Evolutionary Computation as a Paradigm for DNA-Based Computing

Evolutionary Computation focuses on probabilistic search and optimization methods gleaned from the model of organic evolution. Genetic algorithms, evolution strategies, and evolutionary programming are three independently developed representatives of this class of algorithms, with genetic programming and classifier systems as additional paradigms in the field.

[1]  P D Kaplan,et al.  DNA solution of the maximal clique problem. , 1997, Science.

[2]  Elizabeth Laun,et al.  Wet splicing systems , 1997, DNA Based Computers.

[3]  Hans-Georg Beyer,et al.  Toward a Theory of Evolution Strategies: On the Benefits of Sex the (/, ) Theory , 1995, Evolutionary Computation.

[4]  Larry J. Eshelman,et al.  Proceedings of the 6th International Conference on Genetic Algorithms , 1995 .

[5]  David B. Fogel,et al.  Evolutionary algorithms in theory and practice , 1997, Complex.

[6]  Thomas Bäck,et al.  Evolutionary computation: comments on the history and current state , 1997, IEEE Trans. Evol. Comput..

[7]  J. Håstad.  Clique is hard to approximate withinn1−ε , 1999 .

[8]  W. Vent,et al.  Rechenberg, Ingo, Evolutionsstrategie — Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. 170 S. mit 36 Abb. Frommann‐Holzboog‐Verlag. Stuttgart 1973. Broschiert , 1975 .

[9]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[10]  John H. Holland,et al.  Induction: Processes of Inference, Learning, and Discovery , 1987, IEEE Expert.

[11]  BeyerHans-Georg.  Toward a theory of evolution strategies , 1993 .

[12]  R. Deaton,et al.  A DNA based implementation of an evolutionary search for good encodings for DNA computation , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[13]  Thomas Bäck,et al.  The Interaction of Mutation Rate, Selection, and Self-Adaptation Within a Genetic Algorithm , 1992, PPSN.

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

[15]  Rajarshi Das,et al.  A Study of Control Parameters Affecting Online Performance of Genetic Algorithms for Function Optimization , 1989, ICGA.

[16]  Alfonso Rodríguez-Patón,et al.  Molecular Computation for Genetic Algorithms , 1998, Rough Sets and Current Trends in Computing.

[17]  Günter Rudolph,et al.  Convergence analysis of canonical genetic algorithms , 1994, IEEE Trans. Neural Networks.

[18]  Thomas Bäck,et al.  Extended Selection Mechanisms in Genetic Algorithms , 1991, ICGA.

[19]  Ingo Rechenberg,et al.  Evolutionsstrategie '94 , 1994, Werkstatt Bionik und Evolutionstechnik.

[20]  J. Launer.  Darwin's dangerous idea. , 2002, QJM : monthly journal of the Association of Physicians.

[21]  Alice E. Smith,et al.  Genetic Optimization Using A Penalty Function , 1993, ICGA.

[22]  Stuart A. Kauffman,et al.  ORIGINS OF ORDER , 2019, Origins of Order.

[23]  Anany Levitin,et al.  Introduction to the Design and Analysis of Algorithms , 2002 .

[24]  Elena Marchiori.  A simple heuristic based genetic algorithm for the maximum clique problem , 1998, SAC '98.

[25]  Thomas Bäck,et al.  Intelligent Mutation Rate Control in Canonical Genetic Algorithms , 1996, ISMIS.

[26]  Jim Smith,et al.  Self adaptation of mutation rates in a steady state genetic algorithm , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[27]  Hans-Georg Beyer,et al.  Toward a Theory of Evolution Strategies: The (, )-Theory , 1994, Evolutionary Computation.

[28]  László Lovász,et al.  Approximating clique is almost NP-complete , 1991, [1991] Proceedings 32nd Annual Symposium of Foundations of Computer Science.

[29]  Thang Nguyen Bui,et al.  A Hybrid Genetic Algorithm for the Maximum Clique Problem , 1995, ICGA.

[30]  Thomas Bäck,et al.  Evolutionary computation: Toward a new philosophy of machine intelligence , 1997, Complex..

[31]  J. David Schaffer,et al.  Proceedings of the third international conference on Genetic algorithms , 1989 .

[32]  D. Labie,et al.  Molecular Evolution , 1991, Nature.

[33]  Terence C. Fogarty,et al.  Varying the Probability of Mutation in the Genetic Algorithm , 1989, ICGA.

[34]  Hans-Georg Beyer,et al.  Toward a Theory of Evolution Strategies: Self-Adaptation , 1995, Evolutionary Computation.

