Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Combining convergence and diversity in evolutionary multiobjective optimization
Evolutionary Computation
A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Rotationally invariant crossover operators in evolutionary multi-objective optimization
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
A review of multiobjective test problems and a scalable test problem toolkit
IEEE Transactions on Evolutionary Computation
Comprehensive Survey of the Hybrid Evolutionary Algorithms
International Journal of Applied Evolutionary Computation
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This paper presents a Genetic Algorithm (GA) for multi-objective function optimization. In multi-objective function optimization, we believe that GA should adaptively switch search strategies in the early stage and the last stage for effective search. Non-biased sampling and family-wise alternation are suitable to overcome local Pareto optima in the early stage of search, and extrapolation-directed sampling and population-wise alternation are effective to cover the Pareto front in the last stage. These situation-dependent requests make it difficult to keep good performance through the whole search process by repeating a single strategy. We propose a new GA that switches two search strategies, each of which is specialized for global and local search, respectively. This is done by utilizing the ratio of non-dominated solutions in the population. We examine the effectiveness of the proposed method using benchmarks and a real-world problem.