Selected population characteristics of fine-grained parallel genetic algorithms with re-initialization
Ivan Sekaj, Michal Oravec · 2009
A class of fine-grained parallel genetic algorithms (F-PGA) are analyzed and experimentally compared. Each node of the F-PGA represents a single individual. Selected topologies are proposed, which are using various parent selection and offspring selection methods. Also the influence of population re-initialization on the parallel genetic algorithm performance is analyzed and selected characteristics of evolutionary algorithm population are proposed. These characteristics represent such properties as relative number of modified genes and number of duplicate individuals in population. The results are demonstrated on examples with minimization of selected test functions.