Generalized Hybrid Evolutionary Algorithm Framework with a Mutation Operator Requiring no Adaptation

Foo, Y. W., Goh, C., Chan, L., Li, L. and Li, Y. (2017) Generalized Hybrid Evolutionary Algorithm Framework with a Mutation Operator Requiring no Adaptation. In: The 11th International Conference on Simulated Evolution and Learning (SEAL 2017), Shenzhen, China, 10-13 Nov 2017, pp. 486-498. (doi: 10.1007/978-3-319-68759-9_40)

Abstract

This paper presents a generalized hybrid evolutionary optimization structure that not only combines both nondeterministic and deterministic algorithms on their individual merits and distinct advantages, but also offers behaviors of the three originating classes of evolutionary algorithms (EAs). In addition, a robust mutation operator is developed in place of the necessity of mutation adaptation, based on the mutation properties of binary-coded individuals in a genetic algorithm. The behaviour of this mutation operator is examined in full and its performance is compared with adaptive mutations. The results show that the new mutation operator outperforms adaptive mutation operators while reducing complications of extra adaptive parameters in an EA representation.

• Item Type: Conference Proceedings
• Additional Information: Published in Lecture Notes in Computer Science v. 10593:486-498
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Goh, Dr Cindy Sf
• Authors: Foo, Y. W., Goh, C., Chan, L., Li, L., and Li, Y.
• College/School: College of Science and Engineering > School of Engineering
• ISSN: 0302-9743
• Published Online: 14 October 2017
• Copyright Holders: Copyright © 2017 The AuthorsSpringer International Publishing AG
• First Published: First published in Lecture Notes in Computer Science 10593:486-498
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher.