Article

Optimising cancer chemotherapy using an estimation of distribution algorithm and genetic algorithms

Authors:

Andrei Petrovski,

Siddhartha Shakya,

John McCallAuthors Info & Claims

GECCO '06: Proceedings of the 8th annual conference on Genetic and evolutionary computation

Pages 413 - 418

https://doi.org/10.1145/1143997.1144073

Published: 08 July 2006 Publication History

Abstract

This paper presents a methodology for using heuristic search methods to optimise cancer chemotherapy. Specifically, two evolutionary algorithms - Population Based Incremental Learning (PBIL), which is an Estimation of Distribution Algorithm (EDA), and Genetic Algorithms (GAs) have been applied to the problem of finding effective chemotherapeutic treatments. To our knowledge, EDAs have been applied to fewer real world problems compared to GAs, and the aim of the present paper is to expand the application domain of this technique.We compare and analyse the performance of both algorithms and draw a conclusion as to which approach to cancer chemotherapy optimisation is more efficient and helpful in the decision-making activity led by the oncologists.

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https://doi.org/10.1007/s10596-024-10331-9
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cover image ACM Conferences

GECCO '06: Proceedings of the 8th annual conference on Genetic and evolutionary computation

July 2006

2004 pages

ISBN:1595931864

DOI:10.1145/1143997

General Chair:
Mike Cattolico
Tiger Mountain Scientific, Inc., USA

Copyright © 2006 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 08 July 2006

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GECCO06: Genetic and Evolutionary Computation Conference

July 8 - 12, 2006

Washington, Seattle, USA

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GECCO '06 Paper Acceptance Rate 205 of 446 submissions, 46%;

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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https://doi.org/10.1007/s10596-024-10331-9
Zannat Ahmed ANath Das K(2024)Ethical Issues on Drug Delivery and Its Impact in HealthcareComputational Intelligence in Healthcare Informatics10.1007/978-981-99-8853-2_19(307-328)Online publication date: 22-Feb-2024
https://doi.org/10.1007/978-981-99-8853-2_19
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https://dl.acm.org/doi/10.1007/978-3-030-91100-3_19
Goranova MContreras-Cruz MHoyle AOchoa G(2020)Optimising Antibiotic Treatments with Multi-objective Population-based Algorithms2020 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC48606.2020.9185489(1-7)Online publication date: 19-Jul-2020
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Thomson SOchoa G(2020)The Local Optima Level in Chemotherapy Schedule OptimisationEvolutionary Computation in Combinatorial Optimization10.1007/978-3-030-43680-3_13(197-213)Online publication date: 15-Apr-2020
https://dl.acm.org/doi/10.1007/978-3-030-43680-3_13
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Shakya SPoon KOuali ATakadama K(2018)A GA based network optimization tool for passive in-building distributed antenna systemsProceedings of the Genetic and Evolutionary Computation Conference10.1145/3205455.3205640(1371-1378)Online publication date: 2-Jul-2018
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Szlachcic EKlempous R(2018)Performance Assessment of Optimal Chemotherapy Strategies for Cancer Treatment PlanningComputer Aided Systems Theory – EUROCAST 201710.1007/978-3-319-74727-9_46(386-393)Online publication date: 26-Jan-2018
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Shakya SBeum Seuk Lee Di Cairano-Gilfedder COwusu G(2017)Spares parts optimization for legacy telecom networks using a permutation-based evolutionary algorithm2017 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2017.7969512(1742-1748)Online publication date: 5-Jun-2017
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Takiyar SSingh V(2015)Trend analysis and evolution of Short Term Load Forecasting Techniques2015 4th International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions)10.1109/ICRITO.2015.7359233(1-6)Online publication date: Sep-2015
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