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Abstract: A genetic algorithm (GA) is a stochastic search technique based on the principles of biological evolution, natural selection, and genetic recombination, simulating "survival of the fittest" in a population of potential solutions or individuals. GAs are capable of globally exploring a solution space, pursuing potentially fruitful paths while also examining random points to reduce the likelihood of settling for a local optimum. They are conceptually simple yet computationally powerful, making them attractive for use in complex domains, and have been demonstrated on a wide variety of problems. We begin by describing the basic genetic algorithm within the framework of a simple illustrative example. We will then examine some of the issues which govern genetic algorithm design decisions and the trade-offs which have given rise to variations on the basic algorithm. Applications of GA's to more complex problems will be discussed, including a brief introduction to Genetic Programming.

Speaker's Biography: Professor V. Rao Vemuri is in the Department of Applied Science, Graduate Group of Computer Science, and Graduate Group in Biomedical Engineering at the University of California-Davis. He has 20 years of academic experience and 9 years of industrial experience. He is the author of six books and over sixty journal publications. His research areas include modeling, simulation, numerical methods, neural networks, genetic algorithms and their applications to signal processing, digital communications, optimization, control systems and user interface design. He is a member of ACM, senior member of IEEE, and a former Editor-in-Chief of the Computer Society Press. He is now an ACM Lecturer in ACM's Outreach Program.

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