A Meta-Parallel Evolutionary System for Solving Optimization Problems

W.R. Britt and G.V. Dozier (USA)


parallel systems, meta-algorithms, heterogeneity


The purpose of the Meta-Parallel Evolutionary System (MPES) is to develop fast, efficient parallel evolutionary systems for function optimization. Given an optimization problem and a set number of nodes available for the com putation, the MPES searches for a strong, potentially het erogeneous combination of evolutionary algorithms to co ordinate in order to effectively solve a problem. The Evo lutionary Algorithms that are utilized in the parallel system are a Particle Swarm Optimizer (PSO), a variety of Ge netic Algorithms (GAs), and an Evolutionary Hill-Climber Algorithm (EHC). The subpopulations communicate with each other via one or more centralized buffers. At a higher level exists the MPES, which uses evolutionary methods in order to discover parameters for effective parallel sys tems. This methodology provides an immediate benefit in the form of a strong tool to solve the optimization problem. Further, it provides a long-term benefit by identifying a sys tem that has the potential to effectively solve other difficult optimization problems with a similar search space.

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