Adaptive local landscape feature vector for problem classification and algorithm selection

Abstract

Fitness landscape analysis is a data-driven technique to study the relationship between problem characteristics and algorithm performance by characterizing the landscape features in the search space of an optimization problem. However, most of the existing landscape features still face poor in classifying the problems and low accuracy in selecting the most appropriate algorithm for a given problem. In this study, an adaptive local landscape feature vector (ALLFV) is proposed for problem classification and algorithm selection. Specifically, an adaptive discretization scheme is designed to calculate adaptive related parameters and construct the sequence between the fitness values of the search point and its nearest neighbors. By considering the frequencies of the same sequence values, the spatial structural information for the fitness landscape is computed as a feature vector according to the feature vector calculation mechanism. The experimental results tested on various problems demonstrate the excellence of ALLFV in terms of accuracy, stability, and computational cost. Moreover, ALLFV has shown superior practicality and reliability in the application of algorithm selection for numerical optimization problems. Consequently, ALLFV is well suited as an alternative for problem classification, as well as algorithm selection under excessive candidate optimization algorithms and limited prior knowledge of problems.