Comparing a coevolutionary genetic algorithm for multiobjective optimization
Comparing a coevolutionary genetic algorithm for multiobjective optimization
复制标题
DOI：
10.1109/cec.2002.1004406
复制
发表时间：
2002-05
期刊：
影响因子：
--
通讯作者：
J. Lohn;W. Kraus;G. Haith
中科院分区：
文献类型：
--
作者：
J. Lohn;W. Kraus;G. Haith
我们展示了一项研究的结果，该研究使用一套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比较。 CGA 体现了竞争性协同进化，并采用基于发展学习理论的简单、直接的目标群体表示和适应度计算。由于这些特性，设置额外的群体很简单，实现起来并不比使用标准 GA 更困难。使用一套双目标测试函数的经验结果表明，该 CGA 在寻找凸、非凸、离散和欺骗性帕累托最优前沿上的解决方案方面表现良好，同时在非均匀优化上给出了可观的结果。在多模态 Pareto 前沿上，CGA 在整个 Pareto 前沿上的覆盖率很差，但找到了一个优于其他八种算法产生的所有解决方案的解决方案。
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 yields poor coverage across the Pareto front, yet finds a solution that dominates all the solutions produced by the eight other algorithms.