### Search, polynomial complexity, and the fast messy genetic algorithm

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[1] Feller William,et al. An Introduction To Probability Theory And Its Applications , 1950 .

[2] F. Young. Biochemistry , 1955, The Indian Medical Gazette.

[3] J. Monod,et al. Genetic regulatory mechanisms in the synthesis of proteins. , 1961, Journal of molecular biology.

[4] Lawrence J. Fogel,et al. Artificial Intelligence through Simulated Evolution , 1966 .

[5] John Daniel. Bagley,et al. The behavior of adaptive systems which employ genetic and correlation algorithms : technical report , 1967 .

[6] R. Rosenberg. Simulation of genetic populations with biochemical properties : technical report , 1967 .

[7] Nils J. Nilsson,et al. A Formal Basis for the Heuristic Determination of Minimum Cost Paths , 1968, IEEE Trans. Syst. Sci. Cybern..

[8] M. H. Heycock,et al. Papers , 1971, BMJ : British Medical Journal.

[9] K. Dejong,et al. An analysis of the behavior of a class of genetic adaptive systems , 1975 .

[10] John H. Holland,et al. Adaptation in natural and artificial systems , 1975 .

[11] I. Olkin,et al. Selecting and Ordering Populations: A New Statistical Methodology , 1977 .

[12] Jacobus P. H. Wessels,et al. The Art and Theory of Dynamic Programming , 1979 .

[13] Temple F. Smith. Occam's razor , 1980, Nature.

[14] Anne Brindle,et al. Genetic algorithms for function optimization , 1980 .

[15] Nesa L'abbe Wu,et al. Linear programming and extensions , 1981 .

[16] Lashon B. Booker,et al. Intelligent Behavior as an Adaptation to the Task Environment , 1982 .

[17] J. Davies,et al. Molecular Biology of the Cell , 1983, Bristol Medico-Chirurgical Journal.

[18] C. D. Gelatt,et al. Optimization by Simulated Annealing , 1983, Science.

[19] Leslie G. Valiant,et al. A theory of the learnable , 1984, STOC '84.

[20] Francesco Archetti,et al. A survey on the global optimization problem: General theory and computational approaches , 1984, Ann. Oper. Res..

[21] Alexander H. G. Rinnooy Kan,et al. Stochastic methods for global optimization , 1984 .

[22] David E. Goldberg,et al. Alleles, loci and the traveling salesman problem , 1985 .

[23] D. E. Goldberg,et al. Simple Genetic Algorithms and the Minimal, Deceptive Problem , 1987 .

[24] David J. Sirag,et al. Toward a unified thermodynamic genetic operator , 1987 .

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

[26] D. Ackley. A connectionist machine for genetic hillclimbing , 1987 .

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

[28] David Haussler,et al. Quantifying Inductive Bias: AI Learning Algorithms and Valiant's Learning Framework , 1988, Artif. Intell..

[29] Gilbert Syswerda,et al. Uniform Crossover in Genetic Algorithms , 1989, ICGA.

[30] J. Mockus,et al. The Bayesian approach to global optimization , 1989 .

[31] John J. Grefenstette,et al. How Genetic Algorithms Work: A Critical Look at Implicit Parallelism , 1989, ICGA.

[32] Kalyanmoy Deb,et al. Messy Genetic Algorithms: Motivation, Analysis, and First Results , 1989, Complex Syst..

[33] Fred W. Glover,et al. Tabu Search - Part I , 1989, INFORMS J. Comput..

[34] Kalyanmoy Deb,et al. Messy Genetic Algorithms Revisited: Studies in Mixed Size and Scale , 1990, Complex Syst..

[35] David E. Goldberg,et al. A Note on Boltzmann Tournament Selection for Genetic Algorithms and Population-Oriented Simulated Annealing , 1990, Complex Syst..

[36] D. Goldberg,et al. An investigation of messy genetic algorithms , 1990 .

[37] Gerhard W. Dueck,et al. Threshold accepting: a general purpose optimization algorithm appearing superior to simulated anneal , 1990 .

[38] Kalyanmoy Deb,et al. A Comparative Analysis of Selection Schemes Used in Genetic Algorithms , 1990, FOGA.

[39] S. Vavasis. Nonlinear optimization: complexity issues , 1991 .

[40] K. Deb. Binary and floating-point function optimization using messy genetic algorithms , 1991 .

