The island model as a Markov dynamic system
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|Dec 2012References
- Alba, E. and Tomassini, M. (2002). Parallelism and evolutionary algorithms,(5): 443-462.
- Aparicio, J., Correia, L. and Moura-Pires, F. (1999). Populations are multisets-plato,W. Banzhaf, J. Daida, A.E. Eiben, M.H. Garzon, V. Honavar, M. Jakiela and R.E. Smith (Eds.),, Vol. 2, Morgan Kaufmann, San Francisco, CA, pp. 1845-1850.
- Bäck, T., Fogel, D. and Michalewicz, Z. (2000)., Vols. 1 and 2, Institute of Physics Publishing, Bristol/Philadelphia, PA .
- Back, T., Hammel, U. and Schwefel, H.-P. (1997). Evolutionary computation: Comments on the history and current state,(1): 3-17.
- Billingsley, P. (1995)., Wiley-Interscience, Hoboken, NJ.
- Brabazon, A. and O’Neill, M. (2006)., Springer Verlag, Berlin/Heidelberg.
- Buckley, F., Nicol, S. and Pollett, P. (2010). Preface to the selected papers on modeling and control of metapopulation networks,(21): 2512-2514.
- Byrski, A. and Schaefer, R. (2009). Stochastic model of evolutionary and immunological multi-agent systems: Mutually exclusive actions,(2-3): 263-285.
- Cantú-Paz, E. (1995). A summary of research on parallel genetic algorithms,University of Illinois, Chicago, IL.
- Cantú-Paz, E. (2000)., Kluwer Academic Publishers, Norwell, MA.
- Davis, T.E. and Principe, J.C. (1991). A simulated annealing like convergence theory for the simple genetic algorithm,, pp. 174-181.
- Diekert, V. and Rozenberg, G. (1995)., World Scientific, Singapore.
- Droste, S., Jansen, T. and Wegener, I. (1998a). On the optimization of unimodal functions with the (1+1) evolutionary algorithm,, pp. 13-22.
- Droste, S., Jansen, T. and Wegener, I. (1998b). A rigorous complexity analysis of the (1+1) evolutionary algorithm for separable functions with Boolean inputs,(2): 185-196.
- Gajda, E., Schaefer, R. and Smołka, M. (2010). Evolutionary multiobjective optimization algorithm as a Markov system,, pp. 617-626.
- Goldberg, D.E. and Segrest, P. (1987). Finite Markov chain analysis of genetic algorithms,, pp. 1-8.
- Gordon, V., Whitley, D. and Bohn, A. (1992). Data flow parallelism in genetic algorithms,R. Manner and B. Manderick (Eds.),, Elsevier Science, Amsterdam, pp. 553-542.
- Grochowski, M., Schaefer, R. and Uhruski, P. (2004). Diffusion based scheduling in the agent-oriented computing systems,R. Wyrzykowski, J. Dongarra, M. Paprzycki and J. Wa s´ niewski (Eds.),, Lecture Notes in Computer Science, Vol. 3019, Springer, Berlin/Heidelberg, pp. 97-104.
- Harik, G., Cantú-Paz, E., Goldberg, D.E. and Miller, B.L. (1999). The gambler’s ruin problem, genetic algorithms, and the sizing of populations,(3): 251-253.
- Hennessy, M. (1988)., The MIT Press, Cambridge, MA.
- Hewitt, C., Bishop, P. and Steiger, R. (1973). A universal modular ACTOR formalism for artificial intelligence,, pp. 235-245.
- Horn, J. (1993). Finite Markov chain analysis of genetic algorithms with niching,, pp. 110-117.
- Horst, R. and Pardalos, P. (1995)., Kluwer, Norwell, MA.
- Iosifescu, M. (1980)., John Wiley & Sons, Alphen aan den Rijn.
- Kołodziej, J. and Xhafa, F. (2011). Modern approaches to modeling user requirements on resource and task allocation in hierarchical computational grids,(2) 243-257, DOI: 10.2478/v10006-011-0018-x.
- Kowalczuk, Z. and Białaszewski, T. (2006). Niching mechanisms in evolutionary computations,(1): 59-84.
- Kushner, H. (1971).Rinehart and Winston, Holt.
- Lässig, J. and Sudholt, D. (2010). General scheme for analyzing running times of parallel evolutionary algorithms,R. Schaefer, C. Cotta, J. Kołodziej and G. Rudolph (Eds.),Springer-Verlag, pp. 234-243.
- Li, C. and Yang, S. (2008). An island based hybrid evolutionary algorithm for optimization,X. Li, M. Kirley, M. Zhang, D.G. Green, V. Ciesielski, H.A. Abbass, Z. Michalewicz, T. Hendtlass, K. Deb, K.C. Tan, J. Branke and Y. Shi (Eds.),Lecture Notes in Computer Science, Vol. 5361, Springer, Berlin/Heidelberg, pp. 180-189.
