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https://hdl.handle.net/2440/133310
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Type: | Conference paper |
Title: | A fitness landscape analysis of the travelling thief problem |
Author: | Yafrani, M.E. Wagner, M. Martins, M.S.R. Delgado, M.R.B.S. Lders, R. El Krari, M. Ahiod, B. |
Citation: | Proceedings of the 2018 Genetic and Evolutionary Computation Conference, as published in ACM Digital Library, 2018 / Aguirre, H. (ed./s), pp.277-284 |
Publisher: | ACM |
Publisher Place: | online |
Issue Date: | 2018 |
ISBN: | 9781450356183 |
Conference Name: | 2018 Genetic and Evolutionary Computation Conference (15 Jul 2018 - 19 Jul 2018 : Kyoto) |
Editor: | Aguirre, H. |
Statement of Responsibility: | Mohamed El Yafrani, Marcella S. R. Martins, Mehdi El Krari, Markus Wagner, Myriam R. B. S. Delgado, Belaïd Ahiod, Ricardo Lüders |
Abstract: | Local Optima Networks are models proposed to understand the structure and properties of combinatorial landscapes. The fitness landscape is explored as a graph whose nodes represent the local optima (or basins of attraction) and edges represent the connectivity between them. In this paper, we use this representation to study a combinatorial optimisation problem, with two interdepend components, named the Travelling Thief Problem (TTP). The objective is to understand the search space structure of the TTP using basic local search heuristics and to distinguish the most impactful problem features. We create a large set of enumerable TTP instances and generate a Local Optima Network for each instance using two hill climbing variants. Two problem features are investigated, namely the knapsack capacity and profit-weight correlation. Our insights can be useful not only to design landscape-aware local search heuristics, but also to better understand what makes the TTP challenging for specific heuristics. |
Keywords: | Fitness landscape; basins of attraction; local optima networks; multi-component problems; travelling thief problem |
Rights: | © 2018 ACM. |
DOI: | 10.1145/3205455.3205537 |
Grant ID: | http://purl.org/au-research/grants/arc/DE160100850 |
Published version: | http://dx.doi.org/10.1145/3205455.3205537 |
Appears in Collections: | Computer Science publications |
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