Stochastic method for the solution of unconstrained vector optimization problems
Journal of Optimization Theory and Applications
Exploiting gradient information in numerical multi--objective evolutionary optimization
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Local search for multiobjective function optimization: pareto descent method
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Implementation of Simple Multiobjective Memetic Algorithms and Its Application to Knapsack Problems
International Journal of Hybrid Intelligent Systems
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
Evolutionary continuation methods for optimization problems
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
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
Computing gap free pareto front approximations with stochastic search algorithms
Evolutionary Computation
Generalization of dominance relation-based replacement rules for memetic EMO algorithms
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
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
HM'07 Proceedings of the 4th international conference on Hybrid metaheuristics
HCS: a new local search strategy for memetic multiobjective evolutionary algorithms
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
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This work presents the main aspects to tackle when designing memetic algorithms using gradient-based local searchers.. We address the main drawbacks and advantages of this coupling, when focusing on the efficiency of the local search stage. We conclude with some guidelines and draw further research paths in these topics.