Genetic programming - on the programming of computers by means of natural selection

Background on genetic algorithms, LISP, and genetic programming hierarchical problem-solving introduction to automatically-defined functions - the two-boxes problem problems that straddle the breakeven point for computational effort Boolean parity functions determining the architecture of the program the lawnmower problem the bumblebee problem the increasing benefits of ADFs as problems are scaled up finding an impulse response function artificial ant on the San Mateo trail obstacle-avoiding robot the minesweeper problem automatic discovery of detectors for letter recognition flushes and four-of-a-kinds in a pinochle deck introduction to biochemistry and molecular biology prediction of transmembrane domains in proteins prediction of omega loops in proteins lookahead version of the transmembrane problem evolutionary selection of the architecture of the program evolution of primitives and sufficiency evolutionary selection of terminals evolution of closure simultaneous evolution of architecture, primitive functions, terminals, sufficiency, and closure the role of representation and the lens effect. Appendices: list of special symbols list of special functions list of type fonts default parameters computer implementation annotated bibliography of genetic programming electronic mailing list and public repository.

[1]  J. Joseph.  Fourier Series , 1946, Nature.

[2]  J. Kemeny.  Man Viewed as a Machine , 1955 .

[3]  J. von Neumann,et al.  Probabilistic Logic and the Synthesis of Reliable Organisms from Unreliable Components , 1956 .

[4]  J. D. Bernal,et al.  “The Origins of Life” , 1957, Nature.

[5]  Richard M. Friedberg,et al.  A Learning Machine: Part I , 1958, IBM J. Res. Dev..

[6]  F ROSENBLATT,et al.  The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.

[7]  Richard M. Friedberg,et al.  A Learning Machine: Part II , 1959, IBM J. Res. Dev..

[8]  Arthur L. Samuel,et al.  Some Studies in Machine Learning Using the Game of Checkers , 1967, IBM J. Res. Dev..

[9]  Leonard Uhr,et al.  A pattern recognition program that generates, evaluates, and adjusts its own operators , 1961, IRE-AIEE-ACM '61 (Western).

[10]  A. Gray,et al.  I. THE ORIGIN OF SPECIES BY MEANS OF NATURAL SELECTION , 1963 .

[11]  Michael A. Arbib,et al.  Simple Self-Reproducing Universal Automata , 1966, Inf. Control..

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

[13]  John von Neumann,et al.  Theory Of Self Reproducing Automata , 1967 .

[14]  Aristid Lindenmayer,et al.  Mathematical Models for Cellular Interactions in Development , 1968 .

[15]  G. Marsaglia.  Random numbers fall mainly in the planes. , 1968, Proceedings of the National Academy of Sciences of the United States of America.

[16]  M. D. MacLaren.  The Art of Computer Programming—Volume 1: Fundamental Algorithms (Donald E. Knuth) , 1969 .

[17]  Arthur W. Burks,et al.  VON NEUMANN'S SELF-REPRODUCING AUTOMATA , 1969 .

[18]  D. J. Cavicchio,et al.  Adaptive search using simulated evolution , 1970 .

[19]  Arthur W. Burks,et al.  Essays on cellular automata , 1970 .

[20]  M. D. MacLaren.  The Art of Computer Programming. Volume 2: Seminumerical Algorithms (Donald E. Knuth) , 1970 .

[21]  J. Meditch,et al.  Applied optimal control , 1972, IEEE Transactions on Automatic Control.

[22]  John R. Koza,et al.  On inducing a non-trivial, parsimonious, heirarchical grammar for a given sample of sentences , 1973 .

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

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

[25]  Douglas B. Lenat,et al.  AM, an artificial intelligence approach to discovery in mathematics as heuristic search , 1976 .

[26]  Richard Laing.  Automaton Introspection , 1976, J. Comput. Syst. Sci..

[27]  Douglas B. Lenat,et al.  Automated Theory Formation in Mathematics , 1977, IJCAI.

[28]  John H. Holland,et al.  Cognitive systems based on adaptive algorithms , 1977, SGAR.

[29]  R. Laing.  Automaton models of reproduction by self-inspection. , 1977, Journal of theoretical biology.

