Guest Editorial Special Issue on Theoretical Foundations of Evolutionary Computation

• Published in: IEEE transactions on evolutionary computation Vol. 24; no. 6; pp. 993 - 994
• Main Authors: Oliveto, Pietro S., Auger, Anne, Chicano, Francisco, Fonseca, Carlos M.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Evolutionary algorithms; Evolutionary computation; Genetic algorithms
• ISSN: 1089-778X; 1941-0026
• DOI: 10.1109/TEVC.2020.3035225

It is our pleasure to introduce this special issue on the recent advances in the theoretical foundations of evolutionary computation (EC). While in the early days of this field, theoretical analyses inevitably focused on simplified models of evolutionary algorithms (EAs), the continuous progress made in the development of suitable mathematical techniques for the analysis now allows to derive proven statements regarding the performance of off-the-shelf metaheuristics, such as standard generational and steady-state genetic algorithms with no algorithmic simplifications. Comparisons between the performance of a given EA with the best possible one can also be made nowadays, allowing to assess whether a given algorithm may be improved upon or whether its performance is optimal for a given class of problems. Such understanding often provides insights for the design of new EAs which provably have better performance for given problems. We are glad that examples of results of this kind are present within this special issue. A total of 27 papers were submitted which were the subject of at least three independent reviews, and six manuscripts of the highest quality were selected for publication in the special issue. In the following, we provide a brief summary of these manuscripts.