Author:
Li Yaxin,Liang Jing,Yu Kunjie,Yue Caitong,Zhang Yingjie
Abstract
AbstractFitness landscape analysis devotes to characterizing different properties of optimization problems, such as evolvability, sharpness, and neutrality. Although several landscape features have been proposed, only a few of them can be used in practice as predictors of algorithm performance. In this study, the keenness ($$\textrm{KEE}_{s}$$
KEE
s
) is proposed to characterize the sharpness of the fitness landscape for continuous optimization problems and predict the performance of the differential evolution algorithm. Specifically, a mirror simple random walk algorithm is designed to construct the relevance between the front and back search points in the sampling. The fitness value of each point is replaced by the specific integer. The values in the set of integers with the same circumstance are computed as the feature scalar using the cumulative calculation mechanism. The results of experimental studies in various functions demonstrate the superiority of $$\textrm{KEE}_{s}$$
KEE
s
in terms of accuracy, reliability, and coverage of samples. Moreover, $$\textrm{KEE}_{s}$$
KEE
s
has shown excellent practicability in the application of differential evolution algorithm performance prediction for continuous optimization problems. Thus, $$\textrm{KEE}_{s}$$
KEE
s
is a new landscape feature for fitness landscape analysis of continuous optimization problems and algorithm performance prediction within limited prior knowledge of the unknown problem.
Funder
National Natural Science Foundation of China
China Postdoctoral Science Foundation
Natural Science Foundation of Henan Province
Program for Science & Technology Innovation Talents in Universities of Henan Province
Program for Science & Technology Innovation Teams in Universities of Henan Province
Key R &D and Promotion Projects in Henan Province
Publisher
Springer Science and Business Media LLC
Subject
Computational Mathematics,Engineering (miscellaneous),Information Systems,Artificial Intelligence