Abstract
Capturing the metaphor of evolutionary transitions in biological complexity, the Evolutionary Transition Algorithm (ETA) evolves solutions of increasing structural and functional complexity from the symbiotic interaction of partial ones. In this chapter we show that the ETA indeed captures this idea and we illustrate this on instances of the Binary Constraint Satisfaction problem. The results make the ETA a promising optimization approach that requires more extensive investigation from both a theoretical and optimization perspective. We analyze here, in depth, some of the design choices that are made for the algorithm. The analysis of these choices provides insight on the plasticity of the algorithm toward alternative choices and other kinds of problems.
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References
Defaweux, A., Lenaerts, T.: Evolutionary transitions in sequence complexity; a proof of concept. In: Proceedings of the Annual Machine Learning Conference of Belgium and The Nederlands, Brussels, Belgium, pp. 38–45 (2004)
Defaweux, A., Lenaerts, T., van Hemert, J.: Evolutionary transitions as a metaphor for evolutionary optimisation. In: Capcarrère, M.S., Freitas, A.A., Bentley, P.J., Johnson, C.G., Timmis, J. (eds.) ECAL 2005. LNCS, vol. 3630, pp. 342–352. Springer, Heidelberg (2005)
Defaweux, A., Lenaerts, T., van Hemert, J., Parent, J.: Complexity transitions in evolutionary algorithms: Evaluating the impact of the initial population. In: Proceedings of the IEEE Congress on Evolutionary Computation, Edinburgh, UK, pp. 2174–2181 (2005)
Defaweux, A., Lenaerts, T., van Hemert, J., Parent, J.: Transition models as an incremental approach for problem solving in evolutionary algorithms. In: Proceedings of the Genetic and Evolutionary Computation Conference, Washington DC, USA, pp. 599–607 (2005)
Goldberg, D.E., Deb, K., Clark, J.H.: Genetic algorithms, noise, and the sizing of populations. Complex Systems 6, 333–362 (1992)
Goldberg, D.E., Korb, B., Deb, K.: Messy genetic algorithms: motivation, analysis, and first results. Complex Systems 3, 493–530 (1989)
Goldberg, D.E., Korb, B., Deb, K.: Messy genetic algorithms revisited: Studies in mixed size and scale. Complex Systems 4, 415–444 (1990)
Golomb, S., Baumert, L.: Backtrack programming. Journal of the ACM 12, 516–524 (1965)
Greffenstette, J.: Credit assignment in rule discovery systems based on genetic algorithms. Machine Learning 3, 225–245 (1988)
Holmes, J., Lanzi, P.L., Stolzmann, W., Wilson, S.W.: Learning classifier systems: New models, successful applications. Information Processing Letters 82, 23–30 (2002)
Hoos, H.H., Stützle, T.: Stochastic Local Search: Foundations and Applications. Morgan-Kaufmann/ Elsevier (2005)
Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.): IWLCS 1999. LNCS, vol. 1813, p. 243. Springer, Heidelberg (2000)
Lenaerts, T.: Different Levels of Selection in Artificial Evolutionary Systems: Analysis and Simulation of Selection Dynamics. PhD thesis, Department of Computer Science, Vrije Universiteit Brussel, Belgium (2003)
Maynard Smith, J., Szathmáry, E.: The Major Transitions in Evolution. W.H. Freeman, San Francisco (1995)
Michod, R.: Darwinian Dynamics: Evolutionary transitions in Fitness and Individuality. Princeton University Press, Princeton (1999)
Potter, M.: The Design and Analysis of a Computational Model of Cooperative Coevolution. PhD thesis, Department of Computer Science, George Mason University, USA (1997)
Raynal, F., Collet, P., Lutton, E., Schoenauer, M.: Individual gp: an alternative viewpoint for the resolution of complex problems. In: Banzhaf, W., et al. (eds.) Proceeding of the Genetic and Evolutionary Computation Conference, Orlando, Florida, USA, pp. 974–981 (1999)
Raynal, F., Collet, P., Lutton, E., Schoenauer, M.: Polar ifs + parisian genetic programming = efficient ifs inverse problem solving. Genetic Programming and Evolvable Machines Journal 1(4), 339–361 (2000)
Rossi, F., Dhar, V.: On the equivalence of constraint satisfaction problems. In: Aiello, L.C. (ed.) Proceedings of the 9th European Conference on Artificial Intelligence, Stockholm, Sweden, pp. 550–556 (1990)
Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach. Prentice-Hall, Englewood Cliffs (1995)
Salido, M.A., Garrido, A., Barták, R.: Introduction: special issue on constraint satisfaction techniques for planning and scheduling problems. Engineering Applications of Artificial Intelligence 21(5), 679–682 (2008)
Simon, H.A.: The Sciences of the Artificial, 3rd edn. MIT Press, Cambridge (1969)
Thierens, D., de Jong, E.D., Watson, R.A.: On the complexity of hierarchical problem solving. In: Proceedings of The Genetic and Evolutionary Computation Conference, Washington DC, USA, pp. 1201–1208 (2005)
Tsang, E.P.K.: Foundations of Constraint Satisfaction. Academic Press, London (1993)
van Hemert, J.: Application of Evolutionary Computation to Constraints Satisfaction and Data Mining. PhD thesis, Universiteit Leiden, Netherlands (2002)
van Hemert, J.: RandomCSP Freely (2002), http://freshmeat.net/projects/randomcsp/
Watson, R.: Compositional Evolution: Interdisciplinary Investigations in Evolvability, Modularity, and Symbiosis. PhD thesis, Brandeis University, USA (2002)
Watson, R.A., Pollack, J.B.: Symbiotic combination as an alternative to sexual recombination in genetic algorithms. In: Deb, K., Rudolph, G., Lutton, E., Merelo, J.J., Schoenauer, M., Schwefel, H.-P., Yao, X. (eds.) PPSN 2000. LNCS, vol. 1917, pp. 425–434. Springer, Heidelberg (2000)
Watson, R.A., Pollack, J.B.: A computational model of symbiotic composition in evolutionary transitions. Special Issue on Evolvability Biosystems 69(2-3), 187–209 (2002)
Wiegand, P.: An Analysis of Cooperative Coevolutionary Algorithms. PhD thesis, George Mason University, USA (2004)
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Lenaerts, T., Defaweux, A., van Hemert, J. (2009). The Evolutionary Transition Algorithm: Evolving Complex Solutions Out of Simpler Ones. In: Chiong, R. (eds) Nature-Inspired Algorithms for Optimisation. Studies in Computational Intelligence, vol 193. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00267-0_4
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DOI: https://doi.org/10.1007/978-3-642-00267-0_4
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