Information sharing and conflict resolution in distributed factored evolutionary algorithms

Competition and cooperation are powerful metaphors that have informed improvements in multi-population algorithms such as the Cooperative Coevolutionary Genetic Algorithm, Cooperative Particle Swarm Optimization, and Factored Evolutionary Algorithms (FEA). However, we suggest a different perspective can give a finer grained understanding of how multi-population algorithms come together to avoid problems like hitchhiking and pseudo-minima. In this paper, we apply the concepts of information sharing and conflict resolution through Pareto improvements to analyze the distributed version of FEA (DFEA). As a result, we find the original DFEA failed to implement FEA with complete fidelity. We then revise DFEA and examine the differences between it and FEA and the new implications for relaxing consensus in the distributed algorithm.

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