RANDOM BUILDING BLOCK OPERATOR FOR GENETIC ALGORITHMS

摘要：

Genetic algorithms work on randomly generated populations, which are refined toward the desired optima. The refinement process is carried out mainly by genetic operators. Most typical genetic operators are crossover and mutation. However, experience has proved that these operators in their classical form are not capable of refining the population efficiently enough. Moreover, due to lack of sufficient variation in the population, the genetic algorithm might stagnate at local optimum points. In this work a new dynamic mutation operator with variable mutation rate is proposed. This operator does not require any pre-fixed parameter. It dynamically takes into account the size (number of bits) of the individual during runtime and replaces a randomly selected section of the individual by a randomly generated bit string of the same size. All the bits of the randomly generated string are not necessarily different from bits of the selected section from the individual. Experimentation with 17 test functions, 34 test cases and 1020 test runs proved the superiority of the proposed dynamic mutation operator over single-point mutation operator with 1％, 5％ and 8％ mutation rates and the multipoint mutation operator with 5％, 8％ and 15％ mutation rates.

关键词： Evolutionary algorithm Genetic algorithm Function optimization Mutation operator Multipoint mutationoperator Random building block operator Fitness evaluation and analysis

作者: Ghodrat Moghadampour

作者单位: VAMK, University of Applied Sciences, Technology and Communication, Wolffintie 30, 65200, Vaasa, Finland

会议类型: 国际会议

会议名称: 13th International Conference on Enterprise Information System(第13届企业信息系统国际会议 ICEIS 2011)

会议地点: 北京

会议语种:英文

页码: 2523-2531

在线出版日期: 2011-06-08（万方平台首次上网日期，不代表论文的发表时间）