协同演化算法研究进展

协同演化算法(coevolutionary algorithms,CEA)是当前国际上计算智能研究的一个热点,它运用生物协同演化的思想,是针对演化算法的不足而兴起的,通过构造两个或多个种群,建立它们之间的竞争或合作关系,多个种群通过相互作用来提高各自性能,适应复杂系统的动态演化环境,以达到种群优化的目的.介绍了协同演化算法的研究状况以及目前的研究进展,概述了它的基本算法、主要特点、理论与技术,同时介绍了一些主要的应用领域,指出了协同演化算法的研究方向.

An Overview of the Research on Coevolutionary Algorithms

Evolutionary algorithms often suffer from premature convergence because of the loss of population diversity at the early stage.Coevolutionary algorithm is a hot research topic in computational intelligence,which aims at improving conventional evolutionary algorithms.Inspired by the principle of natural selection,coevolutionary algorithms are search methods in which processes of mutual adaptation occur amongst agents that interact strategically.The outcomes of interaction reveal a reward structure that guides evolution towards the discovery of increasingly adaptive behaviors.Much of the work on coevolutionary algorithms has focused on two kinds of interaction:competitive coevolutionary systems and cooperative coevolutionary systems.Competitive coevolutionary algorithms are natural models for evolving objects such as game playing programs for which it is difficult to write an external fitness function,but quite simple to define fitness in terms of competitive success against other programs in the evolving population.Cooperative coevolutionary algorithms are natural models for evolving complex objects by decomposing them into subassemblies that coevolve,and subassembly fitness is determined by how well it works with the other subassemblies in producing a complete object.The research state and advances in the coevolutionary algorithms are discussed and surveyed.The implementation techniques and main applications of the coevolutionary algorithms are outlined.Further research directions are indicated.