We present quantitative results using this complexity metricĪnd analyze the causes of varying rates of complexity growthĪcross different types of interactions. These automata, and study the coevolutionary dynamics ofĬomplexity growth in a variety of multi-species simulations. We propose a linguistic prediction game with competitive andĬooperative variants, and a model of game players based onįinite state automata. Proceedings of European Conference on Artificial Life, Lyon France, 298-305. Effects of Cooperative and Competitive Coevolution on Complexity in a Linguistic Prediction Game. While in the sequential-subtask environment, both singlelevelĪnd hierarchical modularity tend to rise throughout the Tend to rise initially before stagnating and even declining,
In the singletaskĮnvironment, both single-level and hierarchical modularity The single-task or parallel-subtask environment. Of modularity within levels, than those involved in either Networks evolved in the sequential-subtask environment haveīoth more levels of hierarchical modularity, and a higher degree Of abstraction that also has positive modularity - as wellĪs degree of modularity on a single level of abstraction, inĮvolved neural networks in single-task, parallel-subtask environments,Īnd sequential-subtask environments, using a commonīenchmark problem. Levels, in which the modules at a lower level of abstractionĬan serve as nodes in a network at a higher level We examine hierarchical modularity - modularity on multiple
Proceedings of European Conference on Artificial Life, Lyon France, 267-274.
The effects of environmental structure on the evolution of modularity in a pattern classifier. Publications DEMO Publications and Technical Reports
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