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首页> 外文会议>Natural Computation (ICNC), 2012 Eighth International Conference on >Genetic algorithm with geographic speciation
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Genetic algorithm with geographic speciation

机译：具有地理物种形成的遗传算法

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摘要

Genetic algorithm (GA) is a bionic algorithm which is widely used for optimization problems. It was initially coined by professor Holland in University of Michigan who took advantage of some phenomenon in natural evolution, such as crossover, mutation, selection and inheritance. However, there is a longstanding problem of genetic algorithm. During the process of optimization, prematurity may occur. It means that local optimum, instead of the global one, is found. But when avoiding prematurity, another problem (i.e. converging slowly) will be triggered. This paper is aiming to propose a modified genetic algorithm simulating the geographic speciation (GS) which has an important role in species' evolution. GS redirect species' evolution by dividing a species into different parts and letting each one evolve in their particular area into a new species. In the meanwhile, GS keep accessing those species. This process is to evaluate whether a specific species is likely to evolve into a superior one. Some new species will be further divided when some conditions are satisfied. Otherwise, these species will no longer be studied. The underlying idea of the genetic algorithm with GS is dropping the domains that global optimal is not exist and investigating the domains that a believed global optimal, may be local, exist at a more precise level.
机译：遗传算法（GA）是一种仿生算法，广泛用于优化问题。它最初是由密歇根大学的荷兰教授创造的，他利用了自然进化中的某些现象，例如交叉，变异，选择和遗传。但是，遗传算法存在一个长期存在的问题。在优化过程中，可能会发生过早。这意味着找到了局部最优而不是全局最优。但是当避免过早出现时，将触发另一个问题（即收敛缓慢）。本文旨在提出一种改进的遗传算法，用于模拟在物种进化中起重要作用的地理物种形成（GS）。 GS通过将一个物种划分为不同的部分并使每个物种在其特定区域内演化为一个新物种来重定向物种的进化。同时，GS继续访问这些物种。这个过程是为了评估特定物种是否有可能进化为上等物种。当满足某些条件时，将进一步划分一些新物种。否则，将不再研究这些物种。具有GS的遗传算法的基本思想是放弃不存在全局最优的域，并研究更精确的水平上认为全局最优的可能是局部的域。

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