Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.
Digital Object Identifier (DOI) : 10.14569/IJARAI.2012.010902
Article Published in International Journal of Advanced Research in Artificial Intelligence(IJARAI), Volume 1 Issue 9, 2012.
Abstract: This paper presents a cumulative multi-niching genetic algorithm (CMN GA), designed to expedite optimization problems that have computationally-expensive multimodal objective functions. By never discarding individuals from the population, the CMN GA makes use of the information from every objective function evaluation as it explores the design space. A fitness-related population density control over the design space reduces unnecessary objective function evaluations. The algorithm’s novel arrangement of genetic operations provides fast and robust convergence to multiple local optima. Benchmark tests alongside three other multi-niching algorithms show that the CMN GA has a greater convergence ability and provides an order-of-magnitude reduction in the number of objective function evaluations required to achieve a given level of convergence.
Matthew Hall, “A Cumulative Multi-Niching Genetic Algorithm for Multimodal Function Optimization” International Journal of Advanced Research in Artificial Intelligence(IJARAI), 1(9), 2012. http://dx.doi.org/10.14569/IJARAI.2012.010902