Interaction networks for ecological communities created by selection schemes

In our paper, "Ecological theory provides insights into evolutionary computation", we show that different techniques for selecting individuals to reproduce create radically different ecological communities. These differences can be characterized using interaction networks: weighted, directed graphs in which nodes represent individuals in the population and edges represent their effects on each other. Red edges indicate harmful interactions and blue edges indicate beneficial interactions. For example, a red edge from node A to node B indicates that individual A harms individual B (i.e. B has a lower chance of reproducing when A is present than if A were absent). Line weight and color saturation indicate interaction intensity (you can also mouse-over edges to see their precise weights).

This page contains a simple simulation to facilitate easy exploration of interaction networks in evolutionary computation. Individuals are lists of numbers (the length of these lists is determined by the "number of traits" slider). Our goal in this toy example is to evolve these lists to have the highest possible sum. In tournament selection and fitness sharing, base fitness is determined by summing the list. The distance function for fitness sharing is Euclidean distance between the vectors. In Eco-EA and Lexicase selection, each element in the list represents a sub-problem .

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Interaction networks