dc.description.references | Alba, E. and Dorronsoro, B. (2005). The exploration/exploitation tradeoff in dynamic cellular genetic algorithms, IEEE Transactions on Evolutionary Computation 9(2): 126–142.
Ashlock, D., Smucker, M. and Walker, J. (1999). Graph based genetic algorithms, in P. J. Angeline, Z. Michalewicz, M. Schoenauer, X. Yao and A. Zalzala (eds), Proceedings of the 1999 IEEE Congress on Evolutionary Computation, Vol. 2, IEEE Press, pp. 1362–1368.
Asoh, H. and Mühlenbein, H. (1994). On the mean convergence time of evolutionary algorithms without selection and mutation, in Y. Davidor, H.-P. Schwefel and R. Männer (eds), Parallel Problem Solving from Nature – PPSN III, Vol. 866 of Lecture Notes in Computer Science, Springer-, pp. 88–97.
Baker, J. E. (1987). Reducing bias and inefﬁciency in the selection algorithm, in J. J. Grefenstette (ed.), Proceedings of the Second International Conference on Genetic Algorithms (ICGA’87), Lawrence Erlbaum Associates, pp. 14–21.
Beasley, D., Bull, D. R. and Martin, R. R. (1993). A sequential niche technique for multimodal function optimization, Evolutionary Computation 1(2): 101–125.
Berg, H. C. (1983). Random Walks in Biology, Princeton University Press.
Blickle, T. and Thiele, L. (1996). A comparison of selection schemes used in evolutionary algorithms, Evolutionary Computation 4(4): 361–394.
Bryden, K., Ashlock, D. and McCorkle, D. (2004). An application of graph based evolutionary algorithms for diversity preservation, in G. Greenwood (ed.), Proceedings of the 2004 IEEE Congress on Evolutionary Computation, IEEE Press, pp. 419–426.
Bryden, K. M., Ashlock, D. A., Corns, S. M. and Willson, S. J. (2006). Graph-based evolutionary algorithms, IEEE Transactions on Evolutionary Computation 10(5): 550–567.
Bui, L. T. (2007). The Role of Communication Messages and Explicit Niching in Distributed Evolutionary Multi-Objective Optimization, PhD thesis, Autstralian Defense Force Academy, University of New South Wales.
Cantú-Paz, E. (2001). Efﬁcient and Accurate Parallel Genetic Algorithms, Kluwer Academic Publishers.
Cavicchio, D. J. (1970). Adaptive Search using Simulated Evolution, PhD thesis, University of Michigan. (University Microﬁlms No. 25-0199).
Collins, R. J. and Jefferson, D. R. (1991). Selection in massively parallel genetic algorithms, in R. K. Belew and L. B. Booker (eds), Proceedings of the Fourth International Conference on Genetic Algorithms (ICGA’91), Morgan Kaufmann, pp. 249–256.
Crow, J. F. and Kimura, M. (1970). Introduction to Population Genetics Theory, Burgess.
Darwen, P. J. and Yao, X. (1995). A dilemma for ﬁtness sharing with a scaling function, in D. B. Fogel (ed.), Proceedings of the Second IEEE International Conference on Evolutionary Computation, IEEE Press, pp. 166–171.
Davidor, Y. (1991). A naturally occuring niche & species phenomenon: The model and ﬁrst results, in R. K. Belew and L. B. Booker (eds), Proceedings of the Fourth International Conference on Genetic Algorithms (ICGA’91), Morgan Kaufmann, pp. 257–263.
Davidor, Y., Yamada, T. and Nakano, R. (1993). The ECOlogical framework II: Improving GA performance at virtually zero cost, in S. Forrest (ed.), Proceedings of the Fifth International Conference on Genetic Algorithms (ICGA’93), Morgan Kaufmann, pp. 171–176.
De Jong, K. A. (1975). An Analysis of the Behavior of a Class of Genetic Adaptive Systems, PhD thesis, University of Michigan. (University Microﬁlms No. 76-9381).
De Jong, K. and Sarma, J. (1995). On decentralizing selection algorithms, in L. J. Eshelman (ed.), Proceedings of the Sixth International Conference on Genetic Algorithms (ICGA’95), Morgan Kaufmann, pp. 17–23.
Deb, K. (2001). Multi-Objective Optimization using Evolutionary Algorithms, John Wiley & Sons.
Deb, K. and Goldberg, D. E. (1989). An investigation of niche and species formation in genetic function optimization, in J. D. Schaffer (ed.), Proceedings of the Third International Conference on Genetic Algorithms (ICGA’89), Morgan Kaufmann, pp. 42–50.
