Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/23005
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings
Peer Review Status: Refereed
Authors: Ochoa, Gabriela
Veerapen, Nadarajen
Whitley, Darrell
Burke, Edmund
Contact Email: nve@cs.stir.ac.uk
Title: The Multi-Funnel Structure of TSP Fitness Landscapes: A Visual Exploration
Editors: Bonnevay, S
Legrand, P
Monmarché, N
Lutton, E
Schoenauer, M
Citation: Ochoa G, Veerapen N, Whitley D & Burke E (2016) The Multi-Funnel Structure of TSP Fitness Landscapes: A Visual Exploration, Bonnevay S, Legrand P, Monmarché N, Lutton E, Schoenauer M (ed.) Artificial Evolution: 12th International Conference, Evolution Artificielle, EA 2015, Lyon, France, October 26-28, 2015. Revised Selected Papers, International Conference on Artificial Evolution (EA-2015), Lyon, France, 26.10.2015 - 28.10.2015, Cham, Switzerland: Springer, pp. 1-13.
Issue Date: 2016
Series/Report no.: Lecture Notes in Computer Science, 9554
Conference Name: International Conference on Artificial Evolution (EA-2015)
Conference Dates: 2015-10-26T00:00:00Z
Conference Location: Lyon, France
Abstract: We use the Local Optima Network model to study the structure of symmetric TSP fitness landscapes. The `big-valley' hypothesis holds that for TSP and other combinatorial problems, local optima are not randomly distributed, instead they tend to be clustered around the global optimum. However, a recent study has observed that, for solutions close in evaluation to the global optimum, this structure breaks down into multiple valleys, forming what has been called `multiple funnels'. The multiple funnel concept implies that local optima are organised into clusters, so that a particular local optimum largely belongs to a particular funnel. Our study is the first to extract and visualise local optima networks for TSP and is based on a sampling methodology relying on the Chained Lin-Kernighan algorithm. We confirm the existence of multiple funnels on two selected TSP instances, finding additional funnels in a previously studied instance. Our results suggests that transitions among funnels are possible using operators such as `double-bridge'. However, for consistently escaping sub-optimal funnels, more robust escaping mechanisms are required.
Type: Conference Paper
Status: Book Chapter: author post-print (pre-copy editing)
Rights: Publisher policy allows this work to be made available in this repository; Published in Bonnevay S, Legrand P, Monmarché N, Lutton E, Schoenauer M (ed.) Artificial Evolution: 12th International Conference, Evolution Artificielle, EA 2015, Lyon, France, October 26-28, 2015. Revised Selected Papers, International Conference on Artificial Evolution (EA-2015), Lyon, France, 26.10.2015 - 28.10.2015, Cham, Switzerland: Springer, pp. 1-13. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-31471-6_1
URI: http://hdl.handle.net/1893/23005
URL: http://link.springer.com/chapter/10.1007/978-3-319-31471-6_1
Affiliation: Computing Science - CSM Dept
Computing Science - CSM Dept
Colorado State University
Computing Science - CSM Dept

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