Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/30821
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings
Author(s): Thomson, Sarah L
Ochoa, Gabriela
Contact Email: s.l.thomson@stir.ac.uk
Title: The Local Optima Level in Chemotherapy Schedule Optimisation
Editor(s): Zarges, Christine
Paquete, Luís
Citation: Thomson SL & Ochoa G (2020) The Local Optima Level in Chemotherapy Schedule Optimisation. In: Zarges C & Paquete L (eds.) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2020. Lecture Notes in Computer Science, 12102. EvoCOP 2020: European Conference on Evolutionary Computation in Combinatorial Optimization, Seville, Spain, 15.04.2020-17.04.2020. Cham, Switzerland: Springer, pp. 197-213. https://doi.org/10.1007/978-3-030-43680-3_13
Issue Date: 2020
Series/Report no.: Lecture Notes in Computer Science, 12102
Conference Name: EvoCOP 2020: European Conference on Evolutionary Computation in Combinatorial Optimization
Conference Dates: 2020-04-15 - 2020-04-17
Conference Location: Seville, Spain
Abstract: In this paper a multi-drug Chemotherapy Schedule Optimisation Problem (CSOP) is subject to Local Optima Network (LON) analysis. LONs capture global patterns in fitness landscapes. CSOPs have not previously been subject to fitness landscape analysis. We fill this gap: LONs are constructed and studied for meaningful structure. The CSOP formulation presents novel challenges and questions for the LON model because there are infeasible regions in the fitness landscape and an unknown global optimum; it also brings a topic from healthcare to LON analysis. Two LON Construction algorithms are proposed for sampling CSOP fitness landscapes: a Markov-Chain Construction Algorithm and a Hybrid Construction Algorithm. The results provide new insight into LONs of highly-constrained spaces, and into the proficiency of search operators on the CSOP. Iterated Local Search and Memetic Search, which are the foundations for the LON algorithms, are found to markedly out-perform a Genetic Algorithm from the literature.
Status: AM - Accepted Manuscript
Rights: This is a post-peer-review, pre-copyedit version of an article published in Zarges C & Paquete L (eds.) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2020. Lecture Notes in Computer Science, 12102. EvoCOP 2020: European Conference on Evolutionary Computation in Combinatorial Optimization, Seville, Spain, 15.04.2020-17.04.2020. Cham, Switzerland: Springer, pp. 197-213. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-43680-3_13
Licence URL(s): https://storre.stir.ac.uk/STORREEndUserLicence.pdf

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