Dick, G. (2003a). An explicit spatial model for niching in genetic algorithms, in P. Whigham (ed.), The 15th Annual Colloquium of the Spatial Information Research Centre, pp. 151–157.
Dick, G. (2003b). The spatially-dispersed genetic algorithm, in E. Cantú-Paz, J. A. Foster, K. Deb, D. Davis, R. Roy, U.-M. O’Reilly, H.-G. Beyer, R. Standish, G. Kendall, S. Wilson, M. Harman, J. Wegener, D. Dasgupta, M. A. Potter, A. C. Schultz, K. Dowsland, N. Jonoska and J. Miller (eds), Proceedings of the 2003 Conference on Genetic and Evolutionary Computation (GECCO 2003), Part II, Vol. 2724 of Lecture Notes in Computer Science, Springer, pp. 1572–1573.
Dick, G. (2003c). The spatially-dispersed genetic algorithm: An explicit spatial population structure for GAs, in R. Sarker, R. Reynolds, H. Abbass, K. C. Tan, B. McKay, D. Essam and T. Gedeon (eds), Proceedings of the 2003 IEEE Congress on Evolutionary Computation, IEEE Press, pp. 2455–2461.
Dick, G. (2004). An empirical investigation into correlation functions in a spatially-dispersed evolutionary algorithm, in P. Whigham (ed.), The 16th Annual Colloquium of the Spatial Information Research Centre, pp. 23–34.
Dick, G. and Whigham, P. A. (2002). Spatially-constrained selection in evolutionary computation, in R. Sarker, R. McKay, M. Gen and A. Namatame (eds), The Sixth Australia-Japan Joint Workshop on Intelligent and Evolutionary Systems, pp. 93–100.
Doebeli, M. and Dieckmann, U. (2003). Speciation along environmental gradients, Nature 421(6920): 259–264.
Dongarra, J. (1994). MPI: A message passing interface standard, International Journal of Supercomputer Applications 8(3/4): 159–416.
Ewens, W. J. (1963). The mean time for absorption in a process of genetic type, Journal of the Australian Mathematical Society 3: 375–383.
Giacobini, M., Alba, E., Tettamanzi, A. and Tomassini, M. (2004). Modeling selection intensity for toroidal cellular evolutionary algorithms, in K. Deb, R. Poli, W. Banzhaf, H.-G. Beyer, E. Burke, P. Darwen, D. Dasgupta, D. Floreano, J. Foster, M. Harman, O. Holland, P. L. Lanzi, L. Spector, A. Tettamanzi, D. Thierens and A. Tyrrell (eds), Proceedings of the 2004 Conference on Genetic and Evolutionary Computation (GECCO 2004), Part I, Vol. 3102 of Lecture Notes in Computer Science, Springer, pp. 1138–1149.
Giacobini, M., Alba, E. and Tomassini, M. (2003). Selection intensity in asynchronous cellular evolutionary algorithms, in E. Cantú-Paz, J. A. Foster, K. Deb, D. Davis, R. Roy, U.-M. O’Reilly, H.-G. Beyer, R. Standish, G. Kendall, S. Wilson, M. Harman, J. Wegener, D. Dasgupta, M. A. Potter, A. C. Schultz, K. Dowsland, N. Jonoska and J. Miller (eds), Proceedings of the 2003 Conference on Genetic and Evolutionary Computation (GECCO 2003), Part I, Vol. 2723 of Lecture Notes in Computer Science, Springer, pp. 955–966.
Giacobini, M. and Tomassini, M. (2003). Investigating selection pressure in asynchronous cellular evolutionary algorithms, in A. M. Barry (ed.), Graduate Student Workshop, Genetic and Evolutionary Computation Conference (GECCO 2003), AAAI, pp. 308–311.
Giacobini, M., Tomassini, M., Tettamanzi, A. G. B. and Alba, E. (2005). Selection intensity in cellular evolutionary algorithms for regular lattices, IEEE Transactions on Evolutionary Computation 9(5): 489–505.
Goldberg, D. E., Deb, K. and Horn, J. (1992). Massive multimodality, deception, and genetic algorithms, in R. Männer and B. Manderick (eds), Parallel Problem Solving from Nature – PPSN II, Elsevier Science Publishers, B. V., pp. 37–46.
Goldberg, D. E. and Richardson, J. (1987). Genetic algorithms with sharing for multi-modal function optimisation, in J. J. Grefenstette (ed.), Proceedings of the Second International Conference on Genetic Algorithms (ICGA’87), Lawrence Erlbaum Associates, pp. 41–49.
