Abstract
This paper introduces a new asynchronous parallel evolutionary algorithm (APEA) based on the island model for solving function optimization problems. Our fully distributed APEA overlaps the communication and computation efficiently and is inherently fault-tolerant in a large-scale distributed computing environment. For the scalable BUMP problem, our APEA algorithm achieves the best solution for the 50-dimension problem, and is the first algorithm of which we are aware that can solve the 1,000,000- dimension problem. For other benchmark problems, our APEA finds the best solution to G7 in fewer time steps than [16][17], and finds a better solution to G10 than [17].
This work was partially supported by National Science Foundation (NFS) Instrumentation Grant EIA9911099.
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Liu, P., Lau, F., Lewis, M.J., Wang, Cl. (2002). A New Asynchronous Parallel Evolutionary Algorithm for Function Optimization. In: Guervós, J.J.M., Adamidis, P., Beyer, HG., Schwefel, HP., Fernández-Villacañas, JL. (eds) Parallel Problem Solving from Nature — PPSN VII. PPSN 2002. Lecture Notes in Computer Science, vol 2439. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45712-7_39
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