Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
An introduction to genetic algorithms
An introduction to genetic algorithms
Niching methods for genetic algorithms
Niching methods for genetic algorithms
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Efficient differential evolution using speciation for multimodal function optimization
GECCO '05 Proceedings of the 7th 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
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Real world problems present instances where more than one optimal solution can be obtained for a system under consideration so as to switch between them without considerably affecting efficiency. In such instances the idea of niching provides a solution. In this paper we propose a swarm-based niching technique that enhances diversity by Teaching and Learning strategy that adapts to the local neighbourhood by controlled exploitation and the knowledge learned helps to preserve population diversity. Our algorithm, imitates the local-explorative swarm behaviour to hover around local sites in groups, exploiting the peaks with high degree of accuracy, is called TLB-lDS (Teaching-Learning Based Optimization with Local Diversification Strategy), without using any niching parameter. TLB-lDS algorithm is compared against sophisticated niching algorithms tested on a set of standard numerical benchmarks.