Genetic Programming 1996: Proceedings of the First Annual Conference (Complex Adaptive Systems)

July 28-31, 1996 A· 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.

Complex Adaptive Systems series. A Bradford Book

John R. Koza is Consulting Professor of Computer Science at Stanford University. David E. Goldberg is Professor of General Engineering at the University of Illinois. David B. Fogel is Chief Scientist at Natural Selection, Inc. in La Jolla, California. Rick L. Riolo is Director of the Computer Laboratory of the Program for Study of Complex Systems at the University of Michigan.