[30]  H. Simon.  Models of Thought , 1979 .

[31]  Stephen F. Smith,et al.  A learning system based on genetic adaptive algorithms , 1980 .

[32]  W. Hamilton,et al.  The evolution of cooperation. , 1984, Science.

[33]  J. Roughgarden.  Theory of Population Genetics and Evolutionary Ecology: An Introduction , 1995 .

[34]  Nils J. Nilsson,et al.  Principles of Artificial Intelligence , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[35]  Stephen F. Smith,et al.  Flexible Learning of Problem Solving Heuristics Through Adaptive Search , 1983, IJCAI.

[36]  Douglas B. Lenat,et al.  Why AM and EURISKO Appear to Work , 1984, Artif. Intell..

[37]  M. E. Muller.  The Handbook of Random Number Generation and Testing with TESTRAND Computer Code , 1983 .

[38]  Ryszard S. Michalski,et al.  A Theory and Methodology of Inductive Learning , 1983, Artificial Intelligence.

[39]  Douglas B. Lenat,et al.  The Role of Heuristics in Learning by Discovery: Three Case Studies , 1983 .

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

[41]  R. Michalski,et al.  Learning from Observation: Conceptual Clustering , 1983 .

[42]  F. K. Hanna,et al.  AM: A Case Study in AI Methodology , 1984, Artif. Intell..

[43]  C. Langton.  Self-reproduction in cellular automata , 1984 .

[44]  Nichael Lynn Cramer,et al.  A Representation for the Adaptive Generation of Simple Sequential Programs , 1985, ICGA.

[45]  Thomas H. Westerdale,et al.  The Bucket Brigade Is Not Genetic , 1985, International Conference on Genetic Algorithms.

[46]  Kenneth A. De Jong,et al.  Genetic algorithms: A 10 Year Perspective , 1985, ICGA.

[47]  Rodney A. Brooks,et al.  A Mobile Robot with Onboard Parallel Processor and Large Workspace Arm , 1986, AAAI.

[48]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[49]  P. Anandan,et al.  Cooperativity in Networks of Pattern Recognizing Stochastic Learning Automata , 1986 .

[50]  Rodney A. Brooks,et al.  A Robust Layered Control Syste For A Mobile Robot , 2022 .

[51]  John H. Holland,et al.  Escaping brittleness: the possibilities of general-purpose learning algorithms applied to parallel rule-based systems , 1995 .

[52]  S. Wolfram.  Random sequence generation by cellular automata , 1986 .

[53]  Christopher G. Langton,et al.  Studying artificial life with cellular automata , 1986 .

[54]  湯浅 太一,et al.  Common Lisp(パネル討論会) , 1986 .

[55]  Jean-Louis Deneubourg,et al.  Random behaviour, amplification processes and number of participants: how they contribute to the foraging properties of ants , 1986 .

[56]  John Dickinson,et al.  Using the Genetic Algorithm to Generate LISP Source Code to Solve the Prisoner's Dilemma , 1987, ICGA.

[57]  Michael R. Genesereth,et al.  Logical foundations of artificial intelligence , 1987 .

[58]  Herbert A. Simon,et al.  Scientific discovery: compulalional explorations of the creative process , 1987 .

[59]  Clive Richards,et al.  The Blind Watchmaker , 1987, Bristol Medico-Chirurgical Journal.

[60]  C. G. Shaefer,et al.  The ARGOT Strategy: Adaptive Representation Genetic Optimizer Technique , 1987, ICGA.

[61]  R. D. Mills,et al.  Using a Small Algebraic Manipulation System to Solve Differential and Integral Equations by Variational and Approximation Techniques , 1987, J. Symb. Comput..

[62]  Tommaso Toffoli,et al.  Cellular Automata Machines , 1987, Complex Syst..

[63]  Rodney A. Brooks,et al.  Asynchronous Distributed Control System For A Mobile Robot , 1987, Other Conferences.

[64]  L. Buss,et al.  The evolution of individuality , 1987 .

[65]  F. A. Bostock,et al.  Discrete hamstrung squad car games , 1987 .

