Fuzzy connectives based crossover operators to model genetic algorithms population diversity
Fuzzy Sets and Systems
Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis
Artificial Intelligence Review
Fuzzy Recombination for the Breeder Genetic Algorithm
Proceedings of the 6th International Conference on Genetic Algorithms
Predictive models for the breeder genetic algorithm i. continuous parameter optimization
Evolutionary Computation
Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization
Evolutionary Computation
Evolutionary algorithms for constrained parameter optimization problems
Evolutionary Computation
Gradual distributed real-coded genetic algorithms
IEEE Transactions on Evolutionary Computation
Improving crossover operator for real-coded genetic algorithms using virtual parents
Journal of Heuristics
Genetic search of block-based structures of dynamical process models
IWANN'03 Proceedings of the Artificial and natural neural networks 7th international conference on Computational methods in neural modeling - Volume 1
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Most real-world optimization problems consist of linear cost functions subject to a set of constraints. In genetic algorithms the techniques for coping with such constraints are manifold: penalty functions, keeping the population in the feasible region, etc. Mutation and crossover operators must take into account the specific features of this kind of problems, as they are the responsible of the generation of new individuals. In this work, we make an analysis of the influence of the selection of the crossover operator in the problem of function optimization with constraints. We focus our work on the crossover operator because this operator is the most characteristic of genetic algorithms. We have used a test set that includes functions with linear and non-linear constraints. The results confirm the importance of crossover operator, as great differences are observed in the performance of the studied operators. The crossover based on confidence intervals shows the most robust behavior.