[41] Nicholas J. Radcliffe,et al. Forma Analysis and Random Respectful Recombination , 1991, ICGA.

[42] Fabio Schoen,et al. Stochastic techniques for global optimization: A survey of recent advances , 1991, J. Glob. Optim..

[43] Kalyanmoy Deb,et al. Genetic Algorithms, Noise, and the Sizing of Populations , 1992, Complex Syst..

[44] Nicholas J. Radcliffe,et al. Genetic Set Recombination , 1992, FOGA.

[45] Kalyanmoy Deb,et al. Accounting for Noise in the Sizing of Populations , 1992, FOGA.

[46] David E. Goldberg,et al. A Genetic Algorithm for Parallel Simulated Annealing , 1992, PPSN.

[47] Translator-IEEE Expert staff. Machine Learning: A Theoretical Approach , 1992, IEEE Expert.

[48] Kenneth A. De Jong,et al. Are Genetic Algorithms Function Optimizers? , 1992, PPSN.

[49] H. Kargupta. Drift, Diffusion And Boltzmann Distribution In Simple Genetic Algorithm , 1992, Workshop on Physics and Computation.

[50] Kalyanmoy Deb,et al. Ordering Genetic Algorithms and Deception , 1992, PPSN.

[51] Kalyanmoy Deb,et al. Analyzing Deception in Trap Functions , 1992, FOGA.

[52] Andrew Dymek. An Examination of Hypercube Implementations of Genetic Algorithms , 1992 .

[53] Lashon B. Booker,et al. Recombination Distributions for Genetic Algorithms , 1992, FOGA.

[54] Melanie Mitchell,et al. Relative Building-Block Fitness and the Building Block Hypothesis , 1992, FOGA.

[55] Günter Rudolph,et al. Massively Parallel Simulated Annealing and Its Relation to Evolutionary Algorithms , 1993, Evolutionary Computation.

[56] Gary B. Lamont,et al. Comparison of Parallel Messy Genetic Algorithm Data Distribution Strategies , 1993, ICGA.

[57] Afonso Ferreira,et al. BOUNDING THE PROBABILITY OF SUCCESS OF STOCHASTIC METHODS FOR GLOBAL OPTIMIZATION , 1993 .

[58] Kalyanmoy Deb,et al. RapidAccurate Optimization of Difficult Problems Using Fast Messy Genetic Algorithms , 1993, ICGA.

[59] Dirk Thierens,et al. Toward a Better Understanding of Mixing in Genetic Algorithms , 1993 .

[60] Hillol Kargupta. Information Transmission in Genetic Algorithm and Shannon's Second Theorem , 1993, ICGA.

[61] Dirk Thierens,et al. Mixing in Genetic Algorithms , 1993, ICGA.

[62] Ryszard S. Michalski,et al. A theory and methodology of inductive learning , 1993 .

[63] Hillol Kargupta,et al. Temporal sequence processing based on the biological reaction-diffusion process , 1994, Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94).

[64] W. Hart. Adaptive global optimization with local search , 1994 .

[65] D. Goldberg. Genetic Algorithm Di culty and the Modality ofFitness , 1994 .

[66] G. Unter Rudolph. Massively Parallel Simulated Annealing and itsRelation to Evolutionary , 1994 .

[67] Terry Jones,et al. A Description of Holland's Royal Road Function , 1994, Evolutionary Computation.

[68] Joseph C. Culberson,et al. Mutation-Crossover Isomorphisms and the Construction of Discriminating Functions , 1994, Evolutionary Computation.

[69] David E. GoldbergDepartment. Decision Making in Genetic Algorithms: a Signal-to-noise Perspective Decision Making in Genetic Algorithms: a Signal-to-noise Perspective , 1994 .

[70] Sylvian R. Ray,et al. A Temporal Sequence Processor Based on the Biological Reaction-diffusion Process , 1993, Complex Syst..

[71] Hillol Kargupta,et al. Signal-to-noise, Crosstalk, and Long Range Problem Difficulty in Genetic Algorithms , 1995, ICGA.

[72] Terry Jones,et al. Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms , 1995, ICGA.

[73] Tim Jones. Evolutionary Algorithms, Fitness Landscapes and Search , 1995 .

[74] M. R. Rao,et al. Combinatorial Optimization , 1992, NATO ASI Series.

[75] Schloss Birlinghoven,et al. How Genetic Algorithms Really Work I.mutation and Hillclimbing , 2022 .