[66]  Kenneth A. De Jong,et al.  On Using Genetic Algorithms to Search Program Spaces , 1987, ICGA.

[67]  H. J. Antonisse,et al.  Genetic Operators for High-Level Knowledge Representations , 1987, ICGA.

[68]  George G. Robertson,et al.  Parallel Implementation of Genetic Algorithms in a Classifier Rystem , 1987, ICGA.

[69]  Stewart W. Wilson.  Hierarchical Credit Allocation in a Classifier System , 1987, IJCAI.

[70]  Riva Wenig Bickel,et al.  Tree Structured Rules in Genetic Algorithms , 1987, ICGA.

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

[72]  P. W. Jones,et al.  Bandit Problems, Sequential Allocation of Experiments , 1987 .

[73]  James E. Baker,et al.  Reducing Bias and Inefficienry in the Selection Algorithm , 1987, ICGA.

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

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

[76]  John H. Holland,et al.  Empirical studies of default hierarchies and sequences of rules in learning classifier systems , 1988 .

[77]  Stewart W. Wilson.  Bid Competition and Specificity Reconsidered , 1988, Complex Systems.

[78]  O. G. Selfridge,et al.  Pandemonium: a paradigm for learning , 1988 .

[79]  A. Magurran.  Ecological Diversity and Its Measurement , 1990, Springer Netherlands.

[80]  Michael F. Barnsley,et al.  Fractals everywhere , 1988 .

[81]  J. Bonner.  The Evolution of Complexity by Means of Natural Selection , 1988 .

[82]  S. K. Park,et al.  Random number generators: good ones are hard to find , 1988, CACM.

[83]  K. Lang,et al.  Learning to tell two spirals apart , 1988 .

[84]  John H. Miller,et al.  Two essays on the economics of imperfect information , 1988 .

[85]  Wei-Min Shen,et al.  Functional transformations in AI discovery systems , 1988, [1988] Proceedings of the Twenty-First Annual Hawaii International Conference on System Sciences. Volume III: Decision Support and Knowledge Based Systems Track.

[86]  B. Widrow,et al.  The truck backer-upper: an example of self-learning in neural networks , 1989, International 1989 Joint Conference on Neural Networks.

[87]  G. Wolf,et al.  on Parcella '88: Fourth International Workshop on Parallel Processing by Cellular Automata and Arrays , 1989 .

[88]  Richard D. Porter,et al.  M2 per unit of potential GNP as an anchor for the price level , 1989 .

[89]  D.E. Goldberg,et al.  Classifier Systems and Genetic Algorithms , 1989, Artif. Intell..

[90]  L. Darrell Whitley,et al.  The GENITOR Algorithm and Selection Pressure: Why Rank-Based Allocation of Reproductive Trials is Best , 1989, ICGA.

[91]  C.W. Anderson,et al.  Learning to control an inverted pendulum using neural networks , 1989, IEEE Control Systems Magazine.

[92]  Christian M. Ernst,et al.  Multi-armed Bandit Allocation Indices , 1989 .

[93]  Geoffrey E. Hinton.  Connectionist Learning Procedures , 1989, Artif. Intell..

[94]  John J. Grefenstette,et al.  A System for Learning Control Strategies with Genetic Algorithms , 1989, ICGA.

[95]  T. M. Humphrey.  Precursors of the P-Star Model , 1989 .

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

[97]  Lawrence S. Kroll.  Mathematica--A System for Doing Mathematics by Computer. , 1989 .

[98]  Rodney A. Brooks,et al.  A robot that walks; emergent behaviors from a carefully evolved network , 1989, Proceedings, 1989 International Conference on Robotics and Automation.

[99]  Peter M. Todd,et al.  Designing Neural Networks using Genetic Algorithms , 1989, ICGA.

[100]  Jan M. Zytkow,et al.  Data-Driven Approaches to Empirical Discovery , 1989, Artif. Intell..

[101]  Paul Manneville,et al.  Cellular Automata and Modeling of Complex Physical Systems , 1989 .

[102]  John R. Koza,et al.  Hierarchical Genetic Algorithms Operating on Populations of Computer Programs , 1989, IJCAI.

