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

In this paper, recent research resul ts [I] are presented which demonstrate the effectiveness of genetic algorithms in the control of dynamic systems. Genetic algorithms are search algorithms based upon the mechanics of natural genetics. They combine a survival-of-the-fittest among string structures with a structured, yet randomized, information exchange to form a search algorithm with some of the innovative flair of human search. While randomized, genetic algorithms are no simple random walk. They efficiently exploit historical information to speculate on new search points with improved performance. Two applications of genetic algorithms are considered. In the first, a tripartite genetic algorithm is applied to a parameter optimization problem, the optimization of a serial natural gas pipeline with 10 compressor stations. While solvable by other methods (dynamic programming, gradient search, etc.) the problem is interesting as a straightforward engineering application of genetic algorithms. Furthermore, a surprisingly small number of function evaluations are required (relative to the size of the discretized search space) to achieve nearoptimal performance. In the second application, a genetic algorithm is used as the fundamental learning algorithm in a more complete rule learning system called a learning classifier system. The learning system combines a complete string rule and message system, an apportionment of credit algorithm modeled after a competitive service econOlllY, and a genetic algorithm to form a system which continually evaluates its present rules while forming new, possibly better, rules from the bits and pieces of the old. In an application to the control of a natural gas pipeline, the learning system is trained to control the pipeline under normal winter and summer conditions. It is also trained to detect the presence or absence of a leak with increasing accuracy. Cover Title Page Copyright Page Table of Contents Wednesday, July 24, 1985 Session 1 Properties of the bucket brigade Genetic algorithms and rules learning in dynamic system control Knowledge growth in an artificial animal Session 2 Implementing semantic network structures using the classifier system The bucket brigade is not genetic Genetic plans and the probabilistic learning system: synthesis and results Session 3 Learning multiclass pattern discrimination Improving the performance of genetic algorithms in classifier systems Thursday, July 25, 1985 Session 4 Multiple objective optimization with vector evaluated genetic algorithms Adaptive selection methods for genetic algorithms Genetic search with approximate function evaluation Session 5 A connectionist algorithm for genetic search Job shop scheduling with genetic algorithms Compaction of symbolic layout using genetic algorithms Session 6 Alleles, loci, and the traveling salesman problem Genetic algorithms for the traveling salesman problem Genetic algorithms: a 10 year perspective, Friday, July 26, 1985 Session 7 Classifier systems with long term memory A representation for the adaptive generation of simple sequential programs Adaptive 'cortical' pattern recognition Machine learning of visual recognition using genetic algorithms Bin packing with adaptive search Directed trees method for fitting a potential function Index 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. Sponsored By Texas Instruments, Inc., Naval Research Laboratory ; John J. Grefenstette, Editor. Includes Bibliographies And Index.