| タイトル | Comparing a Coevolutionary Genetic Algorithm for Multiobjective Optimization |
| 本文（外部サイト） | http://hdl.handle.net/2060/20030015725 |
| 著者（英） | Haith, Gary L.; Kraus, William F.; Clancy, Daniel; Lohn, Jason D. |
| 著者所属（英） | NASA Ames Research Center |
| 発行日 | 2002-01-01 |
| 言語 | eng |
| 内容記述 | We present results from a study comparing a recently developed coevolutionary genetic algorithm (CGA) against a set of evolutionary algorithms using a suite of multiobjective optimization benchmarks. The CGA embodies competitive coevolution and employs a simple, straightforward target population representation and fitness calculation based on developmental theory of learning. Because of these properties, setting up the additional population is trivial making implementation no more difficult than using a standard GA. Empirical results using a suite of two-objective test functions indicate that this CGA performs well at finding solutions on convex, nonconvex, discrete, and deceptive Pareto-optimal fronts, while giving respectable results on a nonuniform optimization. On a multimodal Pareto front, the CGA finds a solution that dominates solutions produced by eight other algorithms, yet the CGA has poor coverage across the Pareto front. |
| NASA分類 | Computer Programming and Software |
| 権利 | Copyright, Distribution as joint owner in the copyright |
| URI | https://repository.exst.jaxa.jp/dspace/handle/a-is/223679 |
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