A learning system based on genetic adaptive algorithms
A learning system based on genetic adaptive algorithms
Chaos and Fractals
Analysis of the initialization stage of a Pittsburgh approach learning classifier system
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Prediction of topological contacts in proteins using learning classifier systems
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Evolutionary and Metaheuristics based Data Mining (EMBDM); Guest Editors: José A. Gámez, María J. del Jesús, José M. Puerta
Empirical Evaluation of Ensemble Techniques for a Pittsburgh Learning Classifier System
Learning Classifier Systems
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
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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.