Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Combining convergence and diversity in evolutionary multiobjective optimization
Evolutionary Computation
A computationally efficient evolutionary algorithm for real-parameter optimization
Evolutionary Computation
Exploiting gradient information in numerical multi--objective evolutionary optimization
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Self-Adaptive Genetic Algorithms with Simulated Binary Crossover
Evolutionary Computation
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Effective use of directional information in multi-objective evolutionary computation
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Gradient Based Stochastic Mutation Operators in Evolutionary Multi-objective Optimization
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
Functional-Specialization Multi-Objective Real-Coded Genetic Algorithm: FS-MOGA
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Using gradient-based information to deal with scalability in multi-objective evolutionary algorithms
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
On gradient based local search methods in unconstrained evolutionary multi-objective optimization
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Constraint-handling method for multi-objective function optimization: Pareto descent repair operator
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
New challenges for memetic algorithms on continuous multi-objective problems
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
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Hybridization with local search (LS) is known to enhance the performance of genetic algorithms (GA) in single objective optimization and have also been studied in the multiobjective combinatorial optimization literature. In most such studies, LS is applied to the solutions of each generation of GA, which is the scheme called "GA with LS" herein. Another scheme, in which LS is applied to the solutions obtained with GA, has also been studied, which is called "GA then LS" herein. It seems there is no consensus in the literature as to which scheme is better, let alone the reasoning for it. The situation in the multiobjective function optimization literature is even more unclear since the number of such studies in the field has been small.This paper, assuming that objective functions are differentiable, reveals the reasons why GA is not suitable for obtaining solutions of high precision, thereby justifying hybridization of GA and LS. It also suggests that the hybridization scheme which maximally exploits both GA and LS is GA then LS. Experiments conducted on many benchmark problems verified our claims.