Genetic Programming 1996: Proceedings of the First Annual Conference, July 28-31, 1996, Stanford University
John R. Koza is Consulting Associate Professor in the Computer Science Department at Stanford University.
Genetic programming is a domain-independent method for automatic programming that evolves computer programs that solve, or approximately solve, problems. Starting with a primordial ooze of thousands of randomly created computer programs composed of functions and terminals appropriate to a problem, a population of programs is progressively evolved over many generations using the Darwinian principle of survival of the fittest, a sexual recombination operation, and occasional mutation.
These proceedings of the first Genetic Programming Conference present the most recent research in the field of genetic programming as well as recent research results in the fields of genetic algorithms, evolutionary programming, and learning classifier systems.
Topics include: Applications of genetic programming. Theoretical foundations of genetic programming. Implementation issues. Technique extensions. Automated synthesis of analog electrical circuits. Automatic programming of cellular automata. Induction. System identification. Control. Evolution of machine language programs. Automatic programming of multi-agent strategies. Automated evolution of program architecture. Evolution of mental models. Implementations of memory and state. Cellular encoding. Evolvable hardware. Parallelization techniques. Relations to biology and cognitive systems. Genetic algorithms. Evolutionary programming. Evolution strategies. Learning classifier systems.
Bradford Books imprint
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Table of Contents
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Long Genetic Programming Papers
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Short Genetic Programming Papers
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Genetic Programming Poster Papers
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Long Genetic Algorithms Papers
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Short Genetic Algorithms Papers
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Genetic Algorithms Poster Papers
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Evolutionary Programming and Evolution Strategies Papers
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Classifier Systems Papers
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