A minimum spanning tree algorithm with inverse-Ackermann type complexity
Journal of the ACM (JACM)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Compaction of Symbolic Layout Using Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
G-Metric: an M-ary quality indicator for the evaluation of non-dominated sets
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Limitations of existing mutation rate heuristics and how a rank GA overcomes them
IEEE Transactions on Evolutionary Computation
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
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Abstract In this paper, a new Multi-Objective Evolutionary Algorithm (MOEA) named RankMOEA is proposed. Innovative niching and ranking-mutation procedures which avoid the need of parameters definition are involved; such procedures outperform traditional diversity-preservation mechanisms under spread-hardness situations. RankMOEA performance is compared with those of other state of the art MOEAs: MOGA, NSGA-II and SPEA2, showing remarkable improvements. RankMOEA is also applied to approximate the Pareto Front of a Dynamic Principal-Agent model with Discrete Actions posed in a Multi-Objective Optimization framework allowing to consider more powerful assumptions than those used in the traditional single-objective optimization approach. Within this new framework a set of feasible contracts is described, while others similar studies only focus on one single contract. The results achieved with RankMOEA show better spread and minor error than those obtained by already mentioned MOEAs, allowing to perform better economic analysis by characterizing contracts in the trade-off surface.