A Species Conserving Genetic Algorithm for Multimodal Function Optimization
提出一种基于物种保护的新技术，通过将种群按相似性分为多个物种并保留物种种子，有效解决多模态优化问题，在多个测试问题中表现良好。
This paper introduces a new technique called species conservation for evolving paral-lel subpopulations. The technique is based on the concept of dividing the population into several species according to their similarity. Each of these species is built around a dominating individual called the species seed. Species seeds found in the current gen-eration are saved (conserved) by moving them into the next generation. Our technique has proved to be very effective in finding multiple solutions of multimodal optimiza-tion problems. We demonstrate this by applying it to a set of test problems, including some problems known to be deceptive to genetic algorithms.