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Comparing a Coevolutionary Genetic Algorithm for Multiobjective Optimization

机译:比较用于多目标优化的协进化遗传算法

摘要

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.
机译:我们提供了一项研究结果,该研究使用一组多目标优化基准将最近开发的协同进化遗传算法(CGA)与一组进化算法进行了比较。 CGA体现了竞争性的协同进化,并基于学习的发展理论采用简单,直接的目标人群表示和适应度计算。由于具有这些特性,设置额外的人口很容易,因此实施起来并不比使用标准GA困难。使用一组两个目标测试函数的经验结果表明,此CGA在找到凸,非凸,离散和欺骗性帕累托最优前沿上的解时表现出色,同时在非均匀优化中给出了可观的结果。在多模式Pareto前沿,CGA找到了一个解决方案,该解决方案主导了其他八种算法产生的解决方案,但是CGA在Pareto前沿的覆盖率很差。

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