Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
A species conserving genetic algorithm for multimodal function optimization
Evolutionary Computation
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Fitness-based neighbor selection for multimodal function optimization
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
A sequential niche technique for multimodal function optimization
Evolutionary Computation
Fitness sharing and niching methods revisited
IEEE Transactions on Evolutionary Computation
Parameter adjustment for genetic algorithm for two-level hierarchical covering location problem
WSEAS Transactions on Computers
Real time trajectory based hand gesture recognition
WSEAS Transactions on Information Science and Applications
Searching minimal fractional graph factors by lattice based evolution
WSEAS Transactions on Information Science and Applications
Peak and valley detection in multimodal functions by means of 3D normal metadata
Proceedings of the South African Institute for Computer Scientists and Information Technologists Conference
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A Detecting Peak's Number (DPN) technique is proposed for multimodal optimization. In DPN technique, we want to know the peak's number of locally multimodal domain of every individual, firstly we use the idea of orthogonal intersection for getting the exploration direction in every locally multimodal domain, and then we attempt to detect peak's number in every one-dimension direction as the result of detecting of locally multimodal domain. At last we design an evolution algorithm (DPNA) based on the characters of DNP technique, which contain four characters: niching, variable population, variable radius and life time, and then give a series of experiment results which show the effectiveness of algorithm, as the DPNA is not only adapting to obtaining multiple optima or suboptima, but also effective for problem of ill-scaled and locally multimodal domain described in [11].