Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Combining convergence and diversity in evolutionary multiobjective optimization
Evolutionary Computation
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
A Spatial Predator-Prey Approach to Multi-objective Optimization: A Preliminary Study
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
A Variant of Evolution Strategies for Vector Optimization
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
Multiobjective Optimization Using Adaptive Pareto Archived Evolution Strategy
ISDA '05 Proceedings of the 5th International Conference on Intelligent Systems Design and Applications
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
An EMO algorithm using the hypervolume measure as selection criterion
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
The combative accretion model – multiobjective optimisation without explicit pareto ranking
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
An efficient multi-objective evolutionary algorithm: OMOEA-II
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Reducing the run-time complexity of multiobjective EAs: The NSGA-II and other algorithms
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
On the Effect of the Steady-State Selection Scheme in Multi-Objective Genetic Algorithms
EMO '09 Proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization
Multiobjective evolutionary algorithm with controllable focus on the knees of the Pareto front
IEEE Transactions on Evolutionary Computation
A fast steady-state ε-dominance multi-objective evolutionary algorithm
Computational Optimization and Applications
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The generic Multi-objective Evolutionary Algorithm (MOEA) aims to produce Pareto-front approximations with good convergence and diversity property. To achieve convergence, most multi-objective evolutionary algorithms today employ Pareto-ranking as the main criteria for fitness calculation. The computation of Pareto-rank in a population is time consuming, and arguably the most computationally expensive component in an iteration of the said algorithms. This paper proposes a Multi-objective Evolutionary Algorithm which avoids Pareto-ranking altogether by employing the transitivity of the domination relation. The proposed algorithm is an elitist algorithm with explicit diversity preservation procedure. It applies a measure reflecting the degree of domination between solutions in a steady-state replacement strategy to determine which individuals survive to the next iteration. Results on nine standard test functions demonstrated that the algorithm performs favorably compared to the popular NSGA-II in terms of convergence as well as diversity of the Pareto-set approximation, and is computationally more efficient.