An adaptive crossover distribution mechanism for genetic algorithms
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Computer
Genetic Algorithms and Simulated Annealing
Genetic Algorithms and Simulated Annealing
Genetic algorithms: A 10 Year Perspective
Proceedings of the 1st International Conference on Genetic Algorithms
Adapting Operator Probabilities in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
IWANN'03 Proceedings of the Artificial and natural neural networks 7th international conference on Computational methods in neural modeling - Volume 1
Self-adaptive mutations may lead to premature convergence
IEEE Transactions on Evolutionary Computation
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Self-adaptation in genetic algorithms has been suggested as a strategy to enhance evolutionary algorithms for optimization tasks. We consider continuous self-adaptation schemes called strategies that are governed by evolutionary rules, and suggest to incorporate multiple strategies to improve the performance of genetic algorithms. We show that employing multiple strategies, and letting evolutionary pressure steer adaptation,can overcome the problem of premature convergence. To demonstrate the power of our method we apply it to optimization problems of uncapacitated facility location. The method outperforms both methods with a single strategy and previously reported methods on several benchmarks.In these runs, algorithms that incorporate multiple strategies avoid getting stuck in local optimum, and converge to better solutions.