Evolution as Computation: DIMACS Workshop, Princeton, by James A. Shapiro (auth.), Prof. Dr. Laura F. Landweber,

By James A. Shapiro (auth.), Prof. Dr. Laura F. Landweber, Prof. Dr. Erik Winfree (eds.)

The research of the genetic foundation for evolution has flourished during this century, in addition to our realizing of the evolvability and programmability of organic structures. Genetic algorithms in the meantime grew out of the belief machine application might use the biologically-inspired techniques of mutation, recombination, and choice to resolve not easy optimization difficulties. Genetic and evolutionary programming offer additional techniques to a large choice of computational difficulties. A synthesis of those stories finds primary insights into either the computational nature of organic evolution and procedures of value to computing device technology. issues comprise organic types of nucleic acid details processing and genome evolution; molecules, cells, and metabolic circuits that compute logical relationships; the foundation and evolution of the genetic code; and the interface with genetic algorithms, genetic and evolutionary programming. This examine combines conception and experiments to appreciate the computations that ensue in cells and the combinatorial methods that force evolution on the molecular level.

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Extra resources for Evolution as Computation: DIMACS Workshop, Princeton, January 1999

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18. T. Blickle and L. Thiele. A comparison of selection schemes used in evolutionary algorithms. Evolutionary Computation, 4(4):361–394. 19. I. M. Bomze, M. Pelillo, and R. Giacomini. Evolutionary approach to the maximum clique problem: Empirical evidence on a larger scale. In I. M. Bomze, editor, Developments in Global Optimization, pages 95–108. Kluwer, Amsterdam, 1997. 20. Thang Nguyen Bui and P. H. Eppley. A hybrid genetic algorithm for the maximum clique problem. In L. Eshelman, editor, Genetic Algorithms: Proceedings of the 6th International Conference, pages 478–484.

Rau´ ´e, and Zs. Ruttkay. Genetic algorithms with multiparent recombination. In Y. -P. Schwefel, and R. M¨ a ¨nner, editors, Parallel Problem Solving from Nature — PPSN III International Conference on Evolutionary Computation, volume 866 of Lecture Notes in Computer Science, pages 78–87. Springer, Berlin, 1994. 26. M. Eigen. Macromolecular evolution: Dynamical ordering in sequence space. Ber. Bunsenges. Phy. , 89:658–667, 1985. 27. L. J. Eshelman, R. A. Caruna, and J. D. Schaffer. Biases in the crossover landscape.

Those mutations that change correct bits back to incorrect ones, resulting in an approximation of p∗ as p∗ ≈ 1/n, which is a reasonable general-purpose heuristic for genetic algorithms [51]. While the generalization of this kind of convergence velocity analysis to (1,λ)- and (μ,λ)-genetic algorithms was formalized in [7] without obtaining a closed expression for the optimal mutation rate, Beyer recently presented an approximation for the (1,λ)-genetic algorithm applied to the bit counting function [17]: p∗ ≈ c21,λ 1 · , ∗ 2 4 · (2 · f (x)/f − 1) n (15) where f ∗ = n denotes the optimal fitness value, f (x) > n/2, and n → ∞ are assumed.

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