Computational Intelligence: An Introduction
Computational Intelligence: An Introduction
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
A Unifying View of Sparse Approximate Gaussian Process Regression
The Journal of Machine Learning Research
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Accelerating evolutionary algorithms with Gaussian process fitness function models
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
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Real-world optimization problems often are non-convex, non-differentiable and highly multimodal, which is why stochastic, population-based metaheuristics are frequently applied. If the optimization problem is also computationally very expensive, only relatively few function evaluations can be afforded. We develop a model-assisted optimization approach as a coupling of Gaussian Process modeling, a regression technique from machine learning, with the Particle Swarm Optimization metaheuristic. It uses earlier function evaluations to predict areas of improvement and exploits the model information in the heuristic search. Under the assumption of a costly target function, it is shown that model-assistance improves the performance across a set of standard benchmark functions. In return, it is possible to reduce the number of target function evaluations to reach a certain fitness level to speed up the search.