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Alba, E. (2005). Parallel Metaheuristics: A New Class of Algorithms, Wiley-Interscience, Hoboken, NJ.10.1002/0471739383Search in Google Scholar

Alba, E. and Luque, G. (2004). Growth curves and takeover time in evolutionary algorithms, in K. Deb (Ed.), GECCO 2004, Lecture Notes in Computer Science, Vol. 3102, Springer-Verlag, Berlin/Heidelberg, pp. 864-876.Search in Google Scholar

Cantu-Paz, E. (2000). Efficient and Accurate Parallel Genetic Algorithms, Kluwer Academic Publishers, Norwell, MA.10.1007/978-1-4615-4369-5Search in Google Scholar

Chakhlevitch, K. and Cowling, P. (2008). Hyperheuristics: Recent developments, in C. Cotta, M. Sevaux and K. Sörensen (Eds.), Adaptive and Multilevel Metaheuristics, Studies in Computational Intelligence, Vol. 136, Springer-Verlag, Berlin/Heidelberg, pp. 3-29.10.1007/978-3-540-79438-7_1Search in Google Scholar

Chen, X. and Ong, Y.-S. (2012). A conceptual modeling of meme complexes in stochastic search, IEEE Transactions on Systems, Man, and Cybernetics, Part C 42(5): 612-625.10.1109/TSMCC.2012.2188832Search in Google Scholar

Chen, X., 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): 591-607.10.1109/TEVC.2011.2132725Search in Google Scholar

Cowling, P., Kendall, G. and Soubeiga, E. (2008). A hyperheuristic approach to schedule a sales submit, in E. Burke and W. Erben (Eds.), PATAT 2000, Lecture Notes in Computer Science, Vol. 2079, Springer-Verlag, Berlin/Heidelberg, pp. 176-190.Search in Google Scholar

Dawkins, R. (1976). The Selfish Gene, Clarendon Press, Oxford.Search in Google Scholar

Deb, K. and Goldberg, D.E. (1993). Analyzing deception in trap functions, in L.D. Whitley (Ed.), Second Workshop on Foundations of Genetic Algorithms,Morgan Kaufmann, San Francisco, CA, pp. 93-108.10.1016/B978-0-08-094832-4.50012-XSearch in Google Scholar

Dorronsoro, B. and Alba, E. (2008). Cellular Genetic Algorithms, Operations Research/Computer Science Interfaces, Vol. 42, Springer, New York, NY.Search in Google Scholar

Giacobini, M., Alba, E. and Tomassini, M. (2003). Selection intensity in asynchronous cellular evolutionary algorithms, in E. Cantú-Paz et al. (Eds.), Genetic and Evolutionary Computation Conference, GECCO 2003, Lecture Notes in Computer Science, Vol. 2723, Springer-Verlag, Berlin/Heidelberg, pp. 955-966.10.1007/3-540-45105-6_107Search in Google Scholar

Giacobini, M., Tomassini, M., Tettamanzi, A. and Alba, E. (2005). Selection intensity in cellular evolutionary algorithms for regular lattices, IEEE Transactions on Evolutionary Computation 9(5): 489-505.10.1109/TEVC.2005.850298Search in Google Scholar

Goldberg, D.E., Deb, K. and Horn, J. (1992). Massive multimodality, deception, and genetic algorithms, Parallel Problem Solving from Nature, PPSN II, Elsevier, Brussels, pp. 37-48.Search in Google Scholar

Hart, W., Krasnogor, N. and Smith, J. (2005). Recent Advances in Memetic Algorithms, Studies in Fuzziness and Soft Computing, Vol. 166, Springer-Verlag, Berlin/Heidelberg, pp. 3-27.Search in Google Scholar

Hoos, H. and Stützle, T. (2004). Stochastic Local Search: Foundations & Applications, Morgan Kaufmann Publishers Inc., San Francisco, CA.Search in Google Scholar

Karcz-Dul˛eba, I. (2004). Time to the convergence of evolution in the space of population states, International Journal of Applied Mathematics and Computer Science 14(3): 279-287.Search in Google Scholar

Krasnogor, N. (2004). Self generating metaheuristics in bioinformatics: The proteins structure comparison case, Genetic Programming and Evolvable Machines 5(2): 181-201.10.1023/B:GENP.0000023687.41210.d7Search in Google Scholar

Krasnogor, N., Blackburne, B., Burke, E. and Hirst, J. (2002). Multimeme algorithms for protein structure prediction, in J. Merelo et al. (Eds.), Parallel Problem Solving From Nature VII, Lecture Notes in Computer Science, Vol. 2439, Springer, Berlin, pp. 769-778.10.1007/3-540-45712-7_74Search in Google Scholar

Krasnogor, N. and Gustafson, S. (2004). A study on the use of “self-generation” in memetic algorithms, Natural Computing 3(1): 53-76.10.1023/B:NACO.0000023419.83147.67Search in Google Scholar

Krasnogor, N. and Smith, J. (2005). A tutorial for competent memetic algorithms: Model, taxonomy, and design issues, IEEE Transactions on Evolutionary Computation 9(5): 474-488.10.1109/TEVC.2005.850260Search in Google Scholar

Moscato, P. (1989). On evolution, search, optimization, genetic algorithms and martial arts: Towards memetic algorithms, Caltech Concurrent Computation Program, Report 826, California Institute of Technology, Pasadena, CA.Search in Google Scholar

