Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Uniform Crossover in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Adaptive crossover and mutation in genetic algorithms based on clustering technique
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Hybrid Fuzzy-Genetic Algorithm Approach for Crew Grouping
ISDA '05 Proceedings of the 5th International Conference on Intelligent Systems Design and Applications
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
An orthogonal genetic algorithm with quantization for globalnumerical optimization
IEEE Transactions on Evolutionary Computation
Evolutionary programming using mutations based on the Levy probability distribution
IEEE Transactions on Evolutionary Computation
A robust stochastic genetic algorithm (StGA) for global numerical optimization
IEEE Transactions on Evolutionary Computation
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The genetic algorithm (GA) is a population-based optimization technique that can be applied to wide range of problems. It has ability for widely search solutions by mutation operator and nearly searches by cross-over operator. This paper proposes a seesaw method to decide suitable cross-over and mutation rate according to solution searching status. The seesaw method can significantly improve efficiency of offspring generation of the original genetic algorithm. In another word, it can enhance population's searching ability and can avoid populations to fall into local minimal. Experiments were conducted on unimodal and multimodal test functions such as Sphere, Rastrigin, Ackley, Griewanks and Generalized Penalized Function. Our approach performs better performance on solution search ability than other GA approaches.