Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Dr. Dobb's Journal
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic algorithms as function optimizers
Genetic algorithms as function optimizers
Diversity and multimodal search with a hybrid two-population GA: an application to ANN development
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
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Genetic Algorithms are non-deterministic, stochastic-search adaptive methods which use the theories of natural evolution and selection in order to solve a problem within a complex range of possible solutions. The aim is to control the distribution of the search space by incorporating an exhaustive method in order to maintain a constant evolution of the population. The main goal is that of redesigning the algorithm in order to add to the classic genetic algorithm method those characteristics which favour exhaustive search methods. The method explained guarantees the achievement of reasonably satisfactory solutions in short time-spans and in a deterministic way, which entails that successive repetitions of the algorithm will achieve the same solutions in almost constant time-spans. We are, therefore, dealing with an evolutionary technique which makes the most of the characteristics of genetic algorithms and exhaustive methods.