We explored the role of modularity as a means to improve evolvability in populations of adaptive agents. We performed two sets of artificial life experiments. In the first, the adaptive agents were neural networks controlling the behavior of simulated garbage collecting robots, where modularity referred to the networks architectural organization and evolvability to the capacity of the population to adapt to environmental changes measured by the agents performance. In the second, the agents were programs that control the changes in network’s synaptic weights (learning algorithms), the modules were emerged clusters of symbols with a well defined function and evolvability was measured through the level of symbol diversity across programs. We found that the presence of modularity (either imposed by construction or as an emergent property in a favorable environment) is strongly correlated to the presence of very fit agents adapting effectively to environmental changes. In the case of learning algorithms we also observed that character diversity and modularity are also strongly correlated quantities.
Evolvability Emergence of modularity Artificial life simulations Evolutionary robotics
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The financial support of the Italian National Research Council (Short-Term Mobility Program at Yale University to RC) is gratefully acknowledged. The authors thank the referees for valuable suggestions on the manuscript. RC would also like to thank Gunter Wagner, Stefano Nolfi and Freek Duynstee for their contribution to the early stages of this work.
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