactive Agents with SignalGP}},
        url = {https://www.mitpressjournals.org/doi/abs/10.1162/isal{\_}a{\_}00069},
        year = {2018}
      }
      ```

  - citation: |
      Emily Dolson, Alexander Lalejini, Steven Jorgensen, and Charles Ofria. (2018).
      [Quantifying the tape of life: Ancestry-based metrics provide insights and intuition
      about evolutionary dynamics.](https://doi.org/10.1162/isal_a_00020)
      In Proceedings of the 2018 Conference on Artificial Life (ALIFE).
      Edited by Takashi Ikegami, Nathaniel Virgo, Olaf Witkowski, Mizuki Oka, Reiji Suzuki, and Hiroyuki Iizuka.
      pp. 75-82. DOI: [10.1162/isal_a_00020](https://doi.org/10.1162/isal_a_00020). MIT Press.
    year: 2018
    venue: conference
    type: peer-reviewed-paper
    badges: "International Society for Artificial Life Outstanding Student Publication Award"
    presentation_link: https://docs.google.com/presentation/d/1dV044J3JzfLrdgfwWS_4Bq8-3UHOqvQQgmqEZEkrcCc/edit?usp=sharing
    github_repository_link: https://github.com/stevenjson/ALife2018-Lineage
    pdf: /pubs/quantifying-the-tape-of-life.pdf
    bibtex_id: Dolson2018-ALIFE
    bibtex: |
      ```bibtex
      @inproceedings{Dolson2018-ALIFE,
        address = {Cambridge, MA},
        author = {Dolson, Emily and Lalejini, Alexander and Jorgensen, Steven and Ofria, Charles},
        booktitle = {The 2018 Conference on Artificial Life},
        doi = {10.1162/isal_a_00020},
        pages = {75--82},
        publisher = {MIT Press},
        title = {{Quantifying the Tape of Life: Ancestry-based Metrics Provide Insights and Intuition about Evolutionary Dynamics}},
        url = {https://www.mitpressjournals.org/doi/abs/10.1162/isal{\_}a{\_}00020},
        year = {2018}
      }
      ```

  - citation: |
      Alexander Lalejini and Charles Ofria. (2018).
      [Evolving event-driven programs with SignalGP](https://doi.org/10.1145/3205455.3205523).
      In Proceedings of the 2018 Genetic and Evolutionary Computation Conference (GECCO).
      pp. 1135-1142.  DOI: [10.1145/3205455.3205523](https://doi.org/10.1145/3205455.3205523). ACM.
    year: 2018
    venue: "conference"
    type: peer-reviewed-paper
    presentation_link: "https://docs.google.com/presentation/d/1YKn9Hg7GNAsAEHNcHUKnWXq0HZD7k6zsM8irkIX-mlw/edit?usp=sharing"
    github_repository_link: https://github.com/amlalejini/GECCO-2018-Evolving-Event-driven-Programs-with-SignalGP
    pdf: /pubs/evolving-event-driven-programs-with-signalgp.pdf
    bibtex_id: Lalejini2018-GECCO
    bibtex: |
      ```bibtex
      @inproceedings{Lalejini2018-GECCO,
        abstract = {We present SignalGP, a new genetic programming (GP) technique designed to incorporate the event-driven programming paradigm into computational evolution's toolbox. Event-driven programming is a software design philosophy that simplifies the development of reactive programs by automatically triggering program modules (event-handlers) in response to external events, such as signals from the environment or messages from other programs. SignalGP incorporates these concepts by extending existing tag-based referencing techniques into an event-driven context. Both events and functions are labeled with evolvable tags; when an event occurs, the function with the closest matching tag is triggered. In this work, we apply SignalGP in the context of linear GP. We demonstrate the value of the event-driven paradigm using two distinct test problems (an environment coordination problem and a distributed leader election problem) by comparing SignalGP to variants that are otherwise identical, but must actively use sensors to process events or messages. In each of these problems, rapid interaction with the environment or other agents is critical for maximizing fitness. We also discuss ways in which SignalGP can be generalized beyond our linear GP implementation.},
        address = {New York, New York, USA},
        archivePrefix = {arXiv},
        arxivId = {1804.05445},
        author = {Lalejini, Alexander and Ofria, Charles},
        booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference on - GECCO '18},
        doi = {10.1145/3205455.3205523},
        eprint = {1804.05445},
        isbn = {9781450356183},
        keywords = {event-driven computation,event-driven programming,genetic programming,linear genetic programming,signalgp,tags},
        pages = {1135--1142},
        publisher = {ACM Press},
        title = {{Evolving event-driven programs with SignalGP}},
        url = {http://arxiv.org/abs/1804.05445{\%}0Ahttp://dx.doi.org/10.1145/3205455.3205523 http://dl.acm.org/citation.cfm?doid=3205455.3205523},
        year = {2018}
      }
      ```

