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Deep optimisation: learning and searching in deep representations of combinatorial optimisation problems

Deep optimisation: learning and searching in deep representations of combinatorial optimisation problems
Deep optimisation: learning and searching in deep representations of combinatorial optimisation problems
Evolutionary algorithms are a class of optimisation techniques used to solve problems by emulating evolutionary processes (variation and selection) to search the solution space. In this thesis, we focus on the evolutionary process of Evolutionary Transitions in Individuality (ETI). In this case, evolutionary processes are scaled up via a multi-scale process whereby individuality (and hence variation and selection) is continually revised by forming associations between formerly independent entities. This thesis develops a novel Model-Building Optimisation Algorithm (MBOA) called Deep Optimisation (DO) that exploits deep learning methods to enable multi-scale optimisation. DO uses an autoencoder model to induce a multi-level representation of solutions. Variation and selection are then performed within the induced representations, allowing search to continue in a new and reorganised space. By using a class of configurable problems, we find and understand more precisely the distinct problem characteristics that DO can solve that other MBOAs cannot. Specifically, we observe a polynomial vs exponential scaling distinction where DO is the only algorithm to show polynomial scaling for all problems. We also demonstrate that some problem characteristics need a deep network in DO. Further, for the first time, we show that overlap differentiates the performance between current MBOAs that are considered state-of-the-art. DO is then applied to different optimisation problem domains to demonstrate its potential for exploiting unknown problem structure and overcoming infeasible solution spaces. Here, DO shows impressive performance and does so without using a domain-specific operator. This thesis provides a connection between deep learning models and MBOAs, showing results that outperform existing algorithms can be achieved by utilising the tools available in deep learning. This suggests numerous avenues for further investigation, transferring deep learning methods into the domain of MBOAs.
University of Southampton
Caldwell, Jamie, Robert
c5107ff8-32df-4d69-a26d-9f1d1a7859dc
Caldwell, Jamie, Robert
c5107ff8-32df-4d69-a26d-9f1d1a7859dc
Watson, Richard
ce199dfc-d5d4-4edf-bd7b-f9e224c96c75

Caldwell, Jamie, Robert (2022) Deep optimisation: learning and searching in deep representations of combinatorial optimisation problems. University of Southampton, Doctoral Thesis, 186pp.

Record type: Thesis (Doctoral)

Abstract

Evolutionary algorithms are a class of optimisation techniques used to solve problems by emulating evolutionary processes (variation and selection) to search the solution space. In this thesis, we focus on the evolutionary process of Evolutionary Transitions in Individuality (ETI). In this case, evolutionary processes are scaled up via a multi-scale process whereby individuality (and hence variation and selection) is continually revised by forming associations between formerly independent entities. This thesis develops a novel Model-Building Optimisation Algorithm (MBOA) called Deep Optimisation (DO) that exploits deep learning methods to enable multi-scale optimisation. DO uses an autoencoder model to induce a multi-level representation of solutions. Variation and selection are then performed within the induced representations, allowing search to continue in a new and reorganised space. By using a class of configurable problems, we find and understand more precisely the distinct problem characteristics that DO can solve that other MBOAs cannot. Specifically, we observe a polynomial vs exponential scaling distinction where DO is the only algorithm to show polynomial scaling for all problems. We also demonstrate that some problem characteristics need a deep network in DO. Further, for the first time, we show that overlap differentiates the performance between current MBOAs that are considered state-of-the-art. DO is then applied to different optimisation problem domains to demonstrate its potential for exploiting unknown problem structure and overcoming infeasible solution spaces. Here, DO shows impressive performance and does so without using a domain-specific operator. This thesis provides a connection between deep learning models and MBOAs, showing results that outperform existing algorithms can be achieved by utilising the tools available in deep learning. This suggests numerous avenues for further investigation, transferring deep learning methods into the domain of MBOAs.

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Published date: June 2022

Identifiers

Local EPrints ID: 458168
URI: http://eprints.soton.ac.uk/id/eprint/458168
PURE UUID: 7430621c-5cb5-4bb9-b026-f68bf6c71878
ORCID for Richard Watson: ORCID iD orcid.org/0000-0002-2521-8255

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Date deposited: 30 Jun 2022 16:35
Last modified: 01 Oct 2022 01:39

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Contributors

Author: Jamie, Robert Caldwell
Thesis advisor: Richard Watson ORCID iD

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