Proceedings of the First International Conference on Genetic Algorithms and Their Applications: July 24-26, 1985 at the Carnegie-Mellon University, Pittsburgh, Pa

Proceedings of the First International Conference on Genetic Algorithms and Their Applications: July 24-26, 1985 at the Carnegie-Mellon University, Pittsburgh, Pa

Proceedings of the First International Conference on Genetic Algorithms and Their Applications: July 24-26, 1985 at the Carnegie-Mellon University, Pittsburgh, Pa

Proceedings of the First International Conference on Genetic Algorithms and Their Applications: July 24-26, 1985 at the Carnegie-Mellon University, Pittsburgh, Pa

Synopsis

Computer solutions to many difficult problems in science and engineering require the use of automatic search methods that consider a large number of possible solutions to the given problems. This book describes recent advances in the theory and practice of one such search method, called Genetic Algorithms. Genetic algorithms are evolutionary search techniques based on principles derived from natural population genetics, and are currently being applied to a variety of difficult problems in science, engineering, and artificial intelligence.

Excerpt

Stephanie Forrest the University of Michigan Ann Arbor, Michigan

One common criticism of Classifier Systems is the low-level nature of their representations. in Classifier Systems information is stored as rules (classifiers) that have a very constrained format (binary bit strings). Low-level binary bit string representations support adaptive learning algorithms well (Holland, 75)(Holland, 80). However, it is difficult to interpret the behavior of these systems without a high-level interpreter that can code and de-code the ones and zeroes into more meaningful terms. in particular, although gross behaviors can be measured at various intervals using some fitness function it is difficult to chart how learning takes place or to determine what role is played by each component of the system. This feature of low-level representations makes it difficult to establish direct connections between the behavior of Classifier Systems and more common high-level symbolic representations used in artificial intelligence programs.

The research described in this paper addresses this criticism by demonstrating that Classifier Systems are capable of representing sophisticated high-level structures. This has been accomplished by selecting one class of knowledge representation paradigms (semantic networks) and showing how they can be implemented as a collection of Classifier System rules. the described system takes high-level semantic network descriptions as input and automatically translates them into a Classifier System representation. It also provides a "query processor" that takes high-level queries about the semantic network, translates them into a sequence of Classifier System operations, and translates the results of the queries back . . .

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