Abstract
We propose using the so called Royal Road functions as test functions for cooperative co-evolutionary algorithms (CCEAs). The Royal Road functions were created in the early 90’s with the aim of demonstrating the superiority of genetic algorithms over local search methods. Unexpectedly, the opposite was found to be true. The research deepened our understanding of the phenomenon of hitchhiking where unfavorable alleles may become established in the population following an early association with an instance of a highly fit schema. Here, we take advantage of the modular and hierarchical structure of the Royal Road functions to adapt them to a co-evolutionary setting. Using a multiple population approach, we show that a CCEA easily outperforms a standard genetic algorithm on the Royal Road functions, by naturally overcoming the hitchhiking effect. Moreover, we found that the optimal number of sub-populations for the CCEA is not the same as the number of components that the function can be linearly separated into, and propose an explanation for this behavior. We argue that this class of functions may serve in foundational studies of cooperative co-evolution.
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References
Collet, P., Lutton, E., Raynal, F., Schoenauer, M.: Polar IFS+parisian genetic programming=efficient IFS inverse problem solving. Genetic Programming and Evolvable Machines 1(4), 339–361 (2000)
Dunn, E., Olague, G., Lutton, E.: Parisian camera placement for vision metrology. Pattern Recognition Letters 27(11), 1209–1219 (2006)
Eriksson, R., Olsson, B.: Cooperative coevolution in inventory control optimisation. In: Proceedings of the Third International Conference on Artificial Neural Networks and Genetic Algorithms, Springer, Heidelberg (1997)
Forrest, S., Mitchell, M.: Relative building-block fitness and the building block hypothesis. In: Foundations of Genetic Algorithms, vol. 2, pp. 109–126. Morgan Kaufmann, San Mateo (1993)
Holland, J.H.: Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor, MI (1975)
Holland, J.H., Reitman, J.S.: Cognitive systems based on adaptive algorithms. In: Pattern-directed inference systems. Academic Press, New York (1978)
Husbands, P.: Distributed coevolutionary genetic algorithms for multi-criteria and multi-constraint optimisation. In: Fogarty, T.C. (ed.) AISB-WS 1994. LNCS, vol. 865, pp. 150–166. Springer, Heidelberg (1994)
Husbands, P., Mill, F.: Simulated co-evolution as the mechanism for emergent planning and scheduling. In: Proceedings of the Fourth International Conference on Genetic Algorithms, pp. 264–270. Morgan Kaufman, San Francisco (1991)
Kauffman, S.A., Johnsen, S.: Co-evolution to the edge of chaos: Coupled fitness landscapes, poised states, and co-evolutionary avalanches. In: Artificial Life II, pp. 325–369. Addison-Wesley, Reading (1992)
Landrin-Schweitzer, Y., Collet, P., Lutton, E.: Introducing lateral thinking in search engines. Genetic Programming and Evolvable Machines 7(1), 9–31 (2006)
Louchet, J., Guyon, M., Lesot, M.-J., Boumaza, A.M.: Dynamic flies: a new pattern recognition tool applied to stereo sequence processing. Pattern Recognition Letters 23(1-3) (2002)
Mitchell, M.: When will a genetic algorithm outperform hill-climbing? In: Proceedings of the Fifth International Conference on Genetic Algorithms, Morgan Kaufman, San Francisco (1993)
Mitchell, M., Forrest, S., Holland, J.H.: The royal road for genetic algorithms: Fitness landscapes and GA performance. In: Proc. of the First European Conference on Artificial Life, pp. 245–254. MIT Press, Cambridge (1992)
Moriarty, D.E., Miikkulainen, R.: Forming neural networks through efficient and adaptive coevolution. Evolutionary Computation 5(4), 373–399 (1998)
Popovici, E., De Jong, K.A.: Understanding cooperative co-evolutionary dynamics via simple fitness landscapes. In: Genetic and Evolutionary Computation Conference, GECCO 2005, pp. 507–514. ACM, New York (2005)
Mitchell, A., Potter, De Jong, K.: A cooperative coevolutionary approach to function optimization. In: Davidor, Y., Männer, R., Schwefel, H.-P. (eds.) PPSN 1994. LNCS, vol. 866, pp. 249–257. Springer, Heidelberg (1994)
De Jong, K.A., Potter, M.A.: The Coevolution of Antibodies for Concept Learning. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 530–539. Springer, Heidelberg (1998)
Potter, M.A., De Jong, K.A.: Cooperative coevolution: An architecture for evolving coadapted subcomponents. Evolutionary Computation 8(1), 1–29 (2000)
Reeves, C., Rowe, J.: Genetic algorithms: Principles and perspectives. Kluwer, Dordrecht (2002)
Watson, R.A., Pollack, J.B.: Hierarchically consistant test problems for genetic algorithms. In: 1999 Congress on Evolutionary Computation, pp. 1406–1413 (1999)
Wiegand, R.P.: An analysis of cooperative coevolutionary algorithms, Ph.D. thesis, George Mason University (2004)
Wiegand, R.P., Liles, W., De Jong, K.: Analyzing cooperative coevolution with evolutionary game theory. In: Proceedings of the 2002 Congress on Evolutionary Computation CEC2002, pp. 1600–1605 (2002)
Wiegand, R.P., Liles, W.C., De Jong, K.A.: An empirical analysis of collaboration methods in cooperative coevolutionary algorithms. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001), pp. 1235–1242. Morgan Kaufmann, San Francisco (2001)
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Ochoa, G., Lutton, E., Burke, E. (2008). The Cooperative Royal Road: Avoiding Hitchhiking . In: Monmarché, N., Talbi, EG., Collet, P., Schoenauer, M., Lutton, E. (eds) Artificial Evolution. EA 2007. Lecture Notes in Computer Science, vol 4926. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79305-2_16
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DOI: https://doi.org/10.1007/978-3-540-79305-2_16
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