Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
A computationally efficient evolutionary algorithm for real-parameter optimization
Evolutionary Computation
Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
On the Performance Assessment and Comparison of Stochastic Multiobjective Optimizers
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Multiobjective Evolutionary Algorithms and Applications (Advanced Information and Knowledge Processing)
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Adaptive mutation with fitness and allele distribution correlation for genetic algorithms
Proceedings of the 2006 ACM symposium on Applied computing
Geometric crossovers for real-code representation
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Predictive models for the breeder genetic algorithm i. continuous parameter optimization
Evolutionary Computation
Multicriteria decision making (MCDM): a framework for research and applications
IEEE Computational Intelligence Magazine
A systems approach to evolutionary multiobjective structural optimization and beyond
IEEE Computational Intelligence Magazine
Inbreeding properties of geometric crossover and non-geometric recombinations
FOGA'07 Proceedings of the 9th international conference on Foundations of genetic algorithms
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Recombination of similar parents in EMO algorithms
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Comparison between lamarckian and baldwinian repair on multiobjective 0/1 knapsack problems
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
The balance between proximity and diversity in multiobjective evolutionary algorithms
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
On the Evolutionary Optimization of Many Conflicting Objectives
IEEE Transactions on Evolutionary Computation
A New Evolutionary Algorithm for Solving Many-Objective Optimization Problems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Recombination of similar parents in SMS-EMOA on many-objective 0/1 knapsack problems
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part II
Exploration and exploitation in evolutionary algorithms: A survey
ACM Computing Surveys (CSUR)
A Modified micro Genetic Algorithm for undertaking Multi-Objective Optimization Problems
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Recent Advances in Soft Computing: Theories and Applications
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In the design of evolutionary multiobjective optimization (EMO) algorithms, it is important to strike a balance between diversity and convergence. Traditional mask-based crossover operators for binary strings (e.g., one-point, two-point, and uniform) tend to decrease the spread of solutions along the Pareto front in EMO algorithms while they improve the convergence to part of the Pareto front. This is because such a crossover operator, which is called geometric crossover, always generates an offspring in the segment between its two parents under the Hamming distance in the genotype space. That is, the sum of the distances from the generated offspring to its two parents is always equal to the distance between the two parents. In this paper, we first propose a non-geometric binary crossover operator to generate an offspring outside the segment between its two parents. Next, we show some properties of our crossover operator. Then we examine its effects on the behavior of EMO algorithms through computational experiments on knapsack problems with two, four, and six objectives. Experimental results show that our crossover operator can increase the spread of solutions along the Pareto front in EMO algorithms without severely degrading their convergence property. As a result, our crossover operator improves some overall performance measures such as the hypervolume.