Characterising fitness landscapes using predictive local search

Marius Gheorghita; Irene Moser; Aldeida Aleti

doi:10.1145/2464576.2464618

Characterising fitness landscapes using predictive local search

Marius Gheorghita, Irene Moser, Aldeida Aleti

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

6 Citations (Scopus)

Abstract

Search space characterisation is a field that strives to define properties of gradients with the general aim of finding the most suitable stochastic algorithms to solve the problems. Diagnostic Optimisation characterises the search landscape while the search progresses. In this work, we have improved Predictive Diagnostic Optimisation to reduce the cost of the local search by introducing a sampling procedure to explore the neighbourhood. The neigbhourhood is created by the swap operator and the sample size recorded during the search is shown to correlate with the known characteristics of the problems.

Original language	English
Title of host publication	GECCO'13, Proceedings of the 2013 Genetic and Evolutionary Computation Conference Companion
Subtitle of host publication	July 6-10, 2013, Amsterdam, The Netherlands
Editors	Christian Blum
Place of Publication	New York, New York
Publisher	Association for Computing Machinery (ACM)
Pages	67-68
Number of pages	2
ISBN (Print)	9781450319645
DOIs	https://doi.org/10.1145/2464576.2464618
Publication status	Published - 2013
Event	The Genetic and Evolutionary Computation Conference 2013 - Amsterdam, Netherlands Duration: 6 Jul 2013 → 10 Jul 2013 Conference number: 15th https://dl.acm.org/doi/proceedings/10.1145/2463372 (Proceedings)

Conference

Conference	The Genetic and Evolutionary Computation Conference 2013
Abbreviated title	GECCO 2013
Country/Territory	Netherlands
City	Amsterdam
Period	6/07/13 → 10/07/13
Internet address	https://dl.acm.org/doi/proceedings/10.1145/2463372 (Proceedings)

Keywords

Artificial intelligence
Diagnostic Optimisation

Access to Document

10.1145/2464576.2464618

Cite this

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title = "Characterising fitness landscapes using predictive local search",

abstract = "Search space characterisation is a field that strives to define properties of gradients with the general aim of finding the most suitable stochastic algorithms to solve the problems. Diagnostic Optimisation characterises the search landscape while the search progresses. In this work, we have improved Predictive Diagnostic Optimisation to reduce the cost of the local search by introducing a sampling procedure to explore the neighbourhood. The neigbhourhood is created by the swap operator and the sample size recorded during the search is shown to correlate with the known characteristics of the problems.",

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author = "Marius Gheorghita and Irene Moser and Aldeida Aleti",

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address = "United States of America",

note = "The Genetic and Evolutionary Computation Conference 2013, GECCO 2013 ; Conference date: 06-07-2013 Through 10-07-2013",

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Gheorghita, M, Moser, I & Aleti, A 2013, Characterising fitness landscapes using predictive local search. in C Blum (ed.), GECCO'13, Proceedings of the 2013 Genetic and Evolutionary Computation Conference Companion: July 6-10, 2013, Amsterdam, The Netherlands. Association for Computing Machinery (ACM), New York, New York, pp. 67-68, The Genetic and Evolutionary Computation Conference 2013, Amsterdam, Netherlands, 6/07/13. https://doi.org/10.1145/2464576.2464618

Characterising fitness landscapes using predictive local search. / Gheorghita, Marius; Moser, Irene; Aleti, Aldeida.
GECCO'13, Proceedings of the 2013 Genetic and Evolutionary Computation Conference Companion: July 6-10, 2013, Amsterdam, The Netherlands. ed. / Christian Blum. New York, New York: Association for Computing Machinery (ACM), 2013. p. 67-68.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

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T1 - Characterising fitness landscapes using predictive local search

AU - Gheorghita, Marius

AU - Moser, Irene

AU - Aleti, Aldeida

N1 - Conference code: 15th

PY - 2013

Y1 - 2013

N2 - Search space characterisation is a field that strives to define properties of gradients with the general aim of finding the most suitable stochastic algorithms to solve the problems. Diagnostic Optimisation characterises the search landscape while the search progresses. In this work, we have improved Predictive Diagnostic Optimisation to reduce the cost of the local search by introducing a sampling procedure to explore the neighbourhood. The neigbhourhood is created by the swap operator and the sample size recorded during the search is shown to correlate with the known characteristics of the problems.

AB - Search space characterisation is a field that strives to define properties of gradients with the general aim of finding the most suitable stochastic algorithms to solve the problems. Diagnostic Optimisation characterises the search landscape while the search progresses. In this work, we have improved Predictive Diagnostic Optimisation to reduce the cost of the local search by introducing a sampling procedure to explore the neighbourhood. The neigbhourhood is created by the swap operator and the sample size recorded during the search is shown to correlate with the known characteristics of the problems.

KW - Artificial intelligence

KW - Diagnostic Optimisation

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BT - GECCO'13, Proceedings of the 2013 Genetic and Evolutionary Computation Conference Companion

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PB - Association for Computing Machinery (ACM)

CY - New York, New York

T2 - The Genetic and Evolutionary Computation Conference 2013

Y2 - 6 July 2013 through 10 July 2013

ER -

Characterising fitness landscapes using predictive local search

Abstract

Conference

Keywords

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