带反馈的混沌并行GA及其在非线性约束优化中的应用

基于生物系统中普遍存在"随机进化 反馈"现象,提出了带反馈机制的混沌并行遗传算法:混沌映射的嵌入保持演化群体良好的多样性,而反馈机制,即基于Baldwin效应的后天强化学习,克服纯粹随机演化,从而加速系统演化进程.通过基准复杂非线性约束优化问题及金融领域中基准的参数优化问题的数值实验,验证了文中算法的高效性、通用性及稳健性.

For Constrained Non-Linear Programming:Chaotic Parallel Genetic Algorithm with Feedback

Basing on a new scheme-random evolution plus feedback, which is reported to well represent the nature of biological evolution process, this paper proposes chaotic parallel genetic algorithm with feedback mechanism. In this new algorithm, chaotic mapping is embedded for maintaining a good diversity of population; and Baldwin effect based posterior reinforcement -learning, which can successfully deal with the feedback information from the evolutionary system, is integrated to speed up the evolution along the right direction. The performance of this new algorithm was demonstrated on a well-known benchmark constrained non-linear problem and a benchmark problem of parameter estimation in finance. Experimental results and comparisons show that this new genetic algorithm is effective, universal and robust.