The general regression neural network-Rediscovered
Neural Networks
A Hierarchical Genetic Algorithm Using Multiple Models for Optimization
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Surrogate Deterministic Mutation: Preliminary Results
Selected Papers from the 5th European Conference on Artificial Evolution
Memetic algorithm using multi-surrogates for computationally expensive optimization problems
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Population clustering in genetic programming
EuroGP'06 Proceedings of the 9th European conference on Genetic Programming
A framework for evolutionary optimization with approximate fitnessfunctions
IEEE Transactions on Evolutionary Computation
A new fitness estimation strategy for particle swarm optimization
Information Sciences: an International Journal
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Stochastic optimization problems widely exist in engineering, management, control and many other fields. In order to search a more effective algorithm for solving these problems, generalized regression neural network is used as a fitness prediction model and an intelligent algorithm which combines generalized regression neural network with particle swarm optimization is presented. In this intelligent algorithm, according to the mechanism combined prediction model with particle swarm optimization and prediction strategy, some of the individuals' fitness is predicted and the rest is estimated by random simulation. Results of simulations show that the algorithm reduces the computational cost greatly in the premise of performance guarantee.