[103]  Olin H. Bray.  Generation and Application of Pseudorandom Sequences for Random Testing , 1990 .

[104]  L. Darrell Whitley,et al.  Genetic algorithms and neural networks: optimizing connections and connectivity , 1990, Parallel Comput..

[105]  John R. Koza,et al.  Concept Formation and Decision Tree Induction Using the Genetic Programming Paradigm , 1990, PPSN.

[106]  W. Fontana.  Functional self-organization in complex systems , 1990 .

[107]  Przemyslaw Prusinkiewicz,et al.  The Algorithmic Beauty of Plants , 1990, The Virtual Laboratory.

[108]  Rodney A. Brooks,et al.  Learning to Coordinate Behaviors , 1990, AAAI.

[109]  John R. Koza,et al.  Genetic breeding of non-linear optimal control strategies for broom balancing , 1990 .

[110]  In Schoenauer,et al.  Parallel Problem Solving from Nature , 1990, Lecture Notes in Computer Science.

[111]  P. Langley,et al.  Computational Models of Scientific Discovery and Theory Formation , 1990 .

[112]  Lawrence. Davis,et al.  Handbook Of Genetic Algorithms , 1990 .

[113]  E. Uberbacher,et al.  Pattern recognition in DNA sequences: The intron-exon junction problem , 1990 .

[114]  Stewart W. Wilson.  Perceptron redux: emergence of structure , 1990 .

[115]  Eugene H. Spafford,et al.  Computer Viruses--A Form of Artificial Life? , 1990 .

[116]  Stuart L. Anderson.  Random Number Generators on Vector Supercomputers and Other Advanced Architectures , 1990, SIAM Rev..

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

[118]  Norman H. Packard,et al.  A Genetic Learning Algorithm for the Analysis of Complex Data , 1990, Complex Syst..

[119]  Stephanie Forrest,et al.  Parallelism and programming in classifier systems , 1990 .

[120]  John R. Koza,et al.  A Hierarchical Approach to Learning the Boolean Multiplexer Function , 1990, FOGA.

[121]  H. McIntosh.  Wolfram's class IV automata and good life , 1990 .

[122]  Maja J. Matarić,et al.  A Distributed Model for Mobile Robot Environment-Learning and Navigation , 1990 .

[123]  Richard K. Belew,et al.  Evolving networks: using the genetic algorithm with connectionist learning , 1990 .

[124]  R. J. Bagley,et al.  Spontaneous emergence of a metabolism , 1990 .

[125]  John R. Koza,et al.  Genetically breeding populations of computer programs to solve problems in artificial intelligence , 1990, [1990] Proceedings of the 2nd International IEEE Conference on Tools for Artificial Intelligence.

[126]  Hendrik James Antonisse,et al.  A Grammar-Based Genetic Algorithm , 1990, FOGA.

[127]  W. Daniel Hillis,et al.  Co-evolving parasites improve simulated evolution as an optimization procedure , 1990 .

[128]  Nils J. Nilsson,et al.  The Mathematical Foundations of Learning Machines , 1990 .

[129]  Steen Rasmussen,et al.  The coreworld: emergence and evolution of cooperative structures in a computational chemistry , 1990 .

[130]  Ryszard S. Michalski,et al.  Machine learning: an artificial intelligence approach volume III , 1990 .

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

[132]  N. Packard,et al.  Extracting cellular automaton rules directly from experimental data , 1991 .

[133]  Karl Sims,et al.  Artificial evolution for computer graphics , 1991, SIGGRAPH.

[134]  Alexis P. Wieland,et al.  Evolving Controls for Unstable Systems , 1991 .

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

[136]  Yuval Davidor,et al.  Genetic algorithms and robotics , 1991 .

[137]  D. Fogel.  The evolution of intelligent decision making in gaming , 1991 .

[138]  Thomas S. Ray,et al.  An Approach to the Synthesis of Life , 1991 .

[139]  Thomas Bäck,et al.  A Survey of Evolution Strategies , 1991, ICGA.

[140]  Sio Carlos,et al.  Evolving a learning algorithm for the binary perceptron , 1991 .

[141]  John R. Koza,et al.  Genetic evolution and co-evolution of computer programs , 1991 .

