"ADMISSIBLE METHOD FOR IMPROVED GENETIC SEARCH IN CELLULAR AUTOMATA MODEL (AMMISCA)": A STRATEGY IN GENETIC CALIBRATION - PRELIMINARY RESULTS

被引：0

作者：

Umeton, R. ^{[1
]}

Di Gregorio, S. ^{[1
]}

机构：

[1] Univ Calabria, Dept Math, I-87036 Arcavacata Di Rende, CS, Italy

来源：

ARTIFICIAL LIFE AND EVOLUTIONARY COMPUTATION | 2010年

关键词：

genetic algorithms; model calibration; cellular automata; lava flows; FLOWS;

D O I：

10.1142/9789814287456_0009

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Genetic Algorithms (GAs) are widely used to incrementally reach admissible solutions for hard problems such as parameter tuning in Cellular Automata (CA) models. This paper presents a genetic strategy, specifically developed for CA model calibration, exploiting the circumstance that the considered CA parameters have a physical meaning. The proposed approach has proved to be comparable and, in some cases outperforming, if compared with the standard GA proposed by Holland. As a further result, the goodness of the proposed genetic strategy opens the door to genetic tuning algorithms lacking of a standard crossover operator. Results, though preliminary, can be considered encouraging and routes to a wider analysis of the proposed approach.

引用

页码：99 / 108

页数：10

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