Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
A species conserving genetic algorithm for multimodal function optimization
Evolutionary Computation
Finding Multimodal Solutions Using Restricted Tournament Selection
Proceedings of the 6th International Conference on 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.
Population climbing evolutionary algorithm for multimodal function global optimization
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
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This paper presents a two-phase steady-state evolutionary algorithm (TSEA) for solving function optimization containing multiple global optima. The algorithm includes two phases: firstly,steady-state evolution algorithm is used to get sub-optimal solutions in the global search,it enables individual to draw closer to each optimal solution,thus population is divided into subpopulations automatically after the global search.Secondly,local search is carried in the neighborhood of the best individual of each subpopulation to obtain precise solutions. Comparing with other algorithms, it has the following advantages. (1) It designs a new multi-parent crossover operator with strong direction which can accelerate the convergence.(2) A novel replacement strategy is proposed to maintain the diversity of population.This strategy is very simple and effective with little computational cost.(3) Proposed algorithm needs no additional control parameter which depends on a special problem.The experiment results show that TSEA is very efficient for the optimization of multi-modal functions.