期刊名称：Applied Computational Intelligence and Soft Computing
印刷版ISSN：1687-9724
电子版ISSN：1687-9732
出版年度：2010
卷号：2010
DOI：10.1155/2010/696345
出版社：Hindawi Publishing Corporation
摘要：Evolutionary algorithms face a fundamental trade-off between exploration and exploitation. Rapid performance improvement tends to be accompanied by a rapid loss of diversity from the population of potential solutions, causing premature convergence on local rather than global optima. However, the rate at which diversity is lost from a population is not simply a function of the strength of selection but also its efficiency, or rate of performance improvement relative to loss of variation. Selection efficiency can be quantified as the linear correlation between objective performance and reproduction. Commonly used selection algorithms contain several sources of inefficiency, some of which are easily avoided and others of which are not. Selection algorithms based on continuously varying generation time instead of discretely varying number of offspring can approach the theoretical limit on the efficient use of population diversity.
