Learning classifier systems for optimisation problems: A case study on fractal travelling salesman problem
This paper presents a set of experiments on the use of Learning Classifier Systems for the purpose of solving combinatorial optimisation problems. We demonstrate our approach with a set of Fractal Travelling Salesman Problem (TSP) instances for which it is possible to know by construction the optima...
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Acceso en línea: | http://hdl.handle.net/20.500.12110/paper_97816055_v_n_p2039_Tabacman |
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todo:paper_97816055_v_n_p2039_Tabacman2023-10-03T16:44:06Z Learning classifier systems for optimisation problems: A case study on fractal travelling salesman problem Tabacman, M. Bacardit, J. Krasnogor, N. Loiseau, I. Learning-classifier-systems Machine learning Optimization Traveling-salesman-problem Classifiers Combinatorial optimization Education Fractals Learning systems Case studies Combinatorial optimisation Learning rules Learning-classifier-systems Machine learning Optimal tours Optimisation Travelling salesman problems Traveling salesman problem This paper presents a set of experiments on the use of Learning Classifier Systems for the purpose of solving combinatorial optimisation problems. We demonstrate our approach with a set of Fractal Travelling Salesman Problem (TSP) instances for which it is possible to know by construction the optimal tour regardless of the number of cities in them. This special type of TSP instances are ideal for testing new methods as they are well characterised in their solvability and easy to use for scalability studies. Our results show that an LCS is capable of learning rules to recognise to which family of instances a set containing a sample of the cities in the problem belongs to and hence automatically select the best heuristic to solve it. Moreover, we have also trained the LCS to recognise links belonging to the optimal tour. Copyright 2008 ACM. Fil:Tabacman, M. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Loiseau, I. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. CONF info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_97816055_v_n_p2039_Tabacman |
institution |
Universidad de Buenos Aires |
institution_str |
I-28 |
repository_str |
R-134 |
collection |
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) |
topic |
Learning-classifier-systems Machine learning Optimization Traveling-salesman-problem Classifiers Combinatorial optimization Education Fractals Learning systems Case studies Combinatorial optimisation Learning rules Learning-classifier-systems Machine learning Optimal tours Optimisation Travelling salesman problems Traveling salesman problem |
spellingShingle |
Learning-classifier-systems Machine learning Optimization Traveling-salesman-problem Classifiers Combinatorial optimization Education Fractals Learning systems Case studies Combinatorial optimisation Learning rules Learning-classifier-systems Machine learning Optimal tours Optimisation Travelling salesman problems Traveling salesman problem Tabacman, M. Bacardit, J. Krasnogor, N. Loiseau, I. Learning classifier systems for optimisation problems: A case study on fractal travelling salesman problem |
topic_facet |
Learning-classifier-systems Machine learning Optimization Traveling-salesman-problem Classifiers Combinatorial optimization Education Fractals Learning systems Case studies Combinatorial optimisation Learning rules Learning-classifier-systems Machine learning Optimal tours Optimisation Travelling salesman problems Traveling salesman problem |
description |
This paper presents a set of experiments on the use of Learning Classifier Systems for the purpose of solving combinatorial optimisation problems. We demonstrate our approach with a set of Fractal Travelling Salesman Problem (TSP) instances for which it is possible to know by construction the optimal tour regardless of the number of cities in them. This special type of TSP instances are ideal for testing new methods as they are well characterised in their solvability and easy to use for scalability studies. Our results show that an LCS is capable of learning rules to recognise to which family of instances a set containing a sample of the cities in the problem belongs to and hence automatically select the best heuristic to solve it. Moreover, we have also trained the LCS to recognise links belonging to the optimal tour. Copyright 2008 ACM. |
format |
CONF |
author |
Tabacman, M. Bacardit, J. Krasnogor, N. Loiseau, I. |
author_facet |
Tabacman, M. Bacardit, J. Krasnogor, N. Loiseau, I. |
author_sort |
Tabacman, M. |
title |
Learning classifier systems for optimisation problems: A case study on fractal travelling salesman problem |
title_short |
Learning classifier systems for optimisation problems: A case study on fractal travelling salesman problem |
title_full |
Learning classifier systems for optimisation problems: A case study on fractal travelling salesman problem |
title_fullStr |
Learning classifier systems for optimisation problems: A case study on fractal travelling salesman problem |
title_full_unstemmed |
Learning classifier systems for optimisation problems: A case study on fractal travelling salesman problem |
title_sort |
learning classifier systems for optimisation problems: a case study on fractal travelling salesman problem |
url |
http://hdl.handle.net/20.500.12110/paper_97816055_v_n_p2039_Tabacman |
work_keys_str_mv |
AT tabacmanm learningclassifiersystemsforoptimisationproblemsacasestudyonfractaltravellingsalesmanproblem AT bacarditj learningclassifiersystemsforoptimisationproblemsacasestudyonfractaltravellingsalesmanproblem AT krasnogorn learningclassifiersystemsforoptimisationproblemsacasestudyonfractaltravellingsalesmanproblem AT loiseaui learningclassifiersystemsforoptimisationproblemsacasestudyonfractaltravellingsalesmanproblem |
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1782024948348354560 |