文章基本信息

标题：Hybrid PSO and GA Models for Document Clustering
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作者：K. Premalatha ; A.M. Natarajan
期刊名称：International Journal of Advances in Soft Computing and Its Applications
印刷版ISSN：2074-8523
出版年度：2010
卷号：2
期号：3
出版社：International Center for Scientific Research and Studies
摘要：A Simple Genetic Algorithm (SGA) is a computational abstraction of biological evolution that can be used to solve some optimization problems (Goldberg 1989, Holland 1975). The Genetic Algorithm (GA), proposed by Holland (1975), is a probabilistic optimal algorithm that is based on the evolutionary theories. This algorithm is population-oriented. Successive populations of feasible solutions are generated in a stochastic manner following laws similar to that of natural selection. PSO is a population-based search algorithm and is initialized with a population of random solutions, called particles (Kennedy and Eberhart 1997). Unlike in the other Evolutionary Computation techniques, each particle in PSO is also associated with a velocity. Particles fly through the search space with velocities which are dynamically adjusted according to their historical behaviours. Therefore, the particles have the tendency to fly towards the better search area over the course of search process