Abstract
The use and benefits of self-adaptive mutation operators are well-known within evolutionary computing. In this paper we examine the use of self-adaptive mutation in Michigan-style Classifier Systems with the aim of improving their performance as controllers for autonomous mobile robots. Initially, we implement the operator in the ZCS classifier and examine its performance in two “animat” environments. It is shown that, although no significant increase in performance is seen over results presented in the literature using a fixed rate of mutation, the operator adapts to approximately this rate regardless of the initial range.
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References
Angeline, P.J., Fogel, D.B., Fogel, L.J. (1996) A Comparison of Self-Adaptation Methods for Finite State Machines in a Dynamic Environment. In L.J. Fogel, P.J. Angeline, & T. Bäck (eds.) Evolutionary Programming V, MIT Press, pp. 441–449.
Bäck, T. (1992) Self-Adaptation in Genetic Algorithms. In F.J. Varela & P. Bourgine (eds.) Toward a Practice of Autonomous Systems: Proceedings of the First European Conference on Artificial Life, MIT Press, pp 263–271.
Cliff, D. & Bullock, S. (1993) Adding ‘Foveal Vision’ to Wilson’s Animat. Adaptive Behavior 2(1):47–70.
Cliff, D. & Ross, S. (1995) Adding Temporary Memory to ZCS. Adaptive Behavior 3(2): 101–150.
Donnart, J-Y. & Meyer, J-A. (1994) Spatial Exploration, Map Learning, and Self-Positioning with MonaLysa. In P. Maes, M. Mataric, J-A. Meyer, J. Pollack & S.W. Wilson (eds.) From Animals to Animats 4: Proceedings of the Fourth International Conference on Simulation of Adaptive Behaviour, MIT Press, pp 204–213.
Fogel, D.B. (1992) Evolving Artificial Intelligence. PhD dissertation, University of California.
Holland, J.H. (1975) Adaptation in Natural and Artificial Systems. University of Michigan Press.
Holland, J.H., Holyoak, K.J., Nisbett, R.E. & Thagard, P.R. (1986) Induction: Processes of Inference, Learning and Discovery. MIT Press.
Koza, J.R. (1991) Genetic Programming. MIT Press.
Lanzi, P-L., Stolzmann, W. & Wilson, S.W. (eds.) (2000) Proceedings of the Second International Workshop on Learning Classifier Systems, Springer-Verlag.
Rechenberg, I. (1973) Evolutionsstrategie; Optimierung technischer Systeme nach Prinzipen der biologischen Evolution. Frommann-Holzboog Verlag.
Riolo, R. (1991) Lookahead Planning and Latent Learning in a Classifier System. In J-A. Meyer & S.W. Wilson (eds.) From Animals to Animats: Proceedings of the First International Conference on Simulation of Adaptive Behaviour, MIT Press, pp316–326.
Stolzmann, W. (1999) Latent Learning in Khepra Robots with Anticipatory Classifier Systems. In A.S. Wu (ed.) Proceedings of the 1999 Genetic and Evolutionary Computation Conference Workshop Program, Morgan Kauffman, pp290–297.
Tomlinson, A. & Bull, L. (1998) A Corporate Classifier System. In A.E. Eiben, T. Bäck, M. Schoenauer & H-P. Schwefel (eds.) Parallel Problem Solving from Nature-PPSN V, Springer, pp. 550–559.
Watkins, C. (1989) Learning from Delayed Rewards. PhD dissertation, University of Cambridge.
Wilson, S.W. (1985) Knowledge Growth in an Artificial Animal. In J.J. Grefenstette (ed.) Proceedings of the First International Conference on Genetic Algorithms and their Applications, Lawrence Erlbaum Associates, pp 16–23.
Wilson, S.W. (1987) Classifier Systems and the Animat Problem. Machine Learning 2:199–228.
Wilson, S.W. (1994) ZCS: A Zeroth-level Classifier System. Evolutionary Computation 2(1):1–18.
Wilson, S.W. (1995) Classifier Fitness Based on Accuracy. Evolutionary Computation 3(2):149–177.
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Bull, L., Hurst, J. (2000). Self-Adaptive Mutation in ZCS Controllers. In: Cagnoni, S. (eds) Real-World Applications of Evolutionary Computing. EvoWorkshops 2000. Lecture Notes in Computer Science, vol 1803. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45561-2_33
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DOI: https://doi.org/10.1007/3-540-45561-2_33
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