Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
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IEA/AIE '94 Proceedings of the 7th international conference on Industrial and engineering applications of artificial intelligence and expert systems
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
An Introduction to Genetic Algorithms
An Introduction to Genetic Algorithms
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Information Sciences—Informatics and Computer Science: An International Journal
A method for parameter calibration and relevance estimation in evolutionary algorithms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
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IEEE Transactions on Evolutionary Computation
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Applying the triple parameter hypothesis to maintenance scheduling
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Is the triple parameter hypothesis generalizable
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
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When an optimization problem is encoded using genetic algorithms, one must address issues of population size, crossover and mutation operators and probabilities, stopping criteria, selection operator and pressure, and fitness function to be used in order to solve the problem. This paper tests a relationship between (1) crossover probability, (2) mutation probability, and (3) selection pressure using two problems. This relationship is based on the schema theorem proposed by Holland and reflects the fact that the choice of parameters and operators for genetic algorithms needs to be problem specific.