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Variable discrimination of crossover versus mutation using parameterized modular structure

Variable discrimination of crossover versus mutation using parameterized modular structure
Variable discrimination of crossover versus mutation using parameterized modular structure
Recent work has provided functions that can be used to prove a principled distinction between the capabilities of mutation-based and crossover-based algorithms. However, prior functions are isolated problem instances that do not provide much intuition about the space of possible functions that is relevant to this distinction or the characteristics of the problem class that affect the relative success of these operators. Modularity is a ubiquitous and intuitive concept in design, engineering and optimisation, and can be used to produce functions that discriminate the ability of crossover from mutation. In this paper, we present a new approach to representing modular problems, which parameterizes the amount of modular structure that is present in the epistatic dependencies of the problem. This adjustable level of modularity can be used to give rise to tuneable discrimination of the ability of genetic algorithms with crossover versus mutation-only algorithms.
1312-1319
Association for Computing Machinery
Mills, Rob
3d53d4bc-e1de-4807-b89b-f5813f2172a7
Watson, Richard A.
ce199dfc-d5d4-4edf-bd7b-f9e224c96c75
Thierens, Dirk
0f991e11-375d-452b-8613-bbc66a799def
Lipson, Hod
2c878483-b35c-4946-be97-e68430f8db7d
Mills, Rob
3d53d4bc-e1de-4807-b89b-f5813f2172a7
Watson, Richard A.
ce199dfc-d5d4-4edf-bd7b-f9e224c96c75
Thierens, Dirk
0f991e11-375d-452b-8613-bbc66a799def
Lipson, Hod
2c878483-b35c-4946-be97-e68430f8db7d

Mills, Rob and Watson, Richard A. (2007) Variable discrimination of crossover versus mutation using parameterized modular structure. Thierens, Dirk and Lipson, Hod (eds.) In GECCO '07 Proceedings of the 9th annual conference on Genetic and evolutionary computation. Association for Computing Machinery. pp. 1312-1319 .

Record type: Conference or Workshop Item (Paper)

Abstract

Recent work has provided functions that can be used to prove a principled distinction between the capabilities of mutation-based and crossover-based algorithms. However, prior functions are isolated problem instances that do not provide much intuition about the space of possible functions that is relevant to this distinction or the characteristics of the problem class that affect the relative success of these operators. Modularity is a ubiquitous and intuitive concept in design, engineering and optimisation, and can be used to produce functions that discriminate the ability of crossover from mutation. In this paper, we present a new approach to representing modular problems, which parameterizes the amount of modular structure that is present in the epistatic dependencies of the problem. This adjustable level of modularity can be used to give rise to tuneable discrimination of the ability of genetic algorithms with crossover versus mutation-only algorithms.

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More information

Published date: July 2007
Organisations: Agents, Interactions & Complexity, EEE

Identifiers

Local EPrints ID: 264033
URI: http://eprints.soton.ac.uk/id/eprint/264033
PURE UUID: ec820a0c-5e8b-4ab0-ac77-9c41f1492218
ORCID for Richard A. Watson: ORCID iD orcid.org/0000-0002-2521-8255

Catalogue record

Date deposited: 23 May 2007
Last modified: 16 Mar 2024 03:42

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Contributors

Author: Rob Mills
Author: Richard A. Watson ORCID iD
Editor: Dirk Thierens
Editor: Hod Lipson

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