Evolutionary Computation: Centralized, Parallel or Collaborative

This chapter discusses the nature and the importance of spatial interactions in evolutionary computation. The current state of evolution theories is discussed. An interaction model is investigated which we have called Darwin’s continent-island cycle conjecture. Darwin argued that such a cycle is the most efficient for successful evolution. This bold conjecture has not yet been noticed in science. We confirm Darwin’s conjecture using an evolutionary game based on the iterated prisoner’s dilemma. A different interaction scheme, called the stepping-stone model is used by the Parallel Genetic Algorithm PGA. The PGA is used to solve combinatorial optimization problems. Then the Breeder Genetic Algorithm BGA used for global optimization of continuous functions is described. The BGA uses competition between subpopulations applying different strategies. This kind of interaction is found in ecological systems.

[1]  Cecilia R. Aragon,et al.  Optimization by Simulated Annealing: An Experimental Evaluation; Part I, Graph Partitioning , 1989, Oper. Res..


[3]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[4]  Josef Hofbauer,et al.  Evolutionary Games and Population Dynamics , 1998 .

[5]  Sewall Wright,et al.  Factor interaction and linkage in evolution , 1965, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[6]  Robin Milner,et al.  On Observing Nondeterminism and Concurrency , 1980, ICALP.

[7]  Dana S. Richards,et al.  Punctuated Equilibria: A Parallel Genetic Algorithm , 1987, ICGA.


[9]  Shen Lin Computer solutions of the traveling salesman problem , 1965 .

[10]  J. K. Lenstra,et al.  Local Search in Combinatorial Optimisation. , 1997 .

[11]  Reiko Tanese,et al.  Distributed Genetic Algorithms , 1989, ICGA.

[12]  M. Padberg,et al.  Addendum: Optimization of a 532-city symmetric traveling salesman problem by branch and cut , 1990 .

[13]  L. Dugatkin,et al.  Nepotism vs Tit-For-Tat, or, why should you be nice to your rotten brother? , 1991, Evolutionary Ecology.

[14]  Robert Axelrod,et al.  The Evolution of Strategies in the Iterated Prisoner's Dilemma , 2001 .

[15]  Gregor von Laszewski,et al.  Intelligent Structural Operators for the k-way Graph Partitioning Problem , 1991, ICGA.

[16]  Susan Oyama,et al.  Evolution's eye: A systems view of the biology-culture divide. , 2000 .

[17]  J. Felsenstein A Pain in the Torus: Some Difficulties with Models of Isolation by Distance , 1975, The American Naturalist.

[18]  Domenico Parisi,et al.  Living in Enclaves , 2001, Complex..

[19]  Martin Pelikan,et al.  Scalable Optimization via Probabilistic Modeling , 2006, Studies in Computational Intelligence.

[20]  Schloss Birlinghoven Evolution in Time and Space -the Parallel Genetic Algorithm , 1991 .

[21]  T. Mahnig,et al.  A new adaptive Boltzmann selection schedule SDS , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[22]  Heinz Mühlenbein,et al.  Predictive Models for the Breeder Genetic Algorithm I. Continuous Parameter Optimization , 1993, Evolutionary Computation.

[23]  Martina Gorges-Schleuter,et al.  Genetic algorithms and population structures: a massively parallel algorithm , 1991 .

[24]  Heinz Mühlenbein,et al.  The Factorized Distribution Algorithm and the Minimum Relative Entropy Principle , 2006, Scalable Optimization via Probabilistic Modeling.

[25]  P. Griffiths The Philosophy of Molecular and Developmental Biology , 2000 .

[26]  Brian W. Kernighan,et al.  An efficient heuristic procedure for partitioning graphs , 1970, Bell Syst. Tech. J..

[27]  Heinz Mühlenbein,et al.  Evolution in Time and Space - The Parallel Genetic Algorithm , 1990, FOGA.

[28]  R. Punnett,et al.  The Genetical Theory of Natural Selection , 1930, Nature.

[29]  H. Maturana,et al.  Autopoiesis and Cognition : The Realization of the Living (Boston Studies in the Philosophy of Scie , 1980 .

[30]  S. Gould,et al.  Punctuated equilibria: the tempo and mode of evolution reconsidered , 1977, Paleobiology.

[31]  Heinz Mühlenbein,et al.  Evolutionary optimization and the estimation of search distributions with applications to graph bipartitioning , 2002, Int. J. Approx. Reason..

[32]  E. Jablonka,et al.  Evolution in Four Dimensions , 2005 .

[33]  J M Smith,et al.  Evolution and the theory of games , 1976 .

[34]  Heinz Mühlenbein,et al.  Strategy Adaption by Competing Subpopulations , 1994, PPSN.

[35]  David S. Johnson,et al.  Local Optimization and the Traveling Salesman Problem , 1990, ICALP.

[36]  H. Jane Brockmann,et al.  The selfish gene (2nd edn) , 1990 .

[37]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[38]  W. Hamilton The genetical evolution of social behaviour. I. , 1964, Journal of theoretical biology.

[39]  H. Maturana,et al.  Autopoiesis and Cognition , 1980 .

[40]  S. Wright,et al.  The Distribution of Gene Frequencies Under Irreversible Mutation. , 1938, Proceedings of the National Academy of Sciences of the United States of America.


[42]  W. Hamilton,et al.  The evolution of cooperation. , 1984, Science.

[43]  Heinz Mühlenbein,et al.  Adaptation of population sizes by competing subpopulations , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[44]  Bernard Manderick,et al.  Fine-Grained Parallel Genetic Algorithms , 1989, ICGA.

[45]  Heinz Mühlenbein,et al.  Evolution algorithms in combinatorial optimization , 1988, Parallel Comput..

[46]  Eörs Szathmáry,et al.  The Major Transitions in Evolution , 1997 .

[47]  Dirk Schlierkamp Voosen Strategy Adaptation by Competing Subpopulations , 1994 .

[48]  Emile H. L. Aarts,et al.  Genetic Local Search Algorithms for the Travelling Salesman Problem , 1990, PPSN.

[49]  John H. Miller,et al.  The coevolution of automata in the repeated Prisoner's Dilemma , 1996 .

[50]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[51]  John Merritt Emlen Population biology. The coevolution of population dynamics and behaviour , 1984 .

[52]  Michael Silberstein,et al.  The Blackwell Guide to the Philosophy of Science , 2002 .

[53]  Reinhard Männer,et al.  Parallel Problem Solving from Nature — PPSN III , 1994, Lecture Notes in Computer Science.

[54]  Lawrence Davis,et al.  Genetic Algorithms and Simulated Annealing , 1987 .

[55]  M. Feldman,et al.  Cultural transmission and evolution: a quantitative approach. , 1981, Monographs in population biology.

[56]  Heinz Mühlenbein,et al.  The parallel genetic algorithm as function optimizer , 1991, Parallel Comput..

[57]  Robert E. Marks,et al.  Breeding hybrid strategies: optimal behaviour for oligopolists , 1989, ICGA.

[58]  H. Muhlenbein,et al.  Gene pool recombination and utilization of covariances for the Breeder Genetic Algorithm , 1995, Proceedings of 1995 IEEE International Conference on Evolutionary Computation.

[59]  Martina Gorges-Schleuter,et al.  ASPARAGOS An Asynchronous Parallel Genetic Optimization Strategy , 1989, ICGA.

[60]  Heinz Mühlenbein,et al.  The Science of Breeding and Its Application to the Breeder Genetic Algorithm (BGA) , 1993, Evolutionary Computation.

[61]  J. Felsenstein The theoretical population genetics of variable selection and migration. , 1976, Annual review of genetics.

[62]  Dan Boneh,et al.  On genetic algorithms , 1995, COLT '95.