Paixao, T., Perez Heredia, J., Sudholt, D. orcid.org/0000-0001-6020-1646 et al. (1 more author) (2017) Towards a Runtime Comparison of Natural and Artificial Evolution. Algorithmica, 78 (2). pp. 681-713. ISSN 1432-0541
Abstract
Evolutionary algorithms (EAs) form a popular optimisation paradigm inspired by natural evolution. In recent years the field of evolutionary computation has developed a rigorous analytical theory to analyse the runtimes of EAs on many illustrative problems. Here we apply this theory to a simple model of natural evolution. In the Strong Selection Weak Mutation (SSWM) evolutionary regime the time between occurrences of new mutations is much longer than the time it takes for a mutated genotype to take over the population. In this situation, the population only contains copies of one genotype and evolution can be modelled as a stochastic process evolving one genotype by means of mutation and selection between the resident and the mutated genotype. The probability of accepting the mutated genotype then depends on the change in fitness. We study this process, SSWM, from an algorithmic perspective, quantifying its expected optimisation time for various parameters and investigating differences to a similar evolutionary algorithm, the well-known (1+1) EA. We show that SSWM can have a moderate advantage over the (1+1) EA at crossing fitness valleys and study an example where SSWM outperforms the (1+1) EA by taking advantage of information on the fitness gradient.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © The Author(s) 2016. This article is published with open access at Springerlink.com |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Funding Information: | Funder Grant number EUROPEAN COMMISSION - FP6/FP7 SAGE - 138086 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 29 Sep 2016 09:10 |
Last Modified: | 16 Jan 2020 14:39 |
Published Version: | http://dx.doi.org/10.1007/s00453-016-0212-1 |
Status: | Published |
Publisher: | Springer Verlag |
Refereed: | Yes |
Identification Number: | 10.1007/s00453-016-0212-1 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:105250 |