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Information Sharing and Conflict Resolution in Distributed factored evolutionary algorithms 18

Information Sharing and Conflict Resolution in Distributed F...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Butcher, Stephyn G. W. Sheppard, John W. Strasser, Shane Johns Hopkins Univ Baltimore MD 21218 USA Montana State Univ Bozeman MT 59717 USA

ISBN: (纸本)9781450356183

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.

关键词： multi-population algorithms factored evolutionary algorithms Pareto improvement

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A Study in Overlapping Factor Decomposition for Cooperative Co-Evolution

A Study in Overlapping Factor Decomposition for Cooperative ...

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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Pryor, Elliott Peerlinck, Amy Sheppard, John Montana State Univ Gianforte Sch Comp Bozeman MT 59717 USA

ISBN: (纸本)9781728190488

Large scale global optimization is where we seek to optimize a function with a high number of decision variables. Cooperative co-evolutionary algorithms (CCEA) improve optimization performance on these large scale problems through a divide and conquer approach. How the problem is divided can have a large impact on optimization performance. We provide two new decomposition methods that are capable of generating overlapping groups of variables. We apply a generalized CCEA called factored evolutionary algorithm (FEA) that is capable of optimizing and combining overlapping sub-problems. We compare results to existing methods to analyze the effect of introducing overlap in the sub-problems. We use five functions from the CEC'2010 benchmark suite as a base of comparison for all algorithms. We show that overlap can be beneficial for optimizing problems that are not fully separable.

关键词： cooperative co-evolution particle swarm optimization problem decomposition factored evolutionary algorithms

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factored Particle Swarm Optimization for Policy Co-training in Reinforcement Learning 23

Factored Particle Swarm Optimization for Policy Co-training ...

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Genetic and evolutionary Computation Conference (GECCO)

作者： France, Kordel K. Sheppard, John W. Johns Hopkins Univ Baltimore MD 21218 USA Montana State Univ Bozeman MT 59717 USA

ISBN: (纸本)9798400701191

Uncertainty of the environment limits the circumstances with which any optimization problem can provide meaningful information. Multiple optimizers can combat this problem by communicating *** information through cooperative coevolution. In reinforcement learning (RL), uncertainty can be reduced by applying learned policies collaboratively with another agent. Here, we propose policy Co-training with factored evolutionary algorithms (CoFEA) to evolve an optimal policy for such scenarios. We hypothesize that self-paced co-training can allow factored particle swarms with imperfect knowledge to consolidate knowledge from each of their imperfect policies in order to approximate a single optimal policy. Additionally, we show how the performance of co-training swarms of RL agents can be maximized through the speci.c use of Expected SARSA as the policy learner. We evaluate CoFEA against comparable RL algorithms and attempt to establish limits for which our procedure does and does not provide bene.t. Our results indicate that Particle Swarm Optimization (PSO) is *** in training multiple agents under uncertainty and that FEA reduces swarm and policy updates. This paper contributes to *** of cooperative co-evolutionary algorithms by proposing a method by which factored evolutionary techniques can *** improve how multiple RL agents collaborate under extreme uncertainty to solve complex tasks faster than a single agent can under identical conditions.

关键词： factored evolutionary algorithms co-training reinforcement learning

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