Abstract
We define a set of measures that capture some different aspects of neutrality in evolutionary algorithms fitness landscapes from a qualitative point of view. If considered all together, these measures offer a rather complete picture of the characteristics of fitness landscapes bound to neutrality and may be used as broad indicators of problem hardness. We compare the results returned by these measures with the ones of negative slope coefficient, a quantitative measure of problem hardness that has been recently defined and with success rate statistics on a well known genetic programming benchmark: the multiplexer problem. In order to efficaciously study the search space, we use a sampling technique that has recently been introduced and we show its suitability on this problem.
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References
Altenberg, L.: The evolution of evolvability in genetic programming. In: Kinnear, K. (ed.) Advances in Genetic Programming, pp. 47–74. The MIT Press, Cambridge (1994)
Barnett, L.: Evolutionary Search on Fitness Landscapes with Neutral Networks. PhD thesis, University of Sussex (2003)
Collard, P., Clergue, M., Defoin-Platel, M.: Synthetic neutrality for artificial evolution. In: Artificial Evolution, pp. 254–265 (1999)
Collard, P., Verel, S., Clergue, M.: Local search heuristics: Fitness cloud versus fitness landscape. In: Mántaras, R.L.D., Saitta, L. (eds.) European Conference on Artificial Intelligence. ECAI04, Valence, Spain, pp. 973–974. IOS Press, Amsterdam (2004)
Collins, M.: Finding needles in haystacks is harder with neutrality. In: Beyer, H.-G., et al. (eds.) Proceedings of the 2005 conference on Genetic and evolutionary computation, vol. 2. GECCO 2005, Washington DC, USA, 25–29 June, pp. 1613–1618. ACM Press, New York (2005)
Geard, N.: A comparison of neutral landscapes – nk, nkp and nkq. In: Congress on Evolutionary Computation (CEC’02), Honolulu, Hawaii, IEEE Press, Piscataway (2002)
Huynen, M.: Exploring phenotype space through neutral evolution. J. Mol. Evol. 43, 165–169 (1996)
Koza, J.R.: Genetic Programming. The MIT Press, Cambridge (1992)
Langdon, W.B., Poli, R.: Foundations of Genetic Programming. Springer, Heidelberg (2002)
Madras, N.: Lectures on Monte Carlo Methods. American Mathematical Society, Providence (2002)
Reidys, C.M., Stadler, P.F.: Neutrality in fitness landscapes. Applied Mathematics and Computation 117(2–3), 321–350 (2001)
Schuster, P., Fontana, W., Stadler, P.F., Hofacker, I.L.: From sequences to shapes and back: a case study in RNA secondary structures. Proc. R. Soc. London B. 255, 279–284 (1994)
Stadler, P.F.: Fitness landscapes. In: Lässig, M., Valleriani (eds.) Biological Evolution and Statistical Physics. Lecture Notes Physics, vol. 585, pp. 187–207. Springer, Heidelberg (2002)
Toussaint, M., Igel, C.: Neutrality: A necessity for self-adaptation. In: Congress on Evolutionary Computation (CEC’02), Honolulu, Hawaii, pp. 1354–1359. IEEE Press, Piscataway (2002)
Vanneschi, L.: Theory and Practice for Efficient Genetic Programming. Ph.D. thesis, Faculty of Sciences, University of Lausanne, Switzerland (2004)
Vanneschi, L., et al.: A quantitative study of neutrality in GP boolean landscapes. In: Keijzer, M., et al. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference, vol. 1. GECCO’06, pp. 895–902. ACM Press, New York (2006)
Vanneschi, L., Tomassini, M., Collard, P., Clergue, M.: Fitness distance correlation in structural mutation genetic programming. In: Ryan, C., Soule, T., Keijzer, M., Tsang, E.P.K., Poli, R., Costa, E. (eds.) EuroGP 2003. LNCS, vol. 2610, pp. 455–464. Springer, Heidelberg (2003)
Vanneschi, L., Tomassini, M., Collard, P., Vérel, S.: Negative slope coefficient. In: Collet, P., Tomassini, M., Ebner, M., Gustafson, S., Ekárt, A. (eds.) EuroGP 2006. LNCS, vol. 3905, pp. 178–189. Springer, Heidelberg (2006)
Wright, S.: The roles of mutation, inbreeding, crossbreeding and selection in evolution. In: Jones, D.F. (ed.) Proceedings of the Sixth International Congress on Genetics, vol.1, pp. 356–366 (1932)
Yu, T.: “Six degrees of separation” in boolean function networks with neutrality. In: Poli, R., et al. (eds.) GECCO 2004 Workshop Proceedings, Seattle, Washington, USA, 26-30 June (2004)
Yu, T., Miller, J.: Neutrality and the evolvability of boolean function landscape. In: Miller, J., Tomassini, M., Lanzi, P.L., Ryan, C., Tetamanzi, A.G.B., Langdon, W.B. (eds.) EuroGP 2001. LNCS, vol. 2038, pp. 204–217. Springer, Heidelberg (2001)
Yu, T., Miller, J.F.: Finding needles in haystacks is not hard with neutrality. In: Foster, J.A., Lutton, E., Miller, J., Ryan, C., Tettamanzi, A.G.B. (eds.) EuroGP 2002. LNCS, vol. 2278, pp. 13–25. Springer, Heidelberg (2002)
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Vanneschi, L., Tomassini, M., Collard, P., Vérel, S., Pirola, Y., Mauri, G. (2007). A Comprehensive View of Fitness Landscapes with Neutrality and Fitness Clouds. In: Ebner, M., O’Neill, M., Ekárt, A., Vanneschi, L., Esparcia-Alcázar, A.I. (eds) Genetic Programming. EuroGP 2007. Lecture Notes in Computer Science, vol 4445. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71605-1_22
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DOI: https://doi.org/10.1007/978-3-540-71605-1_22
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