Is Evolutionary Computation evolving fast enough?

Kendall, G. (2018) Is Evolutionary Computation evolving fast enough? IEEE Computational Intelligence Magazine, 13 (2). pp. 42-51. ISSN 1556-603X

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Abstract

Evolutionary Computation (EC) has been an active research area for over 60 years, yet its commercial/home uptake has not been as prolific as we might have expected. By way of comparison, technologies such as 3D printing, which was introduced about 35 years ago, has seen much wider uptake, to the extent that it is now available to home users and is routinely used in manufacturing. Other technologies, such as immersive reality and artificial intelligence have also seen commercial uptake and acceptance by the general public. In this paper we provide a brief history of EC, recognizing the significant contributions that have been made by its pioneers. We focus on two methodologies (Genetic Programming and Hyper-heuristics), which have been proposed as being suitable for automated software development, and question why they are not used more widely by those outside of the academic community. We suggest that different research strands need to be brought together into one framework before wider uptake is possible. We hope that this position paper will serve as a catalyst for automated software development that is used on a daily basis by both companies and home users.

Item Type: Article
Additional Information: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Keywords: Genetic algorithms, Job shop scheduling, Evolutionary computation, Commercialization, Artificial intelligence
Schools/Departments: University of Nottingham, Malaysia > Faculty of Science > School of Computer Science
University of Nottingham, UK > Faculty of Science > School of Computer Science
Identification Number: https://doi.org/10.1109/MCI.2018.2807019
Depositing User: Kendall, Graham
Date Deposited: 05 Feb 2018 10:29
Last Modified: 07 May 2018 01:59
URI: http://eprints.nottingham.ac.uk/id/eprint/49527

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