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This paper presents a cumulative multi-niching genetic algorithm (CMN GA),designed to expedite optimization problems that have computationally-expensivemultimodal objective functions. By never discarding individuals from thepopulation, the CMN GA makes use of the information from every objectivefunction evaluation as it explores the design space. A fitness-relatedpopulation density control over the design space reduces unnecessary objectivefunction evaluations. The algorithm's novel arrangement of genetic operationsprovides fast and robust convergence to multiple local optima. Benchmark testsalongside three other multi-niching algorithms show that the CMN GA has agreater convergence ability and provides an order-of-magnitude reduction in thenumber of objective function evaluations required to achieve a given level ofconvergence.

A Cumulative Multi-Niching Genetic Algorithm for Multimodal Function Optimization