corrections of paper

paper/paper.bib

@@ -1,3 +1,10 @@
+@article{fister2013brief,
+  title={A brief review of nature-inspired algorithms for optimization},
+  author={Fister Jr, Iztok and Yang, Xin-She and Fister, Iztok and Brest, Janez and Fister, Du{\v{s}}an},
+  journal={arXiv preprint arXiv:1307.4186},
+  year={2013}
+}
+
 @incollection{Fister2020Continuous,
   title={Continuous Optimizers for Automatic Design and Evaluation of Classification Pipelines},
   author={Fister Jr., Iztok and Zorman, Milan and Fister, Du{\v{s}}an and Fister, Iztok},
@@ -42,13 +49,16 @@ @INPROCEEDINGS{7280767
   pages={1-8},
   doi={10.1109/IJCNN.2015.7280767}}
 
-@inproceedings{tpot,
-  title={TPOT: A tree-based pipeline optimization tool for automating machine learning},
-  author={Olson, Randal S and Moore, Jason H},
-  booktitle={Workshop on automatic machine learning},
-  pages={66--74},
-  year={2016},
-  organization={PMLR}
+
+@incollection{tpot,
+  doi = {10.1007/978-3-030-05318-5_8},
+  url = {https://doi.org/10.1007/978-3-030-05318-5_8},
+  year = {2019},
+  publisher = {Springer International Publishing},
+  pages = {151--160},
+  author = {Randal S. Olson and Jason H. Moore},
+  title = {{TPOT}: A Tree-Based Pipeline Optimization Tool for Automating Machine Learning},
+  booktitle = {Automated Machine Learning}
 }
 
 @book{bookHutter,
@@ -66,3 +76,31 @@ @book{bookHutter
   biburl    = {https://dblp.org/rec/books/sp/HKV2019.bib},
   bibsource = {dblp computer science bibliography, https://dblp.org}
 }
+
+@book{engelbrecht2007computational,
+  title={Computational intelligence: an introduction},
+  author={Engelbrecht, Andries P},
+  year={2007},
+  publisher={John Wiley \& Sons},
+  doi={10.1002/9780470512517}
+}
+
+@incollection{yang2014,
+  doi = {10.1016/b978-0-12-416743-8.00017-8},
+  url = {https://doi.org/10.1016/b978-0-12-416743-8.00017-8},
+  year = {2014},
+  publisher = {Elsevier},
+  title = {Nature-Inspired Optimization Algorithms},
+  booktitle = {Nature-Inspired Optimization Algorithms}
+  author={Yang, Xin-She},
+}
+
+@article{tzanetos2020comprehensive,
+  title={A comprehensive database of Nature-Inspired Algorithms},
+  author={Tzanetos, Alexandros and Fister Jr, Iztok and Dounias, Georgios},
+  journal={Data in Brief},
+  pages={105792},
+  year={2020},
+  publisher={Elsevier}
+  doi={10.1016/j.dib.2020.105792}
+}

paper/paper.md

@@ -22,21 +22,25 @@ bibliography: paper.bib
 
 # Summary
 
-Searching for the optimal classification pipeline in Machine Learning (ML) that provides the best results for a particular classification task involves a lot of domain-specific knowledge and numerous trial-and-error approaches. For instance, several conditions must be fulfilled for an ML method, such as proper preparation and preprocessing of input data, and selection of appropriate classifiers as well as their parameters. Due to the aforementioned specifics, mostly ML experts and data scientists were able to handle such tasks in the past.
-Automated Machine Learning (AutoML) methods are intended to automate some phases of ML tasks [@bookHutter; @Fister2020Continuous]. With the rise of AutoML methods, dealing with the ML also became available to non-experts from other related research areas. Empirical evidence shows that automation of the optimal classifier selection process, feature preprocessing steps and their hyperparameters, can be dealt with by non-experts [@NIPS2015_11d0e628; @7280767; @Fister2020Continuous].
+The growing interest and development of Machine Learning (ML) methods has led to the search for ways to use these methods in the simplest way, as in general it is considered that data preprocessing and selection of appropriate ML algorithms is a demanding and time-consuming task that requires a lot of domain-specific knowledge and the use of trial-and-error approaches. That is why Automated Machine Learning (AutoML) methods have been developed. Their purpose is to automate data preprocessing and search for ML algorithms together with their hyperparameters, in order to discover the best possible ML pipeline depending on the input data [@bookHutter; @Fister2020Continuous].
 
