A Pyramidal Evolutionary Algorithm with Different Inter-Agent Partnering Strategies for Scheduling Problems

This paper combines the idea of a hierarchical distributed genetic algorithm with different inter-agent partnering strategies. Cascading clusters of sub-populations are built from bottom up, with higher-level sub-populations optimising larger parts of the problem. Hence higher-level sub-populations...

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Main Author: Aickelin, Uwe
Format: Conference or Workshop Item
Published: 2001
Online Access:https://eprints.nottingham.ac.uk/638/
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author Aickelin, Uwe
author_facet Aickelin, Uwe
author_sort Aickelin, Uwe
building Nottingham Research Data Repository
collection Online Access
description This paper combines the idea of a hierarchical distributed genetic algorithm with different inter-agent partnering strategies. Cascading clusters of sub-populations are built from bottom up, with higher-level sub-populations optimising larger parts of the problem. Hence higher-level sub-populations search a larger search space with a lower resolution whilst lower-level sub-populations search a smaller search space with a higher resolution. The effects of different partner selection schemes amongst the agents on solution quality are examined for two multiple-choice optimisation problems. It is shown that partnering strategies that exploit problem-specific knowledge are superior and can counter inappropriate (sub-) fitness measurements.
first_indexed 2025-11-14T18:12:49Z
format Conference or Workshop Item
id nottingham-638
institution University of Nottingham Malaysia Campus
institution_category Local University
last_indexed 2025-11-14T18:12:49Z
publishDate 2001
recordtype eprints
repository_type Digital Repository
spelling nottingham-6382020-05-04T20:32:37Z https://eprints.nottingham.ac.uk/638/ A Pyramidal Evolutionary Algorithm with Different Inter-Agent Partnering Strategies for Scheduling Problems Aickelin, Uwe This paper combines the idea of a hierarchical distributed genetic algorithm with different inter-agent partnering strategies. Cascading clusters of sub-populations are built from bottom up, with higher-level sub-populations optimising larger parts of the problem. Hence higher-level sub-populations search a larger search space with a lower resolution whilst lower-level sub-populations search a smaller search space with a higher resolution. The effects of different partner selection schemes amongst the agents on solution quality are examined for two multiple-choice optimisation problems. It is shown that partnering strategies that exploit problem-specific knowledge are superior and can counter inappropriate (sub-) fitness measurements. 2001 Conference or Workshop Item PeerReviewed Aickelin, Uwe (2001) A Pyramidal Evolutionary Algorithm with Different Inter-Agent Partnering Strategies for Scheduling Problems. In: Genetic and Evolutionary Computation Conference, late-breaking papers volume, 2001.
spellingShingle Aickelin, Uwe
A Pyramidal Evolutionary Algorithm with Different Inter-Agent Partnering Strategies for Scheduling Problems
title A Pyramidal Evolutionary Algorithm with Different Inter-Agent Partnering Strategies for Scheduling Problems
title_full A Pyramidal Evolutionary Algorithm with Different Inter-Agent Partnering Strategies for Scheduling Problems
title_fullStr A Pyramidal Evolutionary Algorithm with Different Inter-Agent Partnering Strategies for Scheduling Problems
title_full_unstemmed A Pyramidal Evolutionary Algorithm with Different Inter-Agent Partnering Strategies for Scheduling Problems
title_short A Pyramidal Evolutionary Algorithm with Different Inter-Agent Partnering Strategies for Scheduling Problems
title_sort pyramidal evolutionary algorithm with different inter-agent partnering strategies for scheduling problems
url https://eprints.nottingham.ac.uk/638/