Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis
Artificial Intelligence Review
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Journal of Global Optimization
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Lamarckian Evolution, The Baldwin Effect and Function Optimization
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Jumping Genes-mutators Can Rise Efficacy Of Evolutionary Search
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Particle swarm optimization with preference order ranking for multi-objective optimization
Information Sciences: an International Journal
Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II
IEEE Transactions on Evolutionary Computation
Baldwinian learning in clonal selection algorithm for optimization
Information Sciences: an International Journal
Information Sciences: an International Journal
Achieving balance between proximity and diversity in multi-objective evolutionary algorithm
Information Sciences: an International Journal
Multi-Objective differential evolution with adaptive control of parameters and operators
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
A review of multiobjective test problems and a scalable test problem toolkit
IEEE Transactions on Evolutionary Computation
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
IEEE Transactions on Evolutionary Computation
A Jumping Gene Paradigm for Evolutionary Multiobjective Optimization
IEEE Transactions on Evolutionary Computation
AbYSS: Adapting Scatter Search to Multiobjective Optimization
IEEE Transactions on Evolutionary Computation
Information Sciences: an International Journal
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Exploration and exploitation are two cornerstones of evolutionary multiobjective optimization. Most of the existing works pay more attention to the exploitation, which mainly focuses on the fitness assignment and environmental selection. However, the exploration, usually realized by traditional genetic search operators, such as crossover and mutation, has not been fully addressed yet. In this paper, we propose a general learning paradigm based on Jumping Genes (JG) to enhance the exploration ability of multiobjective evolutionary algorithms. This paradigm adapts the JG to the continuous search space, and its activation is completely adaptive during the evolutionary process. Moreover, in order to efficiently utilize the useful information, only non-dominated solutions eliminated by the environmental selection are chosen for the secondary exploitation. Empirical studies demonstrate that the performance of a baseline algorithm can be significantly improved by the proposed paradigm.