DNA To Protein: Transformations and Their Possible Role in Linkage Learning

This paper first extends the traditional perspective of linkage using the basic concepts developed in the SEARCH framework and identifies the fundamental objectives of linkage learning. It then explores the computational role of gene-expression (DNA{r_arrow}RNA{r_arrow}Protein transformations) in evolutionary linkage learning, using group representation theory. It offers strong evidence to support the hypothesis that the transformations in gene-expression define a group of symmetry transformations that leaves the fitness invariant; however, they change the eigen functions leading to identifying independent subspaces of the search space (a major objective of linkage learning) using irreducible representations of such transformations.

[1]  H. Kargupta,et al.  Computational processes of evolution and the gene expression messy genetic algorithm , 1996 .

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

[3]  David E. Goldberg,et al.  Learning Linkage , 1996, FOGA.

[4]  V. Heine.  Group theory in quantum mechanics , 1960 .

[5]  Georges R. Harik,et al.  Foundations of Genetic Algorithms , 1997 .

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

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

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

[9]  Sanghamitra Bandyopadhyay,et al.  Further Experimentations on the Scalability of the GEMGA , 1998, PPSN.

[10]  Vilius Ivanauskas.  Conferences , 1979 .

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

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

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

[14]  Hillol Kargupta,et al.  SEARCH, Computational Processes in Evolution, and Preliminary Development of the Gene Expression Messy Genetic Algorithm , 1997, Complex Syst..

[15]  Hillol Kargupta.  Gene expression: The missing link in evolutionary computation , 1997 .

[16]  Satosi Watanabe,et al.  Knowing and guessing , 1969 .

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

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

[19]  Hillol Kargupta,et al.  Function induction, gene expression, and evolutionary representation construction , 1999 .

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

[21]  H. Kargupta.  SEARCH , Evolution , And The Gene Expression Messy Genetic Algorithm , 1994 .

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

[23]  Hillol Kargupta,et al.  The Gene Expression Messy Genetic Algorithm , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.