Paper 25 Details


The Effects of Environmental Structure on the Evolution of Modularity in a Pattern Classifier


Jessica Lowell, Jordan Pollack


Date: Thursday 7 Sept
Talk Time: TBA
Session: Applications of artificial life 14:00


modularity, hierarchy, evolutionary dynamics, neuroevolution, neural networks


We examine hierarchical modularity – modularity on multiple levels, in which the modules at a lower level of abstraction can serve as nodes in a network at a higher level of abstraction that also has positive modularity – as well as degree of modularity on a single level of abstraction, in evolved neural networks in single-task, parallel-subtask environments, and sequential-subtask environments, using a common benchmark problem. We determine that top-performing networks evolved in the sequential-subtask environment have both more levels of hierarchical modularity, and a higher degree of modularity within levels, than those involved in either the single-task or parallel-subtask environment. In the single-task environment, both single-level and hierarchical modularity tend to rise initially before stagnating and even declining, while in the sequential-subtask environment, both single-level and hierarchical modularity tend to rise throughout the period of evolution.

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