[Alba, E. (2005). Parallel Metaheuristics: A New Class of Algorithms, Wiley-Interscience, Hoboken, NJ.10.1002/0471739383]Search 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.10.1007/978-1-4615-4369-5]Search 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_1]Search 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.2012.2188832]Search 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.2011.2132725]Search 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-X]Search 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_107]Search 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.2005.850298]Search 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.d7]Search 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_74]Search 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.0000023419.83147.67]Search 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.2005.850260]Search 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_6]Search 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.2011.11.003]Search 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.2003.819944]Search 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.2010.936309]Search 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.2005.856143]Search 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-z]Search 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)00025-8]Search 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.2006.88327317278554]Search 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_2]Search 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_11]Search 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/BFb0056853]Search in Google Scholar
[Wilcoxon, F. (1945). Individual comparisons by ranking methods, Biometrics 1(6): 80-83. 10.2307/3001968]Search in Google Scholar