Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
Parallel genetic algorithms, population genetics and combinatorial optimization
Proceedings of the third international conference on Genetic algorithms
Using genetic algorithms to learn disjunctive rules from examples
Proceedings of the seventh international conference (1990) on Machine learning
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic algorithm for feature selection for parallel classifiers
Information Processing Letters
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Tabu Search
Designing Neural Networks using Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Towards the Genetic Synthesisof Neural Networks
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
Dynamic Control of Genetic Algorithms Using Fuzzy Logic Techniques
Proceedings of the 5th International Conference on Genetic Algorithms
A Genetic Algorithm Applied to the Maximum Flow Problem
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
Parallel Genetic Algorithms in Optimization
Physik und Informatik - Informatik und Physik, Arbeitsgespräch
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
An adaptive pursuit strategy for allocating operator probabilities
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Combining gradient techniques for numerical multi-objective evolutionary optimization
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
Discovering haplotypes in linkage disequilibrium mapping with an adaptive genetic algorithm
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
Learning hybridization strategies in evolutionary algorithms
Intelligent Data Analysis
Metaheuristics based de novo protein sequencing: A new approach
Applied Soft Computing
Using datamining techniques to help metaheuristics: a short survey
HM'06 Proceedings of the Third international conference on Hybrid Metaheuristics
Path relinking in pareto multi-objective genetic algorithms
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
A genetic programming approach for solving the linear ordering problem
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
Entropy-based adaptive range parameter control for evolutionary algorithms
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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The mutation operation is critical to the success of genetic algorithms since it diversifies the search directions and avoids convergence to local optima. The earliest genetic algorithms use only one mutation operator in producing the next generation. Each problem, even each stage of the genetic process in a single problem, may require appropriately different mutation operators for best results. Determining which mutation operators should be used is quite difficult and is usually learned through experience or by trial-and-error. This paper proposes a new genetic algorithm, the dynamic mutation genetic algorithm, to resolve these difficulties. The dynamic mutation genetic algorithm simultaneously uses several mutation operators in producing the next generation. The mutation ratio of each operator changes according to evaluation results from the respective offspring it produces. Thus, the appropriate mutation operators can be expected to have increasingly greater effects on the genetic process. Experiments are reported that show the proposed algorithm performs better than most genetic algorithms with single mutation operators.