2016/04/22 by Aram Ter-Sarkisov, Ter-Sarkisov, Aram, Stephen Marsland +1
Computer Science · #Advanced Multi-Objective Optimization Algorithms #Evolutionary Algorithms and Applications #FOS: Computer and information sciences #I.2 #Metaheuristic Optimization Algorithms Research #Neural and Evolutionary Computing (cs.NE) #cs.NE
paper · pdf · doi:10.48550/arxiv.1604.06607
accepted for publication in Soft Computing
arxiv created 2016/04/22 · openalex publication_date 2016/04/22 · arxiv updated 2016/04/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
There has been a variety of crossover operators proposed for Real-Coded Genetic Algorithms (RCGAs), which recombine values from the same location in pairs of strings. In this article we present a recombination operator for RC- GAs that selects the locations randomly in both parents, and compare it to mainstream crossover operators in a set of experiments on a range of standard multidimensional optimization problems and a clustering problem. We present two variants of the operator, either selecting both bits uniformly at random in the strings, or sampling the second bit from a normal distribution centered at the selected location in the first string. While the operator is biased towards exploitation of fitness space, the random selection of the second bit for swap- ping makes it slightly less exploitation-biased. Extensive statistical analysis using a non-parametric test shows the advantage of the new recombination operators on a range of test functions.