From Gene Expression to Large Scale Evolutionary Optimization

The Genetic algorithms (GAs) are popular search algorithms motivated by the natural evolution. The GAs have been successfully applied to solve many search, optimization, and machine learning related problems. Despite all the success stories, the popularity of the GAs in the twenty-first century is likely to be determined on grounds of scalable performance, a largely neglected issue in the GA literature. Therefore, the issue of scalability should be of growing concern in GA research.

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

[2]  David E. Goldberg,et al.  Genetic Algorithms and Walsh Functions: Part I, A Gentle Introduction , 1989, Complex Syst..

[3]  Laurence D. Merkle.  Generalization and Parallelization of Messy Genetic Algorithms and Communication in Parallel Genetic Algorithms. , 1992 .

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

[5]  Gang Wang,et al.  Revisiting the GEMGA: scalable evolutionary optimization through linkage learning , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

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

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

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

[9]  Lashon B. Booker,et al.  Proceedings of the fourth international conference on Genetic algorithms , 1991 .

[10]  Kenneth Alan De Jong,et al.  An analysis of the behavior of a class of genetic adaptive systems. , 1975 .

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

[12]  Heinz Mühlenbein,et al.  Schemata, Distributions and Graphical Models in Evolutionary Optimization , 1999, J. Heuristics.

[13]  P Béland,et al.  The origin and evolution of the genetic code. , 1994, Journal of theoretical biology.

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

[15]  H. Kargupta.  Search, polynomial complexity, and the fast messy genetic algorithm , 1996 .

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

[17]  Melanie Mitchell,et al.  An introduction to genetic algorithms , 1996 .

[18]  G. Harik.  Learning gene linkage to efficiently solve problems of bounded difficulty using genetic algorithms , 1997 .

[19]  H. Mühlenbein,et al.  From Recombination of Genes to the Estimation of Distributions I. Binary Parameters , 1996, PPSN.

[20]  Hillol Kargupta,et al.  DNA To Protein: Transformations and Their Possible Role in Linkage Learning , 1997, ICGA.

[21]  Gunar E. Liepins,et al.  Polynomials, Basis Sets, and Deceptiveness in Genetic Algorithms , 1991, Complex Syst..

[22]  B. Sankur,et al.  Applications of Walsh and related functions , 1986 .

[23]  David E. Goldberg,et al.  SEARCH, Blackbox Optimization, And Sample Complexity , 1996, FOGA.

[24]  A. D. Bethke,et al.  Comparison of genetic algorithms and gradient-based optimizers on parallel processors : efficiency of use of processing capacity , 1976 .

[25]  Melanie Mitchell,et al.  The Performance of Genetic Algorithms on Walsh Polynomials: Some Anomalous Results and Their Explanation , 1991, ICGA.

[26]  J Otsuka,et al.  Evolution of genetic information flow from the viewpoint of protein sequence similarity. , 1994, Journal of theoretical biology.

[27]  David E. Goldberg,et al.  Genetic Algorithms and Walsh Functions: Part II, Deception and Its Analysis , 1989, Complex Syst..

[28]  Jim Smith,et al.  Recombination strategy adaptation via evolution of gene linkage , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[29]  S. Baluja,et al.  Using Optimal Dependency-Trees for Combinatorial Optimization: Learning the Structure of the Search Space , 1997 .