Using supportive coevolution to evolve self-configuring crossover
Using supportive coevolution to evolve self-configuring crossover
复制标题
使用支持性协同进化来进化自配置交叉
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
D. Tauritz
中科院分区：
文献类型：
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作者：
Nathaniel R. Kamrath;B. Goldman;D. Tauritz
创建一个进化算法（EA），这是能够自动配置自己和动态控制其参数是一个具有挑战性的问题。然而，解决这个问题可以减少实现EA所需的手动配置量，允许EA更具适应性，并在一系列问题上产生更好的结果，而不需要特定于问题的调优。利用支持性协同进化（SuCo）方法进化自配置交叉算子（SCX），将SuCo的多种群自动配置技术与SCX的动态交叉算子生成和进化相结合。本文报告了几种不同的变异和交叉技术，包括SuCo和SCX的组合的实证比较和分析。出于测试目的，选择Rosenbrock、Rastrigin和Offset Rastrigin基准问题。最后讨论了SCX自适应和进化的优缺点。突变步长和SCX算子的SuCo产生的结果至少与以前的工作一样好，并且一些实验产生的结果明显更好。
Creating an Evolutionary Algorithm (EA) which is capable of automatically configuring itself and dynamically controlling its parameters is a challenging problem. However, solving this problem can reduce the amount of manual configuration required to implement an EA, allow the EA to be more adaptable, and produce better results on a range of problems without requiring problem specific tuning. Using Supportive Coevolution (SuCo) to evolve Self-Configuring Crossover (SCX) combines the automatic configuration technique of multiple populations from SuCo with the dynamic crossover operator creation and evolution of SCX. This paper reports an empirical comparison and analysis of several different combinations of mutation and crossover techniques including SuCo and SCX. The Rosenbrock, Rastrigin, and Offset Rastrigin benchmark problems were selected for testing purposes. The benefits and drawbacks of self-adaptation and evolution of SCX are also discussed. SuCo of mutation step sizes and SCX operators produced results that were at least as good as previous work, and some experiments produced results that were significantly better.