  - citation: |
      Alexander Lalejini, Michael J. Wiser, and Charles Ofria. (2017).
      [Gene duplications drive the evolution of complex traits and regulation](https://doi.org/10.7551/ecal_a_045).
      In Proceedings of the 14th European Conference on Artificial Life (ECAL) 2017.
      Edited by Carole Knibbe, Guillaume Beslon, David Parsons, Dusan Misevic,
      Jonathan Rouzaud-Cornabas, Nicolas Bredèche, Salima Hassas, Olivier Simonin,
      and Hédi Soula. Vol 14. pp. 257-264.
      DOI: [10.7551/ecal_a_045](https://doi.org/10.7551/ecal_a_045). MIT Press.
    link: https://www.mitpressjournals.org/doi/abs/10.1162/isal_a_045
    year: 2017
    venue: "conference"
    type: peer-reviewed-paper
    presentation_link: https://docs.google.com/presentation/d/1by_lcyOZ2872BoSard0-b9LsejGxly9lxOobDiU0zy8/edit?usp=sharing
    pdf: /pubs/gene-duplications-drive-the-evolution-of-complex-traits-and-regulation.pdf
    bibtex_id: Lalejini2017-ECAL
    bibtex: |
      ```bibtex
      @inproceedings{Lalejini2017-ECAL,
        address = {Cambridge, MA},
        author = {Lalejini, Alexander and Wiser, Michael J. and Ofria, Charles},
        booktitle = {Proceedings of the 14th European Conference on Artificial Life ECAL 2017},
        doi = {10.7551/ecal_a_045},
        isbn = {978-0-262-34633-7},
        month = {sep},
        number = {September},
        pages = {257--264},
        publisher = {MIT Press},
        title = {{Gene duplications drive the evolution of complex traits and regulation}},
        url = {https://www.mitpressjournals.org/doi/abs/10.1162/isal{\_}a{\_}045},
        year = {2017}
      }
      ```

  - citation: |
      Alexander Lalejini and Charles Ofria. (2016). [The evolutionary origins of
      phenotypic plasticity](https://doi.org/10.7551/978-0-262-33936-0-ch063).
      In Artificial Life XV: Proceedings of the Fifteenth
      International Conference on the Synthesis and Simulation of Living Systems (ALIFE).
      Edited by Carlos Gershenson, Tom Froese, Jesus M. Siqueiros, Wendy Aguilar,
      Eduardo J. Izquierdo and Hiroki Sayama. Vol 14. pp. 372-379.
      DOI: [10.7551/978-0-262-33936-0-ch063](https://doi.org/10.7551/978-0-262-33936-0-ch063). MIT Press.
    year: 2016
    venue: "conference"
    type: peer-reviewed-paper
    badges: "best student paper"
    presentation_link: https://docs.google.com/presentation/d/12AzO1cYUaEgHHlo6TdKaVV-UKG_hTCtN7vDz6waleLY/edit?usp=sharing
    link: https://www.mitpressjournals.org/doi/abs/10.1162/978-0-262-33936-0-ch063
    pdf: /pubs/the-evolutionary-origins-of-phenotypic-plasticity.pdf
    bibtex_id: Lalejini2016-ALIFE
    bibtex: |
      ```bibtex
      @inproceedings{Lalejini2016-ALIFE,
        abstract = {Many effective and innovative survival mechanisms used by natural organisms rely on the capacity for phenotypic plasticity; that is, the ability of a genotype to alter how it is expressed based on the current environmental conditions. Understanding the evolution of phenotypic plasticity is an important step towards understanding the origins of many types of biological complexity, as well as to meeting challenges in evolutionary computation where dynamic solutions are required. Here, we leverage the Avida Digital Evolution Platform to experimentally explore the selective pressures and evolutionary pathways that lead to phenotypic plasticity. We present evolved lineages wherein unconditional traits tend to evolve first; next, imprecise forms of phenotypic plasticity often ap-pear before optimal forms finally evolve. We visualize the phenotypic states traversed by evolved lineages across environments with differing rates of mutations and environmental change. We see that under all conditions, populations can fail to evolve phenotypic plasticity, instead relying on mutation-based solutions.},
        address = {Cambridge, MA},
        author = {Lalejini, Alexander and Ofria, Charles},
        booktitle = {Proceedings of the Artificial Life Conference 2016},
        doi = {10.7551/978-0-262-33936-0-ch063},
        isbn = {978-0-262-33936-0},
        pages = {372--379},
        publisher = {MIT Press},
        title = {{The Evolutionary Origins of Phenotypic Plasticity}},
        url = {https://www.mitpressjournals.org/doi/abs/10.1162/978-0-262-33936-0-ch063},
        year = {2016}
      }
      ```