[142]  Thomas G. Dietterich,et al.  Readings in Machine Learning , 1991 .

[143]  David J. Chalmers,et al.  The Evolution of Learning: An Experiment in Genetic Connectionism , 1991 .

[144]  Mitchel Resnick,et al.  Animal simulations with Logo: Massive parallelism for the masses , 1991 .

[145]  G. Kampis.  Self-modifying systems in biology and cognitive science , 1991 .

[146]  David R. Jefferson,et al.  Representations for artificial organisms , 1991 .

[147]  Tsu-Chang Lee,et al.  Structure level adaptation for artificial neural networks , 1991 .

[148]  Luc Steels,et al.  Towards a theory of emergent functionality , 1991 .

[149]  John R. Koza,et al.  Genetic generation of both the weights and architecture for a neural network , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.

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

[151]  Jean-Louis Deneubourg,et al.  The dynamics of collective sorting robot-like ants and ant-like robots , 1991 .

[152]  Phillip J. McKerrow,et al.  Introduction to robotics , 1991 .

[153]  Robert J. Schalkoff,et al.  Pattern recognition - statistical, structural and neural approaches , 1991 .

[154]  Jean-Arcady Meyer,et al.  Evolution and Co Evolution of Computer Programs to Control Independently Acting Agents , 1991 .

[155]  John R. Koza,et al.  Evolving a Computer Program to Generate Random Numbers Using the Genetic Programming Paradigm , 1991, ICGA.

[156]  Jean-Louis Deneubourg,et al.  Harvesting by a group of robots , 1992 .

[157]  Shlomo Geva,et al.  A one neuron truck backer-upper , 1992, [Proceedings 1992] IJCNN International Joint Conference on Neural Networks.

[158]  Maaruf Ali,et al.  The use of fractal theory in a video compression system , 1992, Data Compression Conference, 1992..

[159]  John R. Koza,et al.  Automatic Programming of Robots Using Genetic Programming , 1992, AAAI.

[160]  John R. Koza,et al.  Evolution of Food-Foraging Strategies for the Caribbean Anolis Lizard Using Genetic Programming , 1992, Adapt. Behav..

[161]  F. Varela,et al.  Toward a Practice of Autonomous Systems: Proceedings of the First European Conference on Artificial Life , 1992 .

[162]  Noam Nisan.  Using hard problems to create pseudorandom generators , 1992, ACM Distinguished Dissertations.

[163]  Christopher G. Langton,et al.  Artificial life II : video proceedings , 1992 .

[164]  John R. Koza,et al.  A Genetic Approach to Finding a Controller to Back Up a Tractor-Trailer Truck , 1992, 1992 American Control Conference.

[165]  John R. Koza.  Hierarchical Automatic Function Definition in Genetic Programming , 1992, FOGA.

[166]  Sridhar Mahadevan,et al.  Automatic Programming of Behavior-Based Robots Using Reinforcement Learning , 1991, Artif. Intell..

[167]  John R. Koza,et al.  A genetic approach to the truck backer upper problem and the inter-twined spiral problem , 1992, [Proceedings 1992] IJCNN International Joint Conference on Neural Networks.

[168]  M. Bernhard.  Introduction to Chaotic Dynamical Systems , 1992 .

[169]  John R. Koza,et al.  A GENETIC APPROACH TO ECONOMETRIC MODELING , 1992 .

[170]  Tom M. Mitchell,et al.  Learning by experimentation: acquiring and refining problem-solving heuristics , 1993 .

[171]  John R. Koza,et al.  Evolution of Subsumption Using Genetic Programming , 1993 .

[172]  N. Packard,et al.  A LEARNING ALGORITHM FOR OPTIMAL REPRESENTATION OF EXPERIMENTAL DATA , 1994 .

[173]  Ben J. A. Kröse,et al.  Learning from delayed rewards , 1995, Robotics Auton. Syst..

[174]  G. Perry,et al.  Anolis lizards of the Caribbean : ecology, evolution, and plate tectonics , 1997 .

[175]  U. Boscain,et al.  An Introduction to Optimal Control , 2022 .