Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
An adaptive crossover distribution mechanism for genetic algorithms
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
An introduction to genetic algorithms
An introduction to genetic algorithms
Genetic Algorithms Plus Data Structures Equals Evolution Programs
Genetic Algorithms Plus Data Structures Equals Evolution Programs
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms and Fuzzy Logic Systems: Soft Computing Perspectives
Genetic Algorithms and Fuzzy Logic Systems: Soft Computing Perspectives
Proceedings of the 3rd International Conference on Genetic Algorithms
Adapting Operator Probabilities in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Varying the Probability of Mutation in the Genetic Algorithm
Proceedings of the 3rd International Conference on Genetic Algorithms
Uniform Crossover in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Optimal Mutation Rates in Genetic Search
Proceedings of the 5th International Conference on Genetic Algorithms
A New Approach on the Traveling Salesman Problem by Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
Towards an Optimal Mutation Probability for Genetic Algorithms
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
Adaptive Crossover Using Automata
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Cost Based Operator Rate Adaption: An Investigation
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Genetic Algorithms with Adaptive Probabilities of Operators Selection
ICCIMA '99 Proceedings of the 3rd International Conference on Computational Intelligence and Multimedia Applications
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Use of statistical outlier detection method in adaptive evolutionary algorithms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Pre-processing methodology for optimizing stereolithography apparatus build performance
Computers in Industry
Pre-processing methodology for optimizing stereolithography apparatus build performance
Computers in Industry
Quality measures to adapt the participation in MOS
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Learning hybridization strategies in evolutionary algorithms
Intelligent Data Analysis
PC2PSO: personalized e-course composition based on Particle Swarm Optimization
Applied Intelligence
A self-adjusting e-course generation process for personalized learning
Expert Systems with Applications: An International Journal
ACSAC'05 Proceedings of the 10th Asia-Pacific conference on Advances in Computer Systems Architecture
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Traditional genetic algorithms use only one crossover and one mutation operator to generate the next generation. The chosen crossover and mutation operators are critical to the success of genetic algorithms. Different crossover or mutation operators, however, are suitable for different problems, even for different stages of the genetic process in a problem. Determining which crossover and mutation operators should be used is quite difficult and is usually done by trial-and-error. In this paper, a new genetic algorithm, the dynamic genetic algorithm (DGA), is proposed to solve the problem. The dynamic genetic algorithm simultaneously uses more than one crossover and mutation operators to generate the next generation. The crossover and mutation ratios change along with the evaluation results of the respective offspring in the next generation. By this way, we expect that the really good operators will have an increasing effect in the genetic process. Experiments are also made, with results showing the proposed algorithm performs better than the algorithms with a single crossover and a single mutation operator.