Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
A connectionist machine for genetic hillclimbing
A connectionist machine for genetic hillclimbing
A collection of test problems for constrained global optimization algorithms
A collection of test problems for constrained global optimization algorithms
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Test Examples for Nonlinear Programming Codes
Test Examples for Nonlinear Programming Codes
Adapting Operator Probabilities in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
Self-adaptive Operator Scheduling Using the Religion-Based EA
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Self-Adaptive Genetic Algorithm for Numeric Functions
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Predictive models for the breeder genetic algorithm i. continuous parameter optimization
Evolutionary Computation
Adapting operator settings in genetic algorithms
Evolutionary Computation
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Entropy-based adaptive range parameter control for evolutionary algorithms
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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When applying evolutionary algorithms to optimization problems many different strategy parameters have to be set to define the behavior of the evolutionary algorithm itself. To a certain extent these strategy parameter values determine whether the algorithm is capable of finding a near-optimum solution or not. In particular the choice of the different genetic operators and their relative rates is most often based on experience. Furthermore, the operator rates are defined before starting the optimization runs and remain unchanged until the stopping criterion is reached. Controlling the parameter values during the run has the potential of adjusting the algorithm to the problem while solving the problem. This paper investigates an adaptive strategy controlling the rates of arbitrary chosen genetic operators. The control mechanism is based on the state of the optimization by evaluating a success and a diversity measure for each operator. More efficient operators are favored in order to find better solutions with less evaluations. The algorithm is tested with constrained and unconstrained numerical examples and a concrete structural optimization problem is treated.