ASPARAGOS an asynchronous parallel genetic optimization strategy
Proceedings of the third international conference on Genetic algorithms
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Handbook of Evolutionary Computation
Handbook of Evolutionary Computation
Proceedings of the 5th International Conference on Genetic Algorithms
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Genetic algorithms (GAs), as general search models, have proved its success in several applications, however recently, several researchers have argued that they have slow convergence; this slowness is due to the randomness in all their operations. Therefore, recent researches have employed structured populations, in order to eliminate randomness, such as island models, cellular model, multinational evolutionary algorithms, etc. In this paper, a social based GA is introduced; this model is trying to mimic the actual social behaviour and the actual death and birth process. The authors will restrict the recombination for males to the only permitted females. The authors' motivation to such an approach is that they expect the nature to be more robust and optimal; hence, the objectives of this work are to study the effects of these social rules and customs on the standard GA and to investigate its effects on the speed of convergence of GA. The primary results show a faster convergence than a standard GA and a controlled diffusion of individuals and hence better spanning of the search space.