| 书目名称 | Foundations of Global Genetic Optimization |
| 编辑 | Robert Schaefer |
| 视频video | |
| 概述 | Presents the foundations of global genetic optimization.Includes supplementary material: |
| 丛书名称 | Studies in Computational Intelligence |
| 图书封面 |  |
| 描述 | Genetic algorithms today constitute a family of e?ective global optimization methods used to solve di?cult real-life problems which arise in science and technology. Despite their computational complexity, they have the ability to explore huge data sets and allow us to study exceptionally problematic cases in which the objective functions are irregular and multimodal, and where information about the extrema location is unobtainable in other ways. Theybelongtotheclassofiterativestochasticoptimizationstrategiesthat, during each step, produce and evaluate the set of admissible points from the search domain, called the random sample or population. As opposed to the Monte Carlo strategies, in which the population is sampled according to the uniform probability distribution over the search domain, genetic algorithms modify the probability distribution at each step. Mechanisms which adopt sampling probability distribution are transposed from biology. They are based mainly on genetic code mutation and crossover, as well as on selection among living individuals. Such mechanisms have been testedbysolvingmultimodalproblemsinnature,whichiscon?rmedinpart- ular by the many species of animals and |
| 出版日期 | Book 2007 |
| 关键词 | Artificial Genetic Systems; Clustered Genetic Search; adaptation; algorithm; algorithms; behavior; genetic |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-540-73192-4 |
| isbn_softcover | 978-3-642-09225-1 |
| isbn_ebook | 978-3-540-73192-4Series ISSN 1860-949X Series E-ISSN 1860-9503 |
| issn_series | 1860-949X |
| copyright | Springer-Verlag Berlin Heidelberg 2007 |
发表于 2025-3-21 17:59:17