  - citation: |
      Christopher R. Hudson, Alexander Lalejini, Brandon Odom, Daniel W. Carruth, Cindy L. Bethel,
      Phillip J. Durst, and Christopher Goodin. (2015).
      ANVEL-ROS: The integration of the robot operating system with a high-fidelity simulator.
      In Proceedings of the 2015 Ground Vehicle Systems Engineering and Technology Symposium (GVSETS).
      p. 378.
    year: 2015
    venue: conference
    type: peer-reviewed-paper
    pdf: /pubs/ANVEL-ROS-integration.pdf
    bibtex_id: Hudson2015-GVSETS
    bibtex: |
      ```bibtex
      @inproceedings{Hudson2015-GVSETS,
        author = {Hudson, Christopher R and Lalejini, Alexander and Odom, Brandon and Carruth, Daniel W and Bethel, Cindy L and Durst, Phillip J and Goodin, Christopher},
        booktitle = {2015 Ground Vehicle Systems Engineering and Technology Symposium},
        title = {{Anvel-ROS: the Integration of the Robot Operating System With a High-Fidelity Simulator}},
        year = {2015}
        }
      ```

  - citation: |
      Alexander Lalejini, Dexter Duckworth, Richard Sween, Cindy L. Bethel, and Daniel W. Carruth. (2014).
      [Evaluation of supervisory control interfaces for mobile robot integration with tactical teams](https://doi.org/10.1109/ARSO.2014.7020971).
      In Proceedings of 2014 IEEE Workshop on Advanced Robotics and Its Social Impacts (ARSO).
      pp 1-6.
      DOI: [10.1109/ARSO.2014.7020971](https://doi.org/10.1109/ARSO.2014.7020971). IEEE.
    year: 2014
    venue: workshop
    type: peer-reviewed-paper
    pdf: /pubs/evaluation-of-supervisory-control-interfaces.pdf
    bibtex_id: Lalejini2014-ARSO
    bibtex: |
      ```
      @inproceedings{Lalejini2014-ARSO,
        abstract = {As robotic systems become more sophisticated, they are increasingly called upon to accompany humans in high-stress environments. This research was conducted to support the integration of robotic systems into tactical teams operating in challenging and stressful environments. Robotic systems used to assist tactical teams will need to support some form of autonomy; these systems must be capable of providing operators supervisory control in cases of unpredictable real-time events. An evaluation of the relative effectiveness of three different methods of supervisory control of an autonomously operated mobile robot system was conducted: (1) hand gestures using a Microsoft Kinect, (2) an interactive Android application on a hand-held mobile device, and (3) verbal commands issued through a headset. These methods of supervisory control were compared to a teleoperated robot using a gamepad controller. The results from this pilot study determined that the touchscreen device was the easiest interface to use to override the robot's next intended movement (L2(3,23)=11.413, p=.003, d=1.58) and was considered the easiest interface to use overall (L2(3,23)=8.078, p=.044, d=.93). The results also indicate that the touchscreen device provided the most enjoyable, satisfying, and engaging interface of the four user interfaces evaluated.},
        author = {Lalejini, Alexander and Duckworth, Dexter and Sween, Richard and Bethel, Cindy L. and Carruth, Daniel},
        booktitle = {2014 IEEE International Workshop on Advanced Robotics and its Social Impacts},
        doi = {10.1109/ARSO.2014.7020971},
        isbn = {978-1-4799-6968-5},
        issn = {21627576},
        month = {sep},
        number = {January},
        pages = {1--6},
        publisher = {IEEE},
        title = {{Evaluation of supervisory control interfaces for mobile robot integration with tactical teams}},
        url = {http://ieeexplore.ieee.org/document/7020971/},
        volume = {2015-Janua},
        year = {2014}
      }
      ```

