Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
A Comparison of Parallel and Sequential Niching Methods
Proceedings of the 6th International Conference on Genetic Algorithms
Finding Multimodal Solutions Using Restricted Tournament Selection
Proceedings of the 6th International Conference on Genetic Algorithms
Efficient differential evolution using speciation for multimodal function optimization
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Comparison of multi-modal optimization algorithms based on evolutionary algorithms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Adaptive niche radii and niche shapes approaches for niching with the cma-es
Evolutionary Computation
Niching without niching parameters: particle swarm optimization using a ring topology
IEEE Transactions on Evolutionary Computation
Multimodal optimization by means of a topological species conservation algorithm
IEEE Transactions on Evolutionary Computation
Locating and tracking multiple dynamic optima by a particle swarm model using speciation
IEEE Transactions on Evolutionary Computation
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Genetic algorithms are mainly modeled on basic four steps of parent selection, crossover, offspring evaluation and replacement. It is not possible to model this for direct application to multimodal landscapes. In this paper we propose a novel algorithm in which GA named Parent Centric Normal Crossover is modified and works on a clustered population to tackle multimodal problems. We suggest a dynamic clustering scheme to maintain stable yet variable number of clusters of variable size which can tackle multimodal landscapes, and a Crossover Rate operator in GA for controlled convergence to tackle complex multimodal functions. The algorithm has been tested over widely used benchmarks from single dimension to complex composite functions and compared with other State of the art EAs. The results clearly prove C-SPC-PNX to be a robust multimodal optimization technique.