Goldberg, D. E. and Smith, R. E. (1987). Nonstationary function optimization using genetic algorithms with dominance and diploidy, in J. J. Grefenstette (ed.), Proceedings of the Second International Conference on Genetic Algorithms (ICGA’87), Lawrence Erlbaum Associates, pp. 59–68.
Gordon, V. S., Pirie, R., Wachter, A. and Sharp, S. (1999). Terrain-based genetic algorithm (TBGA): Modeling parameter space as terrain, in W. Banzhaf, J. M. Daida, A. E. Eiben, M. H. Garzon, V. Honavar, M. J. Jakiela and R. E. Smith (eds), Proceedings of the 1999 Conference on Genetic and Evolutionary Computation (GECCO 1999), Morgan Kaufmann, pp. 229–235.
Gorges-Schleuter, M. (1989). ASPARAGOS an asynchronous parallel genetic optimization strategy, in J. D. Schaffer (ed.), Proceedings of the Third International Conference on Genetic Algorithms (ICGA’89), Morgan Kaufmann, pp. 422–427.
Gorges-Schleuter, M. (1992). Comparison of local mating strategies in massively parallel genetic algorithms, in R. Männer and B. Manderick (eds), Parallel Problem Solving from Nature – PPSN II, Elsevier, pp. 553–562.
Grosso, P. B. (1985). Computer simulation of genetic adaptation: Parallel subcomponent interaction in a multilocus model, PhD thesis, University of Michigan. (University Microﬁlms No. 8520908).
Harik, G. R., Cantú-Paz, E., Goldberg, D. E. and Miller, B. L. (1999). The gambler’s ruin problem, genetic algorithms, and the sizing of populations, Evolutionary Computation 7(3): 231–253.
Hartl, D. L. and Clark, A. G. (2006). Principles of Population Genetics (Fourth Edition), Sinauer Associates, Inc.
Hillis, W. D. (1986). The Connection Machine, MIT Press.
Holland, J. H. (1975). Adaptation in Natural and Artiﬁcial Systems, University of Michigan Press.
Horn, J. (1997). The Nature of Niching: Genetic Algorithms and the Evolution of Optimal, Cooperative populations, PhD thesis, University of Illinois at Urbana Champaign. (Available as IlliGAL Technical Report 97008).
Horn, J. and Goldberg, D. E. (1995). Genetic algorithm difﬁculty and the modality of ﬁtness landscapes, in L. D. Whitley and M. D. Vose (eds), Foundations of Genetic Algorithms 3, Morgan Kaufmann, pp. 243–269.
Horn, J., Nafpliotis, N. and Goldberg, D. E. (1994). A niched pareto genetic algorithm for multiobjective optimization, Proceedings of the First IEEE Conference on Evolutionary Computation, IEEE World Congress on Computational Intelligence, IEEE Press, pp. 82–87.
Jelasity, M. (1998). UEGO, an abstract niching technique for global optimization, in A. E. Eiben, T. Bäck, M. Schoenauer and H.-P. Schwefel (eds), Parallel Problem Solving from Nature – PPSN V, Vol. 1498 of Lecture Notes in Computer Science, Springer, pp. 378–387.
Kimura, M. (1953). “Stepping-Stone” model of population, Annual Report of the National Institute of Genetics 3: 62–63.
Kimura, M. and Ohta, T. (1969). The average number of generations until ﬁxation of a mutant gene in a ﬁnite population, Genetics 61(3): 763–771.
Kimura, M. and Weiss, G. H. (1964). The stepping stone model of population structure and the decrease of genetic correlation with distance, Genetics 49(4): 561–576.
Kirley, M. (2001). MEA: A metapopulation evolutionary algorithm for multi-objective optimisation problems, Proceedings of the 2001 IEEE Conference on Evolutionary Computation, IEEE Press, pp. 949–956.
Kirley, M. (2002). A cellular genetic algorithm with disturbances: Optimisation using dynamic spatial interactions, J. Heuristics 8(3): 321–342.
Kirley, M. and Stewart, R. (2007a). An analysis of the effects of population structure on scalable multiobjective optimization problems, in D. Thierens (ed.), Proceedings of the 2007 Conference on Genetic and Evolutionary Computation (GECCO 2007), Vol. 1, ACM Press, pp. 845–852.
Kirley, M. and Stewart, R. (2007b). Multiobjective evolutionary algorithms on complex networks, in S. Obayashi, K. Deb, C. Poloni, T. Hiroyasu and T. Murata (eds), Evolutionary Multi-Criterion Optimization, Fourth International Conference, EMO 2007, Vol. 4403 of Lecture Notes in Computer Science, Springer., pp. 81–95.
