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Department of Computer Science
University College, The University of New South Wales
Australian Defence Force Academy, Canberra, ACT 2600, Australia
Last modified Thu Sep 21 10:43:35 1995
During the last three decades there has been a growing interest in algorithms which rely on analogies to natural processes. The emergence of massively parallel computers made these algorithms of practical interest. The best known algorithms in this class include evolutionary programming, genetic algorithms, evolution strategies, simulated annealing, classifier systems, and neural networks.
During the tutorial we discuss a subclass of these algorithms --- those which are based on the principle of evolution (survival of the fittest). In such algorithms a population of individuals (potential solutions) undergoes a sequence of unary (mutation type) and higher order (crossover type) transformations. These individuals strive for survival: a selection scheme, biased towards fitter individuals, selects the next generation. After some number of generations, the program converges --- the best individual represents near-optimum solution. A common term, recently accepted, refers to such techniques as `evolutionary computation' methods.
Evolutionary computation methods include many different techniques like genetic algorithms, evolution strategies, evolutionary programming, genetic programming, classifier systems, learning systems, and many others. The tutorial will provide with technical aspects of these evolutionary computation techniques. We will discuss also a historical background of evolutionary computation methods and their motivations, the major trends in the field and address some current critical issues.