Paper Info
Title
Estimation of fitness landscape contours in EAs
Abstract
Evolutionary algorithms applied in real domain should profit from information about the local fitness function curvature. This paper presents an initial study of an evolutionary strategy with a novel approach for learning the covariance matrix of a Gaussian distribution. The learning method is based one stimation of the fitness landscape contour line between the selected and discarded individuals. The distribution learned this way is then used to generate new population members. The algorithm presented here is the first attempt to construct the Gaussian distribution this way and should beconsidered only a proof of concept; nevertheless, the empirical comparison on low-dimensional quadratic functions shows that our approach is viable and with respect to the number of evaluations needed to find a solution of certain quality, it is comparable to the state-of-the-art CMA-ES incase of sphere function and outperforms the CMA-ES in case of elliptical function.
Year
DOI
Venue
2007
10.1145/1276958.1277075
GECCO
Keywords
Field
DocType
certain quality,covariance matrix,novel approach,sphere function,elliptical function,fitness landscape contour line,state-of-the-art cma-es incase,gaussian distribution,low-dimensional quadratic function,local fitness function curvature,fitness landscape,estimation of distribution algorithms,evolutionary computation,estimation of distribution algorithm,fitness function,evolutionary algorithm,elliptic function,learnable evolution model,profitability,evolutionary strategy,evolutionary computing,proof of concept
Mathematical optimization,Fitness landscape,Estimation of distribution algorithm,Evolutionary algorithm,Learnable Evolution Model,Computer science,Evolutionary computation,Fitness function,Fitness approximation,Evolution strategy,Artificial intelligence,Machine learning
Conference
Citations 
PageRank 
References 
8
0.47
8
Authors
2
Name
Order
Citations
PageRank
Petr Pošík121015.44
Vojtěch Franc258455.78