This repository contains all code to generate and analyze data for:
By Emily Dolson, Wolfgang Banzhaf, and Charles Ofria
In this chapter, we discuss the importance of ecological dynamics in genetic programming (and evolutionary computation more generally) by identifying a number of successful approaches that are aleady taking advantage of them in various forms.
In particular we discuss the idea of "ecologically-mediated hints": suggestions about how to solve the problem, provided by associating them with ecological niches. We explore the trade-offs of providing these hints via two different algorithms: Lexicase selection and Eco-EA. We find that Lexicase selection performs better in cases where all of the hints are accurate. When some hints are misleading, however, Eco-EA outperforms Lexicase selection.
In the process of writing this paper, I put together a simple dashboard for exploring the effects of various parameters. The interface is not particularly nice, as it was something I threw together relatively quickly to test some assumptions. However, in case it is useful to anybody, it is available here: https://emilydolson.github.io/eco-ea-box/web/eco-ea-box.html.
The colored-boxes on the left represent individuals in the population (each row is an individual, each box is one site in it's genome; red sites are closer to 0, blue/purple sites are closer to 1). There is a histogram for each site in the genome, displaying the distribution of values for that site across the population. You can start evolution with the "start" button. The drop-down menu lets you choose a selection scheme. The text boxes let you enter a value for the corresponding parameter. Make sure to click the "reset" button after changing any settings. If you do not change a parameter, it will be set to its default value. The website will interpret clicking on a text box as changing the parameter, so only click one if you intend to enter a number. n_good and n_bad control the number of good and bad hints respectively. Good hints will be provided for the first n_good dimensions, and bad hints for the following n_bad dimensions. These numbers must sum to 10 or less.
- Makefile: for compiling experiment code (
make) and the interactive dashboard (make web) - config: contains the executable used to run all experiments described in the paper.
- source: contains source code for the experiment. source/box_world.h contains the majority of the relevant code. source/native contains the .cc file for compiling a C++ executable. source/web contains the .cc file for compiling a javascript executable (for interactive dahsboard)
- web: contains the compiled javascript for the interactive dashboard, an html file the loads it, and the d3 library to run the data visualizations.
- scripts: contains run_list files listing all of command-line instructions to run these experiments, stats.R (an R file to run all of the stats), the figure generated by that R code, the final data (all_data.csv), and a script that was used to generate the run_list files.
- Empirical: A library of tools for writing scientific software. Necessary for recompiling any of the C++ code. Specifically, the data for this paper were generated using this fork at commit 0342b5e33fe1faee08868d38ee866dd1c5223c03.
- Emscripten: For compiling the interactive web version.
- R: For running the stats. Additionally, you will need the readr, dplyr, ggplot2, brglm R packages.
- If you want to run the run_list files, the easiest option is to copy the bash lines and run them however is most convenient for you. If you happen to be running them on a computer cluster using the PBS queue system, though, you can use dist_qsub to translate them directly to .qsub files.