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Transforming evolutionary search into higher-level evolutionary search by capturing problem structure

Transforming evolutionary search into higher-level evolutionary search by capturing problem structure
Transforming evolutionary search into higher-level evolutionary search by capturing problem structure
The intuitive idea that good solutions to small problems can be reassembled into good solutions to larger problems is widely familiar in many fields including evolutionary computation. This idea has motivated the building-block hypothesis and model-building optimization methods that aim to identify and exploit problem structure automatically. Recently, a small number of works make use of such ideas by learning problem structure and using this information in a particular manner: these works use the results of a simple search process in primitive units to identify structural correlations (such as modularity) in the problem that are then used to redefine the variational operators of the search process. This process is applied recursively such that search operates at successively higher scales of organization, hence multi-scale search. Here, we show for the first time that there is a simple class of (modular) problems that a multi-scale search algorithm can solve in polynomial time that requires super-polynomial time for other methods. We discuss strengths and limitations of the multi-scale search approach and note how it can be developed further.
628 - 642
Mills, Robert
3d53d4bc-e1de-4807-b89b-f5813f2172a7
Jansen, Thomas
edc5f64e-f310-462c-b724-da5ddc826ddc
Watson, Richard
ce199dfc-d5d4-4edf-bd7b-f9e224c96c75
Mills, Robert
3d53d4bc-e1de-4807-b89b-f5813f2172a7
Jansen, Thomas
edc5f64e-f310-462c-b724-da5ddc826ddc
Watson, Richard
ce199dfc-d5d4-4edf-bd7b-f9e224c96c75

Mills, Robert, Jansen, Thomas and Watson, Richard (2014) Transforming evolutionary search into higher-level evolutionary search by capturing problem structure. IEEE Transactions on Evolutionary Computation, 18 (5), 628 - 642. (doi:10.1109/TEVC.2014.2347702).

Record type: Article

Abstract

The intuitive idea that good solutions to small problems can be reassembled into good solutions to larger problems is widely familiar in many fields including evolutionary computation. This idea has motivated the building-block hypothesis and model-building optimization methods that aim to identify and exploit problem structure automatically. Recently, a small number of works make use of such ideas by learning problem structure and using this information in a particular manner: these works use the results of a simple search process in primitive units to identify structural correlations (such as modularity) in the problem that are then used to redefine the variational operators of the search process. This process is applied recursively such that search operates at successively higher scales of organization, hence multi-scale search. Here, we show for the first time that there is a simple class of (modular) problems that a multi-scale search algorithm can solve in polynomial time that requires super-polynomial time for other methods. We discuss strengths and limitations of the multi-scale search approach and note how it can be developed further.

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e-pub ahead of print date: 13 August 2014
Published date: October 2014

Identifiers

Local EPrints ID: 420787
URI: https://eprints.soton.ac.uk/id/eprint/420787
PURE UUID: 50078af5-2402-47ba-b8c5-77f5d630a9f4

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Date deposited: 16 May 2018 16:30
Last modified: 16 May 2018 16:30

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