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
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
An introduction to genetic algorithms
An introduction to genetic algorithms
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
Adaptive operator probabilities in a genetic algorithm that applies three operators
SAC '97 Proceedings of the 1997 ACM symposium on Applied computing
Adapting Operator Probabilities in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
The behavior of adaptive systems which employ genetic and correlation algorithms
The behavior of adaptive systems which employ genetic and correlation algorithms
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
A survey and taxonomy of performance improvement of canonical genetic programming
Knowledge and Information Systems
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Evolutionary Computation (EC) introduces a new paradigm for solving problems in Artificial Intelligence, representing solution candidates as individuals and evolving them based on Darwin's Theory of Natural Selection. Genetic Algorithms (GA) and Genetic Programming (GP), two important EC techniques, have been successfully applied both in theoretical scenarios and practical situations. This work discusses an issue of great relevance and impact on this type of algorithm: the automatic adjustment of the parameters that control the search process. Based on a recent research, a method that controls the population size in a GA is adapted and implemented in GP. A series of classic experiments has been performed before and after the modifications, showing that this method can improve the algorithms' robustness and reliability. The data allow a discussion about the method and the importance of the adaptation of parameters in EC algorithms.