A computationally efficient evolutionary algorithm for real-parameter optimization
Evolutionary Computation
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Dynamic Crossover and Mutation Genetic Algorithm Based on Expansion Sampling
AICI '09 Proceedings of the International Conference on Artificial Intelligence and Computational Intelligence
A hybrid evolutionary multi-objective and SQP based procedure for constrained optimization
ISICA'07 Proceedings of the 2nd international conference on Advances in computation and intelligence
Information Sciences: an International Journal
Parent to mean-centric self-adaptation in SBX operator for real-parameter optimization
SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part I
Adaptive multi-objective genetic algorithm using multi-pareto-ranking
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Leveraging indicator-based ensemble selection in evolutionary multiobjective optimization algorithms
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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Simulated binary crossover (SBX) is a real-parameter recombinationoperator which is commonly used in the evolutionary algorithm (EA) literature. The operatorinvolves a parameter which dictates the spread of offspring solutionsvis-a-vis that of the parent solutions. In all applications of SBX sofar, researchers have kept a fixed value throughout a simulation run. In this paper, we suggest a self-adaptive procedure of updating theparameter so as to allow a smooth navigation over the functionlandscape with iteration. Some basic principles of classicaloptimization literature are utilized for this purpose. The resultingEAs are found to produce remarkable and much better results comparedto the original operator having a fixed value of the parameter. Studieson both single and multiple objective optimization problems are madewith success.