Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
A computationally efficient evolutionary algorithm for real-parameter optimization
Evolutionary Computation
Multi-parent Recombination in Genetic Algorithms with Search Space Boundary Extension by Mirroring
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Why Quality Assessment Of Multiobjective Optimizers Is Difficult
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Multicriteria Optimization
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Multiobjective Optimization: Interactive and Evolutionary Approaches
Multiobjective Optimization: Interactive and Evolutionary Approaches
Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II
IEEE Transactions on Evolutionary Computation
The performance of a new version of MOEA/D on CEC09 unconstrained MOP test instances
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Properties of an adaptive archiving algorithm for storing nondominated vectors
IEEE Transactions on Evolutionary Computation
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
A Multiobjective Optimization-Based Evolutionary Algorithm for Constrained Optimization
IEEE Transactions on Evolutionary Computation
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
IEEE Transactions on Evolutionary Computation
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Different crossover operators suit different problems. It is, therefore, potentially problematic to chose the ideal crossover operator in an evolutionary optimization scheme. Using multiple crossover operators could be an effective way to address this issue. This paper reports on the implementation of this idea, i.e. the use of two crossover operators in a decomposition-based multi-objective evolutionary algorithm, but not simultaneously. After each cycle, the operator which has helped produce the better offspring is rewarded. This means that the overall algorithm uses a dynamic resource allocation to reward the better of the crossover operators in the optimization process. The operators used are the Simplex Crossover operator (SPX) and the Center of Mass Crossover operator (CMX). We report experimental results that show that this innovative use of two crossover operators improves the algorithm performance on standard test problems. Results on the sensitivity of the suggested algorithm to key parameters such as population size, neighborhood size and maximum number of solutions to be altered for a given subproblem in the the decomposition process are also included.