进化计算技术概论

L. Jain, C. L. Karr
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引用次数: 6

摘要

在工业和大学中,人们对进化计算技术的理论和应用的发展有着极大的兴趣。进化计算是基于种群向某个问题的解决方案的进化的算法集合的名称。这些算法成功地应用于许多需要优化某个多维函数的应用中。可能的解决方案从一代发展到下一代,最终达到一个令人满意的解决方案。这些算法的不同之处在于从现有种群生成新种群的方式以及在算法中表示成员的方式。三种类型的进化计算技术最近被广泛报道。它们是遗传算法(GAs)、遗传规划(GP)和进化算法(EAs)。ea可分为进化策略(ES)和进化规划(EP)。这三种算法在某种程度上都是模仿自然界中发生的进化过程。
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Introduction to evolutionary computing techniques
There is a tremendous interest in the development of the theory and applications of evolutionary computing techniques both in industry and universities. Evolutionary computation is the name given to collection of algorithms based on the evolution of a population towards a solution of a certain problem. These algorithms are used successfully in many applications requiring the optimization of a certain multidimensional function. The population of possible solutions evolves from one generation to the next, ultimately arriving at a satisfactory solution to the problem. These algorithms differ in the way a new population is generated from the present one and in the way the members are represented within the algorithm. Three types of evolutionary computing techniques are widely reported recently. These are genetic algorithms (GAs), genetic programming (GP) and evolutionary algorithms (EAs). The EAs can be divided into evolutionary strategies (ES) and evolutionary programming (EP). All three of these algorithms in some way are modelled after the evolutionary processes occurring in nature.<>
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