Lissovoi, A. and Oliveto, P.S. (2020) Computational complexity analysis of genetic programming. In: Doerr, B. and Neumann, F., (eds.) Theory of Evolutionary Computation: Recent Developments in Discrete Optimization. Natural Computing Series . Springer Nature Switzerland AG , pp. 475-518. ISBN 9783030294137
Abstract
Genetic programming (GP) is an evolutionary computation technique to solve problems in an automated, domain-independent way. Rather than identifying the optimum of a function as in more traditional evolutionary optimization, the aim of GP is to evolve computer programs with a given functionality. While many GP applications have produced human competitive results, the theoretical understanding of what problem characteristics and algorithm properties allow GP to be effective is comparatively limited. Compared with traditional evolutionary algorithms for function optimization, GP applications are further complicated by two additional factors: the variable-length representation of candidate programs, and the difficulty of evaluating their quality efficiently. Such difficulties considerably impact the runtime analysis of GP, where space complexity also comes into play. As a result, initial complexity analyses of GP have focused on restricted settings such as the evolution of trees with given structures or the estimation of solution quality using only a small polynomial number of input/output examples. However, the first computational complexity analyses of GP for evolving proper functions with defined input/output behavior have recently appeared. In this chapter, we present an overview of the state of the art.
Metadata
Item Type: | Book Section |
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Copyright, Publisher and Additional Information: | © Springer Nature Switzerland AG 2020. This is an author-produced version of a book chapter subsequently published in Theory of Evolutionary Computation: Recent Developments in Discrete Optimization. Uploaded in accordance with the publisher's self-archiving policy. |
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 ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL (EPSRC) EP/M004252/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 25 Mar 2019 16:30 |
Last Modified: | 21 Nov 2021 01:38 |
Status: | Published |
Publisher: | Springer Nature Switzerland AG |
Series Name: | Natural Computing Series |
Refereed: | Yes |
Identification Number: | 10.1007/978-3-030-29414-4_11 |
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Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:139536 |