`` Direct Search'' Solution of Numerical and Statistical Problems
Journal of the ACM (JACM)
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
A comprehensive survey of fitness approximation in evolutionary computation
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
An evolutionary algorithm with spatially distributed surrogates for multiobjective optimization
ACAL'07 Proceedings of the 3rd Australian conference on Progress in artificial life
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
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
AbYSS: Adapting Scatter Search to Multiobjective Optimization
IEEE Transactions on Evolutionary Computation
MOEA/D assisted by rbf networks for expensive multi-objective optimization problems
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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The development of multi-objective evolutionary algorithms (MOEAs) assisted by meta-models has increased in the last few years. However, the use of local search engines assisted by meta-models for multi-objective optimization has been less common in the specialized literature. In this paper, we propose the use of a local search mechanism which is assisted by a meta-model based on support vector machines. The local search mechanism adopts a free-derivative mathematical programming technique and consists of two main phases: the first generates approximations of the Pareto optimal set. Such solutions are obtained by solving a set of aggregating functions which are defined by different weighted vectors. The second phase generates new solutions departing from those obtained during the first phase. The solutions found by the local search mechanism are incorporated into the evolutionary process of our MOEA. Our experiments show that our proposed approach can produce good quality results with a budget of only 1,000 fitness function evaluations in test problems having between 10 and 30 decision variables.