Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Metamodels for simulation input-output relations
WSC '92 Proceedings of the 24th conference on Winter simulation
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Fitness inheritance in genetic algorithms
SAC '95 Proceedings of the 1995 ACM symposium on Applied computing
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
Artificial Intelligence Review - Special issue on lazy learning
A two-dimensional interpolation function for irregularly-spaced data
ACM '68 Proceedings of the 1968 23rd ACM national conference
A comprehensive survey of fitness approximation in evolutionary computation
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Faster convergence by means of fitness estimation
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Kriging interpolation in simulation: a survey
WSC '04 Proceedings of the 36th conference on Winter simulation
Grid enabled sequential design and adaptive metamodeling
Proceedings of the 38th conference on Winter simulation
Adaptive fuzzy fitness granulation for evolutionary optimization
International Journal of Approximate Reasoning
Generalized sampling: a variational approach .I. Theory
IEEE Transactions on Signal Processing
Generalized sampling: a variational approach .II. Applications
IEEE Transactions on Signal Processing
A framework for evolutionary optimization with approximate fitnessfunctions
IEEE Transactions on Evolutionary Computation
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In order to find a satisfactory solution, genetic algorithms, in spite of their ability to solve difficult optimization problems, usually require a large number of fitness evaluations. When expensive simulations are required, using genetic algorithms as optimization tools can become prohibitive. In this paper we present a strategy for introducing surrogate models into genetic algorithms in order to enhance the quality of the final results, where a fixed budget of simulations is imposed. In this strategy, only a fraction of the population is evaluated by the exact function, thus allowing for more generations to evolve the population. The results obtained indicate that the proposed framework arises as an attractive alternative to improve the performance of the genetic algorithm within a fixed budget of expensive fitness evaluations.