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Among the most promising and active research areas in heuristic optimisation is the field of adaptive memetic algorithms (AMAs). These gain much of their reported robustness by adapting the probability with which each of a set of local improvement operators is applied, according to an estimate of their current value to the search process. This paper addresses the issue of how the current value should be estimated. Assuming the estimate occurs over several applications of a meme, we consider whether the extreme or mean improvements should be used, and whether this aggregation should be global, or local to some part of the solution space. To investigate these issues, we use the well-established COMA framework that coevolves the specification of a population of memes (representing different local search algorithms) alongside a population of candidate solutions to the problem at hand. Two very different memetic algorithms are considered: the first using adaptive operator pursuit to adjust the probabilities of applying a fixed set of memes, and a second which applies genetic operators to dynamically adapt and create memes and their functional definitions. For the latter, especially on combinatorial problems, credit assignment mechanisms based on historical records, or on notions of landscape locality, will have limited application, and it is necessary to estimate the value of a meme via some form of sampling. The results on a set of binary encoded combinatorial problems show that both methods are very effective, and that for some problems it is necessary to use thousands of variables in order to tease apart the differences between different reward schemes. However, for both memetic algorithms, a significant pattern emerges that reward based on mean improvement is better than that based on extreme improvement. This contradicts recent findings from adapting the parameters of operators involved in global evolutionary search. The results also show that local reward schemes outperform global reward schemes in combinatorial spaces, unlike in continuous spaces. An analysis of evolving meme behaviour is used to explain these findings.
Ba¨ck, T. (1992). Self adaptation in genetic algorithms. In F. Varela and P. Bourgine (Eds.), Toward a Practice of Autonomous Systems: Proceedings of the First European Conference on Artificial Life, pp. 263-271.
Ba¨ck, T., Fogel, D., and Michalewicz, Z. (Eds.). (1997). Handbook of evolutionary computation. Bristol, UK: Institute of Physics Publishing.
Barkat Ullah, A. S. S. M., Sarker, R., Cornforth, D., and Lokan, C. (2009). AMA: A new approach for solving constrained real-valued optimization problems. Soft Computing, 13(8-9):741-762.
Bull, L. (1995). Artificial symbiology. PhD thesis, University of the West of England.
Bull, L. (1997). Evolutionary computing in multi agent environments: Partners. In T. Ba¨ck (Ed.), Proceedings of the 7th International Conference on Genetic Algorithms, pp. 370-377.
Bull, L., and Fogarty, T. (1997). Horizontal gene transfer in endosymbiosis. In Proceedings of the 5th International Workshop on Artificial Life : Synthesis and Simulation of Living Systems (ALIFE-96), pp. 77-84.
Burke, E., and Smith, A. (2000). Hybrid evolutionary techniques for the maintenance scheduling problem. IEEE Transactions on Power Systems, 15(1):122-128
Burke, E., Kendall, G., and Soubeiga, E. (2003). A TABU search hyperheuristic for timetabling and rostering. Journal of Heuristics, 9(6):451-470.
Burke, E. K., Curtois, T., Hyde, M. R., Kendall, G., Ochoa, G., Petrovic, S., Rodr´ıguez, J. A. V., and Gendreau, M. (2010). Iterated local search vs. hyper-heuristics: Towards general-purpose search algorithms. In Proceedings of the IEEE Congress on Evolutionary Computation, pp. 1-8.
Caponio, A., Cascella, G., Neri, F., Salvatore, N., and Sumner, M. (2007). A fast adaptive memetic algorithm for online and offline control design of PMSM drives. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 37(1):28-41.
CEC-2003. (2003). Proceedings of the 2003 Congress on Evolutionary Computation (CEC 2003). Piscataway, NJ: IEEE Press.
Chen, X. (2010). An algorithm development environment for problem-solving. In Proceedings of the 2010 International Conference on Computational Problem-Solving (ICCP), pp. 85-90.
Chen, X. S., Ong, Y. S., Lim, M. H., and Tan, K. C. (2011). A multi-facet survey on memetic computation. IEEE Transactions on Evolutionary Computation, 15(5):591-607.
Cowling, P., Kendall, G., and Soubeiga, E. (2001). A hyperheuristic approach to scheduling a sales summit. In Practice and theory of automated timetabling. Lecture Notes in Computer Science, Vol. 2079 (pp. 176-190). Berlin: Springer.
Eiben, A., and van Hemert, J. (1999). SAW-ing EAs: Adapting the fitness function for solving constrained problems. In D. Corne, M. Dorigo, and F. Glover (Eds.), New ideas in optimization (Chap. 26, pp. 389-402). New York: McGraw-Hill.
Eiben, A., Hinterding, R., and Michalewicz, Z. (1999). Parameter control in evolutionary algorithms. IEEE Transactions on Evolutionary Computation, 3(2):124-141.
Fogel, D. (1992). Evolving artificial intelligence. PhD thesis, University of California at San Diego.
Fukunaga, A. (2008). Automated discovery of local search heuristics for satisfiability testing. Evolutionary Computation, 16(1):31-61.
Kendall, G., Cowling, P., and Soubeiga, E. (2002). Choice function and random hyperheuristics. In Proceedings of the Fourth Asia-Pacific Conference on Simulated Evolution and Learning (SEAL), pp. 667-671.
Krasnogor, N. (1999). Coevolution of genes and memes in memetic algorithms. In A. Wu (Ed.), Proceedings of the 1999 Genetic and Evolutionary Computation Conference Workshop Program.
Krasnogor, N. (2002). Studies in the theory and design space of memetic algorithms. PhD thesis, University of the West of England.
