Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
A computationally efficient evolutionary algorithm for real-parameter optimization
Evolutionary Computation
Hybrid crossover operators for real-coded genetic algorithms: an experimental study
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Self-Adaptive Genetic Algorithms with Simulated Binary Crossover
Evolutionary Computation
A simulated annealing method based on a specialised evolutionary algorithm
Applied Soft Computing
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The neighborhood-based crossover operators used in real coded genetic algorithm (RCGA) are based on some probability distribution. It is observed that each crossover operator directs the search towards a different zone in the neighborhood of the parents. The quality of the elements that belong to the visited region depends on the particular problems to be solved. Different crossover operators perform differently with respect to the problems, even at the different stages of the genetic process in the same problem. In this paper, the role of probability distribution is empirically investigated on unimodal and multi-modal test problems. It is observed that the operator based on polynomial distribution achieves superior performance on unimodal test problems. The lognormal distribution based operator is efficient in solving multi-modal problems.