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

[36]  Manfred Eigen,et al.  Macromolecular Evolution: Dynamical Ordering in Sequence Space , 1985 .

[37]  Lothar Thiele,et al.  A Comparison of Selection Schemes Used in Evolutionary Algorithms , 1996, Evolutionary Computation.

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

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

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

[41]  Petr Hájek,et al.  Mathematical Foundations of Computer Science 1995 , 1995, Lecture Notes in Computer Science.

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

[43]  R J Lipton,et al.  DNA solution of hard computational problems. , 1995, Science.

[44]  David B. Fogel,et al.  Evolutionary Computation: Towards a New Philosophy of Machine Intelligence , 1995 .

[45]  Juris Hartmanis.  On the Computing Paradigm and Computational Complexity , 1995, MFCS.

[46]  T. Head.  Formal language theory and DNA: an analysis of the generative capacity of specific recombinant behaviors. , 1987, Bulletin of mathematical biology.

[47]  Zbigniew Michalewicz,et al.  Evolutionary Computation 2 , 2000 .

[48]  Hans-Paul Schwefel,et al.  Evolutionary Programming and Evolution Strategies: Similarities and Differences , 1993 .

[49]  Gunar E. Liepins,et al.  Some Guidelines for Genetic Algorithms with Penalty Functions , 1989, ICGA.

[50]  Marcello Pelillo,et al.  Evolutionary Approach to the Maximum Clique Problem: Empirical Evidence on a Larger Scale , 1997 .

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

[52]  H. Beyer.  An alternative explanation for the manner in which genetic algorithms operate. , 1997, Bio Systems.

[53]  Tom Head.  Splicing systems and molecular processes , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[54]  David I. Lewin,et al.  DNA computing , 2002, Comput. Sci. Eng..

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

[56]  A. E. Eiben,et al.  Genetic algorithms with multi-parent recombination , 1994, PPSN.

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

[58]  Lila Kari,et al.  From Micro-Soft to Bio-Soft: Computing With DNA , 1997, BCEC.

[59]  Thomas Bck.  Generalized convergence models for tournament|and (1; ?)|selection , 1995 .

[60]  Zbigniew Michalewicz,et al.  Handbook of Evolutionary Computation , 1997 .

[61]  Panos M. Pardalos,et al.  The maximum clique problem , 1994, J. Glob. Optim..

[62]  Tibor Csendes,et al.  Developments in Global Optimization , 1997 .

[63]  H. P. Schwefel,et al.  Numerische Optimierung von Computermodellen mittels der Evo-lutionsstrategie , 1977 .

[64]  Thomas Bäck,et al.  Evolution Strategies on Noisy Functions: How to Improve Convergence Properties , 1994, PPSN.

[65]  Heinz Mühlenbein,et al.  How Genetic Algorithms Really Work: Mutation and Hillclimbing , 1992, PPSN.

[66]  Hans-Paul Schwefel,et al.  Evolution and optimum seeking , 1995, Sixth-generation computer technology series.

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

[68]  Ingo Rechenberg,et al.  Evolutionsstrategie : Optimierung technischer Systeme nach Prinzipien der biologischen Evolution , 1973 .

[69]  Paul Thagard,et al.  Induction: Processes Of Inference , 1989 .

[70]  Thomas Bäck,et al.  Optimal Mutation Rates in Genetic Search , 1993, ICGA.

[71]  Johan Håstad,et al.  Clique is hard to approximate within n1-epsilon , 1996, Electron. Colloquium Comput. Complex..

[72]  Stuart A. Kauffman,et al.  The origins of order , 1993 .

[73]  John R. Koza,et al.  Genetic programming: a paradigm for genetically breeding populations of computer programs to solve problems , 1990 .

[74]  David S. Johnson,et al.  Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .

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

[76]  L M Adleman,et al.  Molecular computation of solutions to combinatorial problems. , 1994, Science.

[77]  David E. Goldberg,et al.  Genetic Algorithms, Selection Schemes, and the Varying Effects of Noise , 1996, Evolutionary Computation.

[78]  Thomas Bäck,et al.  Evolutionary Algorithms in Theory and Practice , 1996 .

[79]  Larry J. Eshelman,et al.  Biases in the Crossover Landscape , 1989, ICGA.

[80]  Juris Hartmanis,et al.  On the Weight of Computations , 1995, Bull. EATCS.

[81]  Raymond E. Miller,et al.  Complexity of Computer Computations , 1972 .