Adaptive-surrogate based on a neuro-fuzzy network and granular computing

Israel Cruz-Vega, Mauricio Garcia-Limon, Hugo Jair Escalante
2014 Proceedings of the 2014 conference on Genetic and evolutionary computation - GECCO '14  
Surrogate-based methods aim at reducing the evaluation of expensive fitness functions in optimization processes. Several surrogate-based methods for evolutionary optimization have been proposed so far, including those based on granular computing / clustering. Granular computing provides granules as an assemblage of entities arranged together by their similarity, functional or physical adjacency, indistinguishability, coherency, or the like. Techniques like this avoid multiple and unnecessary
more » ... luations of individuals repeatedly. In this paper, with the aim of granular computing as a method of grouping data, such information is exploited to obtain knowledge of the structure and parameters of individuals and then, design a Neuro-Fuzzy network that adapts granules' parameters, providing convergence to acceptable solutions with a reduced number of evaluations of the fitness function. We implement this adaptive surrogate in a genetic algorithm and show its performance using benchmark functions.
doi:10.1145/2576768.2598376 dblp:conf/gecco/Cruz-VegaGE14 fatcat:xxcukja5jrewjpr43wl6ioqe7q