Krasnogor, N. (2004). Self-generating metaheuristics in bioinformatics: The protein structure comparison case. Genetic Programming and Evolvable Machines, 5(2):181-201.
Krasnogor, N., and Gustafson, S. (2004). A study on the use of “self-generation” in memetic algorithms. Natural Computing, 3(1):53-76.
Krasnogor, N., Blackburne, B., Burke, E., and Hirst, J. (2002). Multimeme algorithms for protein structure prediction. In J. M. Guervos, P. Adamidis, J. L. Fernandez-Villacanas, H. P. Schwefel Maturana, J., Fialho, A´ ., Saubion, F., Schoenauer, M., and Sebag, M. (2009). Extreme compass and dynamic multi-armed bandits for adaptive operator selection. In IEEE Congress on Evolutionary Computation, Trondheim, Norway.
Meuth, R., Lim, M., Ong, Y., and Wunsch, D. (2009). A proposition on memes and meta-memes in computing for higher-order learning. Memetic Computing, 1(2):85-100.
Moscato, P. (1989). On evolution, search, optimization, genetic algorithms and martial arts: Towards memetic algorithms. Caltech Concurrent Computation Program Report 826. Pasadena, CA: Caltech.
Neri, F. (2007a). An adaptive multimeme algorithm for designing HIV multidrug therapies. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 4(2):264-278.
Neri, F. (2007b). Fitness diversity based adaptation in multimeme algorithms: A comparative study. In IEEE Congress on Evolutionary Computation, CEC 2007, pp. 2374-2381.
Nguyen, H. Q., Ong, Y. S., Lim, M. H., and Krasnogor, N. (2009a). Adaptive cellular memetic algorithms. Evolutionary Computation, 17(2):231-256.
Nguyen, Q. H., Ong, Y. S., and Lim, M. H. (2009b). A probabilistic memetic framework. IEEE Transactions on Evolutionary Computation, 13(3):604-623.
Ong, Y., and Keane, A. (2004). Meta-Lamarckian learning in memetic algorithms. IEEE Transactions on Evolutionary Computation, 8(2):99-110.
Ong Y., Lim, M., Zhu, N., and Wong, K. (2006). Classification of adaptive memetic algorithms: A comparative study. IEEE Transactions on Systems, Man and Cybernetics, Part B, 36(1):141-152.
Ong, Y. S., Lim, M. H., and Chen, X. (2010). Memetic computation-Past, present & future [Research Frontier]. Computational Intelligence Magazine, 5(2):24-31.
Ostermeier, A., Gawelczyk, A., and Hansen, N. (1994). A derandomized approach to selfadaptation of evolution strategies. Evolutionary Computation, 2(4):369-380.
Parker, G., and Blumenthal, H. (2004). Varying sample sizes for the co-evolution of heterogeneous agents. In Proceedings of the Congress on Evolutionary Computation (CEC 2004), pp. 766-771.
SATLIB. (2010). Online repository of satisfiability problems. Available at http://www.satlib.org
Schaffer, J., and Morishima, A. (1987). An adaptive crossover distribution mechanism for genetic algorithms. In J. Grefenstette (Ed.), Proceedings of the 2nd International Conference on Genetic Algorithms and Their Applications, pp. 36-40.
Schwefel, H. P. (1981). Numerical optimisation of computer models. New York: Wiley.
Smith, J. (2002a). Co-evolution of memetic algorithms: Initial investigations. In J. M. Guervos, P. Adamis, J. L. Fernandez-Villacanas, and H. P. Schwefel (Eds.), Proceedings of the 7th Conference on Parallel Problem Solving from Nature. Lecture Notes in Computer Science, Vol. 2439, pp. 537- 548.
Smith, J. (2003b). Parameter perturbation mechanisms in binary coded gas with self-adaptive mutation. In Proceedings of Foundations of Genetic Algorithms, 7, pp. 329-346.
Smith, J. (2004). The co-evolution of memetic algorithms for protein structure prediction. In W. Hart, N. Krasnogor, and J. Smith (Eds.), Recent advances in memetic algorithms (pp. 105- 128). Berlin: Springer .
Smith, J. (2007b). Credit assignment in adaptive memetic algorithms. In Proceedings of GECCO, the ACM-SIGEVO Conference on Evolutionary Computation, pp. 1412-1419.
Smith, J. (2010). Meme fitness and memepool sizes in coevolutionary memetic algorithms. Proceedings of the IEEE Congress on Evolutionary Computation, pp. 1-8.
Smith, J., and Fogarty, T. (1996a). Adaptively parameterised evolutionary systems: Self adaptive recombination and mutation in a genetic algorithm. In H. M. Voigt, W. Ebeling, I. Rechenberg, and H. P. Schwefel (Eds.), Proceedings of the 4th Conference on Parallel Problem Solving from Nature. Lecture Notes in Computer Science, Vol. 1141. (pp. 441-450). Berlin: Springer.
Thierens, D. (2007). Adaptive strategies for operator allocation. In F. Lobo, C. Lima, and Z. Michalewicz (Eds.), Parameter setting in evolutionary algorithms, pp. 77-90.
Ting, C., Zeng, W., and Lin, T. (2010). Linkage discovery through data mining. Computational Intelligence Magazine, 5:10-13.
Wiegand, R., Liles, W., and Jong, K. D. (2001). An empirical analysis of collaboration methods in cooperative coevolutionary algorithms. In L. Spector, E. Goodman, A. Wu, W. Langdon, H. M. Voight, M. Gen, S. Sen, M. Dorigo, S. Pezeshk, M. Garzon, and E. Burke (Eds.), Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2011), pp. 1235- 1245.