  - citation: |
      Emily Dolson, Alexander Lalejini, and Charles Ofria. (2019).
      [Exploring Genetic Programming Systems with MAP-Elites](https://doi.org/10.1007/978-3-030-04735-1_1).
      In Banzhaf W., Spector L., Sheneman L. (eds) Genetic Programming Theory and Practice XVI. Genetic and Evolutionary Computation. Springer, Cham
    year: 2019
    venue: book
    type: book-chapter
    github_repository_link: https://github.com/amlalejini/GPTP-2018-Exploring-Genetic-Programming-Systems-with-MAP-Elites
    pdf: /pubs/Dolson_et_al_2019_Exploring_Genetic_Programming_Systems_with_MAP-Elites.pdf
    bibtex_id: Dolson2019-GPTP
    bibtex: |
      ```
      @Inbook{Dolson2019-GPTP,
        author="Dolson, Emily
        and Lalejini, Alexander
        and Ofria, Charles",
        editor="Banzhaf, Wolfgang
        and Spector, Lee
        and Sheneman, Leigh",
        title="Exploring Genetic Programming Systems with MAP-Elites",
        bookTitle="Genetic Programming Theory and Practice XVI",
        year="2019",
        publisher="Springer International Publishing",
        address="Cham",
        pages="1--16",
        abstract="MAP-Elites is an evolutionary computation technique that has proven valuable for exploring and illuminating the genotype-phenotype space of a computational problem. In MAP-Elites, a population is structured based on phenotypic traits of prospective solutions; each cell represents a distinct combination of traits and maintains only the most fit organism found with those traits. The resulting map of trait combinations allows the user to develop a better understanding of how each trait relates to fitness and how traits interact. While MAP-Elites has not been demonstrated to be competitive for identifying the optimal Pareto front, the insights it provides do allow users to better understand the underlying problem. In particular, MAP-Elites has provided insight into the underlying structure of problem representations, such as the value of connection cost or modularity to evolving neural networks. Here, we extend the use of MAP-Elites to examine genetic programming representations, using aspects of program architecture as traits to explore. We demonstrate that MAP-Elites can generate programs with a much wider range of architectures than other evolutionary algorithms do (even those that are highly successful at maintaining diversity), which is not surprising as this is the purpose of MAP-Elites. Ultimately, we propose that MAP-Elites is a useful tool for understanding why genetic programming representations succeed or fail and we suggest that it should be used to choose selection techniques and tune parameters.",
        isbn="978-3-030-04735-1",
        doi="10.1007/978-3-030-04735-1_1",
        url="https://doi.org/10.1007/978-3-030-04735-1_1"
      }
      ```

  - citation: |
      Alexander Lalejini and Charles Ofria (2019).
      [What Else Is in an Evolved Name? Exploring Evolvable Specificity with SignalGP](https://doi.org/10.1007/978-3-030-04735-1_6).
      In Banzhaf W., Spector L., Sheneman L. (eds) Genetic Programming Theory and Practice XVI. Genetic and Evolutionary Computation. Springer, Cham
    year: 2019
    venue: book
    type: book-chapter
    presentation_link: https://docs.google.com/presentation/d/1KF9iQb08CtQiWYD4py53DDJD_kjzT2U6Z3CtqfF8Bdc/edit?usp=sharing
    github_repository_link: https://github.com/amlalejini/GPTP-2018-Exploring-Evolvable-Specificity-with-SignalGP
    pdf: /pubs/Lalejini_Ofria_2019_What_Else_Is_in_an_Evolved_Name.pdf
    bibtex_id: Lalejini2019-GPTP
    bibtex: |
      ```bibtex
      @Inbook{Lalejini2019-GPTP,
        author="Lalejini, Alexander
        and Ofria, Charles",
        editor="Banzhaf, Wolfgang
        and Spector, Lee
        and Sheneman, Leigh",
        title="What Else Is in an Evolved Name? Exploring Evolvable Specificity with SignalGP",
        bookTitle="Genetic Programming Theory and Practice XVI",
        year="2019",
        publisher="Springer International Publishing",
        address="Cham",
        pages="103--121",
        abstract="Tags are evolvable labels that provide genetic programs a flexible mechanism for specification. Tags are used to label and refer to programmatic elements, such as functions or jump targets. However, tags differ from traditional, more rigid methods for handling labeling because they allow for inexact references; that is, a referring tag need not exactly match its referent. Here, we explore how adjusting the threshold for how what qualifies as a match affects adaptive evolution. Further, we propose broadened applications of tags in the context of a genetic programming (GP) technique called SignalGP. SignalGP gives evolution direct access to the event-driven paradigm. Program modules in SignalGP are tagged and can be triggered by signals (with matching tags) from the environment, from other agents, or due to internal regulation. Specifically, we propose to extend this tag based system to: (1) provide more fine-grained control over module execution and regulation (e.g., promotion and repression) akin to natural gene regulatory networks, (2) employ a mosaic of GP representations within a single program, and (3) facilitate major evolutionary transitions in individuality (i.e., allow hierarchical program organization to evolve de novo).",
        isbn="978-3-030-04735-1",
        doi="10.1007/978-3-030-04735-1_6",
        url="https://doi.org/10.1007/978-3-030-04735-1_6"
      }
      ```