A connectionist machine for genetic hillclimbing
A connectionist machine for genetic hillclimbing
Reducing bias and inefficiency in the selection algorithm
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Genetic algorithm with the dynamic probability of mutation in the classification problem
Pattern Recognition Letters
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Evolutionary Computation: Towards a New Philosophy of Machine Intelligence
Evolutionary Computation: Towards a New Philosophy of Machine Intelligence
Distributed Genetic Algorithms
Proceedings of the 3rd 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.
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This paper proposes an elite crossover strategy together with a dynastic change strategy for genetic algorithms. These strategies are applied to the elites, with a different crossover operation applied to the general population. This multi-crossover operation approach is different from the traditional genetic algorithms where the same crossover strategy is used on both elites and general population. The advantage of adopting a multi-crossover operation approach is faster convergence. Additionally, by adopting a dynastic change strategy in the elite crossover operation, the problem of premature convergence does not need to be actively corrected. The inspiration for the dynastic change strategy comes from ancient Chinese history where royal members of a dynasty undertake intermarriages with other royal members in order to enhance their ascendancy. The central thesis of our elite crossover strategy is that a dynasty can never be sustained forever in a society that changes continuously with its environment. A set of 8 benchmark functions is selected to investigate the effectiveness and efficiency of the proposed genetic algorithm.