Utilizing Lamarckian Evolution and the Baldwin Effect in Hybrid Genetic Algorithms
Utilizing Lamarckian Evolution and the Baldwin Effect in Hybrid Genetic Algorithms
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发表时间：
2007
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通讯作者：
C. Houck;M. Kay
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作者：
C. Houck;M. Kay
遗传算法（GA）是非常有效的探索整个搜索空间，但是，他们是相对较差的，在该算法收敛的区域中找到精确的局部最优解。混合遗传算法是遗传算法和改进程序的结合，通常作为评价函数。在混合遗传算法中有两种基本的策略，即拉马克学习和鲍德温学习。传统的图式理论不支持拉马克学习，即，迫使所述遗传表示与所述改进过程找到的解相匹配。然而，拉马克学习确实缓解了多个基因型映射到同一表型的问题。Baldwinian学习使用改进程序来改变适应度，但找到的解决方案不会编码回遗传字符串。本文实证研究了在混合遗传算法中使用拉马克和鲍德温学习的问题。在进行的实证调查中，观察到一个总的趋势，越来越多地使用拉马克学习导致更快的收敛的遗传算法的最佳已知的解决方案的一系列测试问题。
Genetic algorithms(GA) are very efficient at exploring the entire search space; however, they are relatively poor at finding the precise local optimal solution in the region at which the algorithm converges. Hybrid genetic algorithms are the combination of improvement procedures, usually working as evaluation functions, and genetic algorithms. There are two basic strategies in using hybrid GAs, Lamarckian and Baldwinian learning. Traditional schema theory does not support Lamarckian learning, i.e., forcing the genetic representation to match the solution found by the improvement procedure. However, Lamarckian learning does alleviate the problem of multiple genotypes mapping to the same phenotype. Baldwinian learning uses improvement procedures to change the fitness landscape, but the solution that is found is not encoded back into the genetic string. This paper empirically examines the issues of using Lamarckian and Baldwinian learning in hybrid GAs. In the empirical investigation conducted, a general trend was observed where increasing use of Lamarckian learning led to the quicker convergence of the genetic algorithm to the best known solution for a series of test problems.