Kohonen, T. (1995). Self-Organizing Maps, Springer.
Li, J. P., Balazs, M. E., Parks, G. T. and Clarkson, P. J. (2002). A species conserving genetic algorithm for multimodal function optimization, Evolutionary Computation 10(3): 207–234.
Lobo, F. G., Lima, C. F. and Michalewicz, Z. (eds) (2007). Parameter Setting in Evolutionary Algorithms, Vol. 54 of Studies in Computational Intelligence, Springer.
Mahfoud, S. W. (1992). Crowding and preselection revisited, in R. Männer and B. Manderick (eds), Parallel Problem Solving from Nature – PPSN II, North-Holland, pp. 27–36.
Mahfoud, S. W. (1994). Genetic drift in sharing methods, Proceedings of the First IEEE Conference on Evolutionary Computation, IEEE World Congress on Computational Intelligence, IEEE Press, pp. 67–72.
Mahfoud, S. W. (1995a). A comparison of parallel and sequential niching methods, in L. Eshelman (ed.), Proceedings of the Sixth International Conference on Genetic Algorithms (ICGA’95), Morgan Kaufmann, pp. 136–143.
Mahfoud, S. W. (1995b). Niching Methods for Genetic Algorithms, PhD thesis, University of Illinois at Urbana-Champaign. (Available as IlliGAL Technical Report 95001).
Mahfoud, S. W. (1995c). Population size and genetic drift in ﬁtness sharing, in L. D. Whitley and M. D. Vose (eds), Foundations of Genetic Algorithms 3, Morgan Kaufmann, pp. 185–224.
Manderick, B. and Spiessens, P. (1989). Fine-grained parallel genetic algorithms, in J. D. Schaffer (ed.), Proceedings of the Third International Conference on Genetic Algorithms (ICGA’89), Morgan Kaufmann, pp. 428–433.
Maruyama, T. (1974). A simple proof that certain quantities are independent of the geographical structure of population, Theoretical Population Biology 5(2): 148–154.
Mayr, E. (1970). Populations, species and evolution; an abridgment of Animal species and evolution, Harvard University Press.
McInerney, J. M. N. (1992). Biologically inﬂuenced algorithms and parallelism in non-linear optimization, PhD thesis, University of California, San Diego, Department of Computer Science and Engineering.
Mühlenbein, H. (1989). Parallel genetic algorithms, population genetics and commbinatorial optimization, in Schaffer (ed.), Proceedings of the Third International Conference on Genetic Algorithms (ICGA’89), Morgan Kaufmann, pp. 416–421.
Murata, T., Ishibuchi, H. and Gen, M. (2000). Cellular genetic local search for multiobjective optimization, in D. Whitley, D. Goldberg, E. Cantu-Paz, L. Spector, I. Parmee and H.-G. Beyer (eds), Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2000), Morgan Kaufmann, pp. 307–314.
Nix, A. E. and Vose, M. D. (1992). Modeling genetic algorithms with Markov chains, Annals of Mathematics and Artiﬁcial Intelligence 5: 78–88.
Oei, C. K., Goldberg, D. E. and Chang, S. J. (1991). Tournament selection, niching and the preservation of diversity, IlliGAL Report No. 91011, Illinois Genetic Algorithms Laboratory, University of Illinois at Urbana-Champaign.
Parrott, D. and Li, X. (2006). Locating and tracking multiple dynamic optima by a particle swarm model using speciation, IEEE Transactions on Evolutionary Computation 10(4): 440–458.
Perez, E., Herrera, F. and Hernandez, C. (2003). Finding multiple solutions in job shop scheduling by niching genetic algorithms, Journal of Intelligent Manufacturing 14(3–4): 323–339.
Pétrowski, A. (1996). A clearing procedure as a niching method for genetic algorithms, Proceedings of the 1996 IEEE International Conference on Evolutionary Computation, IEEE Press, pp. 798–803.
Pétrowski, A. (1997a). An efﬁcient hierarchical clustering technique for speciation, Technical report, Institut National des Télécommunications, Evry, France.
Pétrowski, A. (1997b). A new selection operator dedicated to speciation, in T. Bäck (ed.), Proceedings of the Seventh International Conference on Genetic Algorithms (ICGA’97), Morgan Kaufmann, pp. 144–151.
Pétrowski, A. and Genet, M. G. (1999). A classiﬁcation tree for speciation, Proceedings of the 1999 IEEE Congress on Evolutionary Computation, IEEE Press, pp. 204–211.
Robertson, A. (1962). Selection for heterozygotes in small populations, Genetics 47(9): 1291–1300.
