一个求解大欺骗问题的多种群进化多集模型
A multiset model of multi-species evolution to solve big deceptive problems
摘要：
本章介绍了SMuGA，一个集成的共生与多集遗传算法（MuGA）。这里使用的共生的方法是基于主机寄生虫模型的新颖性，沿着进化过程中的寄生虫的长度不同。此外，它还模拟了多个寄生虫和单个宿主之间的协作。为了提高效率，我们引入了寄生虫的代理评估，这节省了适应度函数调用，并以指数方式减少了产生的共生协作。另一个新的特征是将进化周期分为两个阶段：共生阶段和宿主和寄生虫独立进化的阶段。SMuGA进行了测试，在优化各种欺骗性的功能，结果一个数量级优于最先进的共生算法。这允许优化具有大尺寸的欺骗性问题，并显示出迭代次数的线性缩放以达到最优。
英文摘要：
This chapter presents SMuGA, an integration of symbiogenesis with the Multiset Genetic Algorithm (MuGA). The symbiogenetic approach used here is based on the host-parasite model with the novelty of varying the length of parasites along the evolutionary process. Additionally, it models collaborations between multiple parasites and a single host. To improve efficiency, we introduced proxy evaluation of parasites, which saves fitness function calls and exponentially reduces the symbiotic collaborations produced. Another novel feature consists of breaking the evolutionary cycle into two phases: a symbiotic phase and a phase of independent evolution of both hosts and parasites. SMuGA was tested in optimization of a variety of deceptive functions, with results one order of magnitude better than state of the art symbiotic algorithms. This allowed to optimize deceptive problems with large sizes, and showed a linear scaling in the number of iterations to attain the optimum.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