- Liekens, A. (2005).Ph.D. thesis, Technische Universiteit Eindhoven, Eindhoven.
- Mahfoud, S. (1991). Finite Markov chain models of an alternative selection strategy for the genetic algorithm,(2): 155-170.
- Manderick, B. and Spiessens, P. (1989). Fine-grained parallel genetic algorithms,J. Schaffer (Ed.),Morgan Kauffman, San Francisco, CA, p. 428.
- Mesghouni, K., Hammadi, S. and Borne, P. (2004). Evolutionary algorithms for job-shop scheduling,(1): 91-103.
- Milner, R. (1990). Functions as processes,M. Paterson (Ed.),Lecture Notes in Computer Science, Vol. 443, Springer, Berlin/Heidelberg, pp. 167-180.
- Mühlenbein, H. (1989). Parallel genetic algorithms, population genetic and combinatorial optimization,J. Schaffer, (Ed.),Morgan Kauffman, San Francisco, CA, pp. 416-421.
- Mühlenbein, H. (1992). How genetic algorithms really work: Mutation and hillclimbing,R. Ma¨nner and B. Manderick (Eds.),Elsevier, Amsterdam, pp. 15-26.
- Nagylaki, T. (1979). The island model with stochastic migration,(1): 163-76.
- Nix, A.E. and Vose, M.D. (1992). Modeling genetic algorithms with Markov chains,(1): 79-88.
- Paredis, J. (1998). Coevolutionary algorithms,T. Back, D. Fogel and Z. Michalewicz (Eds.),1st Suppl., IOP Publishing/Oxford University Press, Bristol/Oxford.
- Peterson, J.L. (1981).Prentice Hall, Upper Saddle River, NJ.
- Potter, M.A. and De Jong, K.A. (2000). Cooperative coevolution: An architecture for evolving coadapted subcomponents,(1): 1-29.
- Rinnoy Kan, A. and Timmer, G. (1987). Stochastic global optimization methods,: 27-56.
- Rudolph, G. (1994). Massively parallel simulated annealing and its relation to evolutionary algorithms,(4): 361-383.
- Rudolph, G. (1997). Stochastic processes (Chapter B.2.2), Models of stochastic convergence (Chapter B.2.3),T. Ba¨ck, D.B. Fogel and Z. Michalewicz (Eds.),, Oxford University Press, Oxford.
- Rudolph, G. (2006). Takeover time in parallel populations with migration,, pp. 63-72.
- Schaefer, R., Byrski, A., Kołodziej, J. and Smołka, M. (2012). An agent-based model of hierarchic genetic search,DOI: 10.1016/j.camwa.2012.02.052, (accepted).
- Schaefer, R., Byrski, A. and Smołka, M. (2009). Stochastic model of evolutionary and immunological multi-agent systems: Parallel execution of local actions,(2-3): 325-348.
- Schaefer, R. and Telega, H. (2007)., Studies in Computational Intelligence, Vol. 74, Springer Verlag, Berlin/Heidelberg/New York, NY.
- Schmitt, L.M. (2001). Theory of genetic algorithm,(1): 1-61.
- Skolicki, Z. (2007)., Ph.D. thesis, George Mason University, Fairfax, VA.
- Skolicki, Z. and de Jong, K. (2004). Improving evolutionary algorithms with multi-representation island models,, pp. 420-429.
- Suzuki, J. (1993). A Markov Chain Analysis on a Genetic Algorithm,S. Forrest (Ed.),, Morgan Kaufmann, San Francisco, CA, pp. 146-154.
- Terzo, O. Mossucca, L., Cucca, M. and Notarpietro R. (2011). Data intensive scientific analysis with grid computing,(2): 219-228, DOI: 10.2478/v10006-011-0016-z.
- Tomassini, M. (2005)., Natural Computing Series, Springer, Berlin/Heidelberg.
- Vose, M. (1998)., MIT Press, Cambridge, MA.
- Vose, M. and Liepins, G. (1991). Punctuated equilibria in genetic search,: 31-44.
- Whitley, D. (1992). An executable model of a simple genetic algorithm,L.D. Whitley (Ed.),, Morgan Kaufmann, San Francisco, CA, pp. 45-62.
- Whitley, W.D., Rana, S.B. and Heckendorn, R.B. (1997). Island model genetic algorithms and linearly separable problems,D. Corne and J.L. Shapiro (Eds.),, Springer-Verlag, London, pp. 109-125.
- Wolpert, D.H. and Macready, W.G. (1997). No free lunch theorems for optimization,(1): 67-82.
- Wood, G.R. and Zabinsky, Z.B. (2002). Stochastic adaptive search,P.M. Pardalos and H.E. Romeijn (Eds.),Vol. 2, Kluwer, Norwell, MA.
Language: English
Page range: 971 - 984
Published on: Dec 28, 2012
Published by: University of Zielona Góra
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© 2012 Robert Schaefer, Aleksander Byrski, Maciej Smołka, published by University of Zielona Góra
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