Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Adaptive operator probabilities in a genetic algorithm that applies three operators
SAC '97 Proceedings of the 1997 ACM symposium on Applied computing
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Simultaneously Applying Multiple Mutation Operators in Genetic Algorithms
Journal of Heuristics
Stochastic method for the solution of unconstrained vector optimization problems
Journal of Optimization Theory and Applications
Adapting Operator Probabilities in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
Adaptive Crossover Using Automata
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Why Quality Assessment Of Multiobjective Optimizers Is Difficult
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Exploiting gradient information in numerical multi--objective evolutionary optimization
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
An adaptive pursuit strategy for allocating operator probabilities
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Multi-objective genetic algorithms: Problem difficulties and construction of test problems
Evolutionary Computation
Fda -a scalable evolutionary algorithm for the optimization of additively decomposed functions
Evolutionary Computation
Covering pareto sets by multilevel evolutionary subdivision techniques
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Effective use of directional information in multi-objective evolutionary computation
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
The naive MIDEA: a baseline multi-objective EA
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
The balance between proximity and diversity in multiobjective evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Adaptive variance scaling in continuous multi-objective estimation-of-distribution algorithms
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A pareto following variation operator for fast-converging multiobjective evolutionary algorithms
Proceedings of the 10th 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
Adapting to the Habitat: On the Integration of Local Search into the Predator-Prey Model
EMO '09 Proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization
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
HM'07 Proceedings of the 4th international conference on Hybrid metaheuristics
Exploiting second order information in computational multi-objective evolutionary optimization
EPIA'07 Proceedings of the aritficial intelligence 13th Portuguese conference on Progress in artificial intelligence
Using gradient information for multi-objective problems in the evolutionary context
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Evolving policies for multi-reward partially observable markov decision processes (MR-POMDPs)
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Multi-reward policies for medical applications: anthrax attacks and smart wheelchairs
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Elitist archiving for multi-objective evolutionary algorithms: to adapt or not to adapt
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part II
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Recently, gradient techniques for solving numerical multi-objective optimization problems have appeared in the literature. Although promising results have already been obtained when combined with multi-objective evolutionary algorithms (MOEAs), an important question remains: what is the best way to integrate the use of gradient techniques in the evolutionary cycle of a MOEA. In this paper, we present an adaptive resource-allocation scheme that uses three gradient techniques in addition to the variation operator in a MOEA. During optimization, the effectivity of the gradient techniques is monitored and the available computational resources are redistributed to allow the (currently) most effective operator to spend the most resources. In addition, we indicate how the multi-objective search can be stimulated to also search $mboxemphalong $ the Pareto front, ultimately resulting in a better and wider spread of solutions. We perform tests on a few well-known benchmark problems as well as two novel benchmark problems with specific gradient properties. We compare the results of our adaptive resource-allocation scheme with the same MOEA without the use of gradient techniques and a scheme in which resource allocation is constant. The results show that our proposed adaptive resource-allocation scheme makes proper use of the gradient techniques only when required and thereby leads to results that are close to the best results that can be obtained by fine-tuning the resource allocation for a specific problem.