Generalization and parameter estimation in feedforward nets: some experiments
Advances in neural information processing systems 2
Future Generation Computer Systems - Special issue on cellular automata: promise in computational science
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A comprehensive survey of fitness approximation in evolutionary computation
Soft Computing - A Fusion of Foundations, Methodologies and Applications
A comparison of evolutionary algorithms for automatic calibration of constrained cellular automata
ICCSA'10 Proceedings of the 2010 international conference on Computational Science and Its Applications - Volume Part I
A general regression neural network
IEEE Transactions on Neural Networks
Optimizing cellular automata through a meta-model assisted memetic algorithm
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part II
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The automatic optimization of Cellular Automata (CA) models often requires a large number of time-consuming simulations before an acceptable solution can be found. As a result, CA optimization processes may involve significant computational resources. In this paper we investigate the possibility of speeding up a CA calibration through the approach of meta-model assisted search, which is widely used in many fields. The adopted technique relies on inexpensive surrogate functions able to approximate the fitness corresponding to the CA simulations. The calibration exercise presented here refers to SCIARA, a CA for the simulation of lava flows. According to the preliminary results, the use of meta-models enables to achieve a significant gain in computational time.