Experimental Comparison of Six Population-Based Algorithms for Continuous Black Box Optimization

Abstract

Six population-based methods for real-valued black box optimization are thoroughly compared in this article. One of them, Nelder-Mead simplex search, is rather old, but still a popular technique of direct search. The remaining five (POEMS, G3PCX, Cauchy EDA, BIPOP-CMA-ES, and CMA-ES) are more recent and came from the evolutionary computation community. The recently proposed comparing continuous optimizers (COCO) methodology was adopted as the basis for the comparison. The results show that BIPOP-CMA-ES reaches the highest success rates and is often also quite fast. The results of the remaining algorithms are mixed, but Cauchy EDA and POEMS are usually slow.

Automatic correction of barrel distorted images using a cascaded evolutionary estimator

“…At a smooth run, 60-80 generations is sufficient, seldom less. In the experiments, the convergence of CMA and jDE (rand2best1/bin) and other EAs tested was verified over several benchmark functions -see (Pošík & Kubalík 2012;Moravec 2014); e.g., (Whitley et al 1996) with number of dimensions 7. In this specific case the optimizer jDE ( ( ) 0.1679) conducted better than CMA ( ( ) 19.22).…”

A Novel Tournament Selection Based Differential Evolution Variant for Continuous Optimization Problems

“…To decrease the risk of premature convergence, the loss of diversity should be kept as low as possible in the population [47]. To enhance the performance of population based algorithms, various researches have used tournament selection in their research work [48,49]. The DE algorithm sometimes faces the problem of slow and/or premature convergence [32].…”