-AutoML can be modeled as a continuous optimization problem with several potential optimization methods applied. Nature-inspired algorithms are a kind of very efficient tool for dealing with such continuous optimization problems. We developed the AutoML framework NiaAML, written fully in the Python programming language. NiaAML is based mainly on the method initially proposed in [@Fister2020Continuous]. The framework incorporates stochastic population-based nature-inspired algorithms to search for the best classification pipeline [@Fister2020Continuous]. NiaAML is easy to use, is expandable, and can be customized to the users’ needs.
+AutoML can be modeled as a continuous optimization problem with several potential optimization methods applied. Stochastic population-based nature-inspired algorithms [@yang2014; @engelbrecht2007computational] are a very popular tool for dealing with such continuous optimization problems. These algorithms are inspired mainly by the biological principles and phenomena of the behavior of various species living in nature [@fister2013brief]. Each stochastic population-based nature-inspired algorithm is composed of a population of individuals that undergo different variation operators during the evolution process that results in new populations. We developed the AutoML framework NiaAML, written fully in the Python programming language. NiaAML is based mainly on the method initially proposed in [@Fister2020Continuous]. The framework incorporates stochastic population-based nature-inspired algorithms to search for the best classification pipeline [@Fister2020Continuous].
 
 The framework is developed in a layer style layout architecture, consisting of several components, i.e. Feature Selection algorithms, Feature Transformation algorithms and classifiers. Its task is to find a perfect combination of components with proper classifiers hyperparameters` settings to build an efficient, yet customizable classification pipeline with the help of a popular collection of nature-inspired algorithms, named NiaPy [@Vrbančič2018]. Two types of optimizations in the NiaAML follow: (1) The first to find the optimal set of components for the pipeline, and (2) The second to tune the hyperparameters. Users can choose the ML components to be included into the optimization process freely, as well as select suitable fitness functions to be used for evaluation of candidate pipelines. Input data can be in the form of numerical and categorical features, as well as missing attributes, while pipelines are exported and imported as binary files for post-hoc use. Further, they can be exported as user-friendly text files that contain all of the relevant information about the pipeline and its components. A graphical outline of the NiaAML method is presented in Fig.\autoref{fig:NiaAMLflow}.
 
 ![NiaAML flow.\label{fig:NiaAMLflow}](niaamlFlow.png)
 
-Although some similar Python frameworks for AutoML such as TPOT [@tpot] and auto-sklearn [@NIPS2015_11d0e628] exist, NiaAML is slightly different, regarding the search for a perfect pipeline with the use of stochastic population-based nature-inspired algorithms [@Fister2020Continuous].
+NiaAML is different: Compared to similar Python AutoML frameworks, such as TPOT [@tpot] and auto-sklearn [@NIPS2015_11d0e628], NiaAML [@Fister2020Continuous] comes with the following benefits:
+- it is fully modeled as a continuous optimization problem, which means that arbitrary stochastic nature-inspired population-based algorithms [@tzanetos2020comprehensive] can be used for solving this task (without any special modifications of their internal mechanisms),
+- the search for the optimal combination of ML components and proper classifiers` hyperparameters can be conducted concurrently,
+- its layer style architecture allows straightforward adding of new ML components,
+- every pipeline in the output is feasible and functional in cases of the correct specified hyperparameters' domains,
+- the included GUI simplifies the work for regular users.
 
 In conclusion, NiaAML is an AutoML framework based on stochastic population-based nature-inspired algorithms. Thus, the NiaAML framework is able to find the optimal classification pipelines built on preprocessing steps and classifiers, and, simultaneously, assuring a user-friendly experience. Working with ML is also, with the help of the NiaPy framework, more easy, adaptable, expandable and customizable. Last but not least, we can assure that more of the ML components and functionalities to the NiaAML framework are yet to come.
 
 # Statement of need
 
-A lot of users became interested in developing practical ML applications, solving industry-related problems with ML, or even studying out of curiosity. Some of the users may not be familiar with the background or characteristics of ML algorithms and the Data Science field, which means that hiring some experts for consulting is unavoidable in such cases for them. In here, we see the bits of NiaAML to the ML community. Simplicity to use, extendability, adaptability and customizability are just a sample of NiaAML recognitions. The reason for its simplicity lies within the layer style architecture, which is easy to understand and allows the user to add customizable components in an easy way. An  available Graphical User Interface as a separate application makes the use of NiaAML even easier.
+Searching for the optimal classification pipeline in ML that provides the best results for a particular classification task involves a lot of domain-specific knowledge and numerous trial-and-error approaches. For instance, several conditions must be fulfilled for an ML method, such as proper preparation and preprocessing of input data, and selection of appropriate classifiers as well as their parameters. Due to the aforementioned specifics, mostly ML experts and data scientists were able to handle such tasks in the past. Empirical evidence shows that automation of the optimal classifier selection process, feature preprocessing steps and their hyperparameters, can be dealt with by non-experts [@NIPS2015_11d0e628; @7280767; @Fister2020Continuous].  With the rise of AutoML methods, dealing with the ML also became available to non-experts from other related research areas for the purpose of developing practical ML applications, solving industry-related problems with ML, or even studying out of curiosity. In here, we see the bits of NiaAML to the ML community. Simplicity to use, extendability, adaptability and customizability are just a sample of NiaAML recognitions. The reason for its simplicity lies within the layer style architecture, which is easy to understand and allows the user to add customizable components in an easy way. An available Graphical User Interface as a separate application makes the use of NiaAML even easier.
 
 # References