Reducing bias and inefficiency in the selection algorithm
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
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
Gradual distributed real-coded genetic algorithms
IEEE Transactions on Evolutionary Computation
Real-coded memetic algorithms with crossover hill-climbing
Evolutionary Computation - Special issue on magnetic algorithms
Comprehensive Survey of the Hybrid Evolutionary Algorithms
International Journal of Applied Evolutionary Computation
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In this paper, we propose a technique for the application of the crossover operator that generates multiple descendants from two parents and selects the two best offspring to replace the parents in the new population. In order to study the proposal, we present different instances based on the BLX-驴 crossover operator for real-coded genetic algorithms. In particular, we investigate the influence of the number of generated descendants in this operator, the number of evaluations, and the value for the parameter 驴. Analyzing the experimentation that we have carried out, we can observe that it is possible, with multiple descendants, to achieve a suitable balance between the explorative properties associated with BLX-驴 and the high selective pressure associated to the selection of the two best descendants.