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
Genetic Search with Approximate Function Evaluation
Proceedings of the 1st International Conference on Genetic Algorithms
Comparison Of Methods For Using Reduced Models To Speed Up Design Optimization
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Improving evolutionary exploration to area-time optimization of FPGA designs
Journal of Systems Architecture: the EUROMICRO Journal
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Generalizing surrogate-assisted evolutionary computation
IEEE Transactions on Evolutionary Computation
Local meta-models for optimization using evolution strategies
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Local function approximation in evolutionary algorithms for the optimization of costly functions
IEEE Transactions on Evolutionary Computation
Evolutionary optimization in uncertain environments-a survey
IEEE Transactions on Evolutionary Computation
Single- and multiobjective evolutionary optimization assisted by Gaussian random field metamodels
IEEE Transactions on Evolutionary Computation
Smooth function approximation using neural networks
IEEE Transactions on Neural Networks
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In this paper we compare two strategies using locally weighted regression as a surrogate model to improve the efficiency of a real-coded generational genetic algorithm where a fixed budget of simulations is imposed. Only a fraction of the candidate solutions are evaluated exactly, allowing for more generations to evolve the population (the number of generations increases according to a user defined parameter). We test the proposed strategies on a set of benchmark optimization problems from the literature. The results show that the surrogate strategies can improve the performance of the GA depending on the user defined parameter. We suggest a threshold value to this parameter so that the locally weighted regression can be used to enhance the efficiency of genetic algorithms, when the number of calls to the expensive simulation is limited.