An Introduction to Evolutionary Programming

Evolutionary programming is a method for simulating evolution that has been investigated for over 30 years. This paper offers an introduction to evolutionary programming, and indicates its relationship to other methods of evolutionary computation, specifically genetic algorithms and evolution strategies. The original efforts that evolved finite state machines for predicting arbitrary time series, as well as specific recent efforts in combinatorial and continuous optimization are reviewed. Some areas of current investigation are mentioned, including empirical assessment of the optimization performance of the technique and extensions of the method to include mechanisms to self-adapt to the error surface being searched.

[1]  D. Fogel Evolutionary algorithms in theory and practice , 1997, Complex..

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

[3]  Charles E. Taylor,et al.  Artificial Life II , 1991 .

[4]  Heinz Mühlenbein,et al.  Evolution in Time and Space - The Parallel Genetic Algorithm , 1990, FOGA.

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

[6]  David B. Fogel,et al.  An introduction to simulated evolutionary optimization , 1994, IEEE Trans. Neural Networks.

[7]  David B. Fogel,et al.  Meta-evolutionary programming , 1991, [1991] Conference Record of the Twenty-Fifth Asilomar Conference on Signals, Systems & Computers.

[8]  K. E. Kinnear,et al.  Evolving a sort: lessons in genetic programming , 1993, IEEE International Conference on Neural Networks.

[9]  David B. Fogel,et al.  Evolving Behaviors in the Iterated Prisoner's Dilemma , 1993, Evolutionary Computation.

[10]  Hans L. And Darrell R. Moore Oestreicher Cybernetic Problems in Bionics , 1968 .

[11]  David B. Fogel,et al.  On the Relationship between the Duration of an Encounter and the Evolution of Cooperation in the Iterated Prisoner's Dilemma , 1995, Evolutionary Computation.

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

[13]  Peter J. Angeline,et al.  An evolutionary algorithm that constructs recurrent neural networks , 1994, IEEE Trans. Neural Networks.

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

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

[16]  Larry J. Eshelman,et al.  On Crossover as an Evolutionarily Viable Strategy , 1991, ICGA.

[17]  D. Fogel,et al.  Evolving continuous behaviors in the Iterated Prisoner's Dilemma. , 1996, Bio Systems.

[18]  Thomas Bäck,et al.  An Overview of Evolutionary Algorithms for Parameter Optimization , 1993, Evolutionary Computation.

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

[20]  D. B. Fogel,et al.  Applying evolutionary programming to selected control problems , 1994 .

[21]  M Conrad,et al.  The artificial worlds approach to emergent evolution. , 1989, Bio Systems.

[22]  Kristian Lindgren,et al.  Evolutionary phenomena in simple dynamics , 1992 .

[23]  D. B. Fogel,et al.  AN INFORMATION CRITERION FOR OPTIMAL NEURAL NETWORK SELECTION , 1990, 1990 Conference Record Twenty-Fourth Asilomar Conference on Signals, Systems and Computers, 1990..

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

[25]  David E. Goldberg,et al.  Genetic and evolutionary algorithms come of age , 1994, CACM.

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

[27]  L. C. Stayton,et al.  On the effectiveness of crossover in simulated evolutionary optimization. , 1994, Bio Systems.

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

[29]  D. Fogel Applying evolutionary programming to selected traveling salesman problems , 1993 .

[30]  W. Hamilton,et al.  The Evolution of Cooperation , 1984 .

[31]  Hans-Paul Schwefel,et al.  Numerical Optimization of Computer Models , 1982 .

[32]  Donald E. Waagen,et al.  Evolving recurrent perceptrons for time-series modeling , 1994, IEEE Trans. Neural Networks.

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

[34]  Mark D. Smucker,et al.  Iterated Prisoner's Dilemma with Choice and Refusal of Partners: Evolutionary Results , 1995, ECAL.

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

[36]  Patrick K. Simpson,et al.  Dynamic Feature Set Training of Neural Nets for Classification , 1995, Evolutionary Programming.

[37]  Wirt Atmar,et al.  Notes on the simulation of evolution , 1994, IEEE Trans. Neural Networks.