Moscato, P. (1999). Memetic algorithms: A short introduction, in D. Corne, M. Dorigo and F. Glover (Eds.), New Ideas in Optimization, McGraw-Hill, Maidenhead, pp. 219-234.Search in Google Scholar

Moscato, P. and Cotta, C. (2010). A modern introduction to memetic algorithms, in M. Gendreau and J.-Y. Potvin (Eds.), Handbook of Metaheuristics, International Series in Operations Research & Management Science, Vol. 146, Springer, New York, NY, pp. 141-183.10.1007/978-1-4419-1665-5_6Search in Google Scholar

Neri, F. and Cotta, C. (2012). Memetic algorithms and memetic computing optimization: A literature review, Swarm and Evolutionary Computation 2: 1-14.10.1016/j.swevo.2011.11.003Search in Google Scholar

Neri, F., Cotta, C. and Moscato, P. (2012). Handbook of Memetic Algorithms, Studies in Computational Intelligence, Vol. 379, Springer-Verlag, Berlin/Heidelberg.Search in Google Scholar

Neri, F., Tirronen, V., Kärkkäinen, T. and Rossi, T. (2007). Fitness diversity based adaptation in multimeme algorithms: A comparative study, IEEE Congress on Evolutionary Computation, CEC 2007, Singapore, pp. 2374-2381.Search in Google Scholar

Nogueras, R. and Cotta, C. (2013). Analyzing meme propagation in multimemetic algorithms: Initial investigations, Proceedings of the 2013 Federated Conference on Computer Science and Information Systems, Cracow, Poland, pp. 1013-1019.Search in Google Scholar

Norman, M. and Moscato, P. (1989). A competitive and cooperative approach to complex combinatorial search, Proceedings of the 20th Informatics and Operations Research Meeting, Buenos Aires, Argentina, pp. 3.15-3.29.Search in Google Scholar

Ong, Y.-S. and Keane, A. (2004). Meta-Lamarckian learning in memetic algorithms, IEEE Transactions on Evolutionary Computation 8(2): 99-110.10.1109/TEVC.2003.819944Search in Google Scholar

Ong, Y.-S., Lim, M.-H. and Chen, X. (2010). Memetic computation-past, present and future, IEEE Computational Intelligence Magazine 5(2): 24-31.10.1109/MCI.2010.936309Search in Google Scholar

Ong, Y.-S., Lim, M.-H., Zhu, N. and Wong, K.-W. (2006). Classification of adaptive memetic algorithms: A comparative study, IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 36(1): 141-152.10.1109/TSMCB.2005.856143Search in Google Scholar

Rudolph, G. and Sprave, J. (1995). A cellular genetic algorithm with self-adjusting acceptance threshold, 1st IEE/IEEE International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, London, UK, pp. 365-372.Search in Google Scholar

Sarma, J. and De Jong, K. (1997). An analysis of local selection algorithms in a spatially structured evolutionary algorithm, in T. Bäck (Ed.), 7th International Conference on Genetic Algorithms, Morgan Kaufmann, San Francisco, CA, pp. 181-186.Search in Google Scholar

Schaefer, R., Byrski, A. and Smołka, M. (2012). The island model as a Markov dynamic system, International Journal of Applied Mathematics and Computer Science 22(4): 971-984, DOI: 10.2478/v10006-012-0072-z.10.2478/v10006-012-0072-zSearch in Google Scholar

Schönfisch, B. and de Roos, A. (1999). Synchronous and asynchronous updating in cellular automata, BioSystems 51(3): 123-143.10.1016/S0303-2647(99)00025-8Search in Google Scholar

Smith, J.E. (2007). Coevolving memetic algorithms: A review and progress report, IEEE Transactions on Systems, Man, and Cybernetics, Part B 37(1): 6-17.10.1109/TSMCB.2006.88327317278554Search in Google Scholar

Smith, J.E. (2008). Self-adaptation in evolutionary algorithms for combinatorial optimisation, in C. Cotta, M. Sevaux and K. Sörensen (Eds.), Adaptive and Multilevel Metaheuristics, Studies in Computational Intelligence, Vol. 136, Springer, Berlin/Heidelberg, pp. 31-57.10.1007/978-3-540-79438-7_2Search in Google Scholar

Smith, J.E. (2012). Self-adaptative and coevolving memetic algorithms, in F. Neri, C. Cotta and P. Moscato (Eds.), Handbook of Memetic Algorithms, Studies in Computational Intelligence, Vol. 379, Springer-Verlag, Berlin/Heidelberg, pp. 167-188.10.1007/978-3-642-23247-3_11Search in Google Scholar

Tomassini, M. (2005). Spatially Structured Evolutionary Algorithms, Natural Computing Series, Springer-Verlag, Berlin/Heidelberg.Search in Google Scholar

Watson, R.A., Hornby, G.S. and Pollack, J.B. (1998). Modeling building-block interdependency, in A. Eiben, T. Back, M. Schoenauer and H.-P. Schwefel (Eds.), Parallel Problem Solving from Nature, PPSN V, Lecture Notes in Computer Science, Vol. 1498, Springer-Verlag, Berlin/Heidelberg, pp. 97-106.10.1007/BFb0056853Search in Google Scholar

Wilcoxon, F. (1945). Individual comparisons by ranking methods, Biometrics 1(6): 80-83. 10.2307/3001968Search in Google Scholar

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Mathematics, Applied Mathematics