Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
An Investigation of Niche and Species Formation in Genetic Function Optimization
Proceedings of the 3rd International Conference on Genetic Algorithms
Adaptive elitist-population based genetic algorithm for multimodal function optimization
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Niching without niching parameters: particle swarm optimization using a ring topology
IEEE Transactions on Evolutionary Computation
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Niche radius adaptation in the CMA-ES niching algorithm
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Maximizing population diversity in single-objective optimization
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Multimodal optimization using a bi-objective evolutionary algorithm
Evolutionary Computation
Multimodal function optimisation with cuckoo search algorithm
International Journal of Bio-Inspired Computation
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In a multimodal optimization task, the main purpose is to find multiple optimal solutions, so that the user can have a better knowledge about different optimal solutions in the search space and as and when needed, the current solution may be replaced by another optimum solution. Recently, we proposed a novel and successful evolutionary multi-objective approach to multimodal optimization. Our work however made use of three different parameters which had to be set properly for the optimal performance of the proposed algorithm. In this paper, we have eliminated one of the parameters and made the other two selfadaptive. This makes the proposed multimodal optimization procedure devoid of user specified parameters (other than the parameters required for the evolutionary algorithm). We present successful results on a number of different multimodal optimization problems of upto 16 variables to demonstrate the generic applicability of the proposed algorithm.