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dc.contributor.authorFyvie, Martinen_UK
dc.contributor.authorMcCall, John A Wen_UK
dc.contributor.authorChristie, Lee Aen_UK
dc.contributor.authorZavoianu, Alexandru-Ciprianen_UK
dc.contributor.authorBrownlee, Alexander E Ien_UK
dc.contributor.authorAinslie, Russellen_UK
dc.description.abstractThe use of Artificial Intelligence-driven solutions in domains involving end-user interaction and cooperation has been continually growing. This has also lead to an increasing need to communicate crucial information to end-users about algorithm behaviour and the quality of solutions. In this paper, we apply our method of search trajectory mining through decomposition to the solutions created by a Genetic Algorithm-a non-deterministic, population-based metaheuristic. We complement this method with the use of One-Way ANOVA statistical testing to help identify explanatory features found in the search trajectories-subsets of the set of optimization variables having both high and low influence on the search behaviour of the GA and solution quality. This allows us to highlight these to an end-user to allow for greater flexibility in solution selection. We demonstrate the techniques on a real-world staff rostering problem and show how, together, they identify the personnel who are critical to the optimality of the rosters being created.en_UK
dc.relationFyvie M, McCall JAW, Christie LA, Zavoianu A, Brownlee AEI & Ainslie R (2023) Explaining A Staff Rostering Problem By Mining Trajectory Variance Structures. In: <i>TBC</i>. Lecture Notes in Artificial Intelligence. AI-2023 Forty-third SGAI International Conference on Artificial Intelligence, Cambridge, 12.12.2023-14.12.2023. Cham, Switzerland: Springer.en_UK
dc.relation.ispartofseriesLecture Notes in Artificial Intelligenceen_UK
dc.rightsThis item has been embargoed for a period. During the embargo please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study.en_UK
dc.subjectEvolutionary Algorithmsen_UK
dc.subjectPopulation Diversityen_UK
dc.titleExplaining A Staff Rostering Problem By Mining Trajectory Variance Structuresen_UK
dc.typeConference Paperen_UK
dc.rights.embargoreason[SGAI_2023___Explaining_A_Staff_Rostering_Problem_By_Mining_Trajectory_Variance_Structures.pdf] Publisher requires embargo of 12 months after publication.en_UK
dc.type.statusAM - Accepted Manuscripten_UK
dc.citation.conferencedates2023-12-12 - 2023-12-14en_UK
dc.citation.conferencenameAI-2023 Forty-third SGAI International Conference on Artificial Intelligenceen_UK
dc.publisher.addressCham, Switzerlanden_UK
dc.description.notesOutput Status: Forthcomingen_UK
dc.contributor.affiliationRobert Gordon Universityen_UK
dc.contributor.affiliationRobert Gordon Universityen_UK
dc.contributor.affiliationRobert Gordon Universityen_UK
dc.contributor.affiliationRobert Gordon Universityen_UK
dc.contributor.affiliationComputing Scienceen_UK
dc.contributor.affiliationBT Group Plcen_UK
rioxxterms.apcnot requireden_UK
rioxxterms.typeConference Paper/Proceeding/Abstracten_UK
local.rioxx.authorFyvie, Martin|en_UK
local.rioxx.authorMcCall, John A W|en_UK
local.rioxx.authorChristie, Lee A|en_UK
local.rioxx.authorZavoianu, Alexandru-Ciprian|en_UK
local.rioxx.authorBrownlee, Alexander E I|0000-0003-2892-5059en_UK
local.rioxx.authorAinslie, Russell|en_UK
local.rioxx.projectProject ID unknown|Datalab|en_UK
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings

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