Rudolph, G. (2000). On takeover times in spatially structured populations: Array and ring, in K. K. Lai, O. Katai, M. Gen and B. Lin (eds), Proceedings of the Second Asia-Paciﬁc Conference on Genetic Algorithms and Applications (APGA ’00), Global-Link Publishing Company, pp. 144–151.
Sareni, B. and Krähenbuhl, L. (1998). Fitness sharing and niching methods revisited, IEEE Transactions on Evolutionary Computation 2(3): 97–106.
Sarma, J. (1998). An Analysis of Decentralized and Spatially Distributed Genetic Algorithms, PhD thesis, George Mason University.
Sarma, J. and De Jong, K. (1997). An analysis of local selection algorithms in a spatially structured evolutionary algorithm, in T. Bäck (ed.), Proceedings of the Seventh International Conference on Genetic Algorithms (ICGA’97), Morgan Kaufmann, pp. 181–187.
Skolicki, Z. M. (2007). An Analysis of Island Models in Evolutionary Computation, PhD thesis, George Mason University.
Spears, W. M. (1994). Simple subpopulation schemes, in A. V. Sebald and L. J. Fogel (eds), Evolutionary Programming: Proceedings of the Third Annual Conference, World Scientiﬁc Press, pp. 296–307.
Spiessens, P. and Manderick, B. (1991). A massively parallel genetic algorithm: Implementation and ﬁrst analysis, in L. B. Booker and R. K. Belew (eds), Proceedings of the Fourth International Conference on Genetic Algorithms (ICGA’91), Morgan Kaufmann, pp. 279–287.
Tanese, R. (1989). Distributed genetic algorithms, in J. D. Schaffer (ed.), Proceedings of the Third International Conference on Genetic Algorithms (ICGA’89), Morgan Kaufmann, pp. 434–439.
Tomassini, M. (1993). The parallel genetic cellular automata: Application to global function optimization, in N. C. Steele, R. F. Albrecht and C. R. Reeves (eds), Proceedings of the International Conference on Artiﬁcial Neural Nets and Genetic Algorithms, Springer, pp. 385–391.
Tomassini, M. (2005). Spatially structured evolutionary algorithms, Springer.
Ursem, R. K. (1999). Multinational evolutionary algorithms, in P. J. Angeline, Z. Michalewicz, M. Schoenauer, X. Yao and A. Zalzala (eds), Proceedings of the 1999 IEEE Congress on Evolutionary Computation, Vol. 3, IEEE Press, pp. 1633–1640.
Watson, J.-P. (1999). A performance assessment of modern niching methods for parameter optimization problems, in W. Banzhaf, J. M. Daida, A. E. Eiben, M. H. Garzon, V. Honavar, M. J. Jakiela and R. E. Smith (eds), Proceedings of the 1999 Conference on Genetic and Evolutionary Computation (GECCO 1999), Morgan Kaufmann, pp. 702–709.
Watterson, G. A. (1962). Some theoretical aspects of diffusion theory in population genetics, The Annals of Mathematical Statistics 33(3): 939–957.
Whigham, P. A. and Dick, G. (2002). A study of spatial distribution and evolution, in P. Whigham (ed.), The 14t h Annual Colloquium of the Spatial Information Research Centre, pp. 157–166.
Whigham, P. A. and Green, D. G. (2004). A spatially-explicit model of genetic tradeoff, in R. Stonier, Q. Han and W. Li (eds), Proceedings of the 7th Asia-Paciﬁc Complex Systems Conference, pp. 91–100.
Whitley, D. (1993). Cellular genetic algorithms, in S. Forrest (ed.), Proceedings of the Fifth International Conference on Genetic Algorithms (ICGA’93), Morgan Kaufmann, pp. 658–658.
Wright, S. (1931). Evolution in mendelian populations, Genetics 16(2): 97–159.
Wright, S. (1932). The roles of mutation, inbreeding, crossbreeding and selection in evolution, Proceedings of the Sixth International Congress of Genetics, Vol. 1, pp. 356–366.
Wright, S. (1940). Breeding structure of populations in relation to speciation, The American Naturalist 74(752): 232–248.
Wright, S. (1943). Isolation by distance, Genetics 28(2): 114–138.
Yang, R. (1998). Line-breeding schemes for combinatorial optimization, Parallel Problem Solving from Nature – PPSN V, Springer, pp. 448–460.
Yao, X. and Liu, Y. (2004). Evolving neural network ensembles by minimization of mutual information, Int. J. Hybrid Intell. Syst 1(1): 12–21. | en_NZ |