Keenness for characterizing continuous optimization problems and predicting differential evolution algorithm performance

Abstract

Abstract Fitness 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.

Keywords

• Fitness landscape analysis
• Random walk
• Differential evolution
• Algorithm performance prediction
• Continuous optimization problem