Optimizing cellular automata through a meta-model assisted memetic algorithm

Authors:
Donato D'Ambrosio;Rocco Rongo;William Spataro;Giuseppe A. Trunfio
Affiliations:
Department of Mathematics, University of Calabria, Rende, CS, Italy;Department of Earth Sciences, University of Calabria, Rende, CS, Italy;Department of Mathematics, University of Calabria, Rende, CS, Italy;DADU, University of Sassari, Alghero, SS, Italy
Venue:
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part II
Year:
2012

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Abstract

This paper investigates the advantages provided by a Meta-model Assisted Memetic Algorithm (MAMA) for the calibration of a Cellular Automata (CA) model. The proposed approach is based on the synergy between a global meta-model, based on a radial basis function network, and a local quadratic approximation of the fitness landscape. The calibration exercise presented here refers to SCIARA, a well-established CA for the simulation of lava flows. Compared with a standard Genetic Algorithm, the adopted MAMA provided much better results within the assigned computational budget.