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
Optimization of Airport Flight Arrival and Departure Based on Compromise Immune Algorithm
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 03
Adaptive genetic algorithm with mutation and crossover matrices
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Multi-armed bandit algorithms and empirical evaluation
ECML'05 Proceedings of the 16th European conference on Machine Learning
Adaptive genetic algorithm and quasi-parallel genetic algorithm: application to knapsack problem
LSSC'05 Proceedings of the 5th international conference on Large-Scale Scientific Computing
EvoApplications'12 Proceedings of the 2012t European conference on Applications of Evolutionary Computation
Topological effects on the performance of island model of parallel genetic algorithm
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advences in computational intelligence - Volume Part II
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A new type of adaptive evolutionary algorithm that combines two genetic algorithms using mutation matrix is developed based on an adaptive resource allocation of CPU time. Performance evaluations are made on the airport scheduling problem with constraint. The two genetic algorithms used are based on the construction of the mutation matrix M(t), which is problem independent as it uses the fitness distribution in the population and the statistical information of the locus only. The mutation matrix is parameter free and adaptive since the matrix elements are time dependent and inherits the information accumulated from past generations. A self-adaptive time sharing method is introduced to allocate resource to the two different strategies, which uses the theory of mean-variance analysis in portfolio management. The application to airport scheduling demonstrates that the self-adaptive mutation only genetic algorithm is able to provide quality solutions efficiently.