Empirical Evaluation of Evolutionary Algorithms with Power-Law Ranking Selection

Duc-Cuong Dang, Anton V. Eremeev, Xiaoyu Qin

Abstract: It has been proven that non-elitist evolutionary algorithms (EAs) with proper selection mechanisms, including the recently proposed power-law ranking selection, can efficiently escape local optima on a broad class of problems called SparseLocalOpt \(_{\alpha ,\varepsilon }\), where elitist EAs fail. However, those theoretical upper bounds on the runtime are not tight as they require large populations and a tight balance between mutation rates and selection pressure to keep the algorithms operating near the so-called “error threshold”. This paper empirically clarifies the significance of these theoretical requirements and makes a series of performance comparisons between the non-elitist EA using power-law ranking selection and other EAs on various benchmark problems.