Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Calculating the expected loss of diversity of selection schemes
Evolutionary Computation
Measurement of Population Diversity
Selected Papers from the 5th European Conference on Artificial Evolution
Predictive models for the breeder genetic algorithm i. continuous parameter optimization
Evolutionary Computation
A comparison of selection schemes used in evolutionary algorithms
Evolutionary Computation
A First Study on the Use of Coevolutionary Algorithms for Instance and Feature Selection
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
Editorial: Hybrid intelligent algorithms and applications
Information Sciences: an International Journal
ICAISC'10 Proceedings of the 10th international conference on Artifical intelligence and soft computing: Part II
Comparison of fuzzy functions for low quality data GAP algorithms
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
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In this paper novel selection method based on hybridization of fuzzy logic and previously elaborated mix selection is presented. Proposed method is based on fuzzy control of trade-off between exploration and exploitation properties of evolutionary algorithm. Two factors are considered in proposed selection method: the first is a number of generations of evolutionary algorithm, and second is a population diversity. Due to these two factors, we control the trade-off between global and local search of solution space, and also due to the fuzzy control the proposed method is more "immune" in falling into the trap of local extremum. The results obtained using proposed method are compared with the results obtained using other selection methods like: roulette selection, elitist selection, fan selection, tournament selection, deterministic selection, truncation selection, and mix selection. The comparison is performed using test functions chosen from literature. The results obtained using proposed selection method are better in many cases than results obtained using other selection techniques.