Showing papers on "Genetic algorithm published in 1985"

Genetic Algorithms In Pipeline Optimization

Abstract: The application of a genetic algorithm to the steady state optimization of a serial liquid pipeline is considered. Genetic algorithms are search procedures based upon the mechanics of natural genet...

Multi-objective learning via genetic algorithms

Abstract: Genetic algorithms (GAs) are powerful, general purpose adaptive search techniques which have been used successfully in a variety of learning systems. In the standard formulation, GAs maintain a set of alternative knowledge structures for the task to be learned, and improved knowledge structures are formed through a combination of competition and knowledge sharing among the alternative knowledge structures. In this paper, we extend the GA paradigm by allowing multidimensional feedback concerning the performance of the alternative structures. The modified GA is shown to solve a multiclass pattern discrimination task which could not be solved by the unmodified GA.

Dynamic system control using rule learning and genetic algorithms

Abstract: In this paper, recent research results are presented which demonstrate the effectiveness of a rule learning system in two dynamic system control tasks. This system, called a learning classifier system (LCS), learns rules to control a simple Internal object and a simulated natural gas pipeline. Starting from a randomly generated state of mind, the learning classifier system learns string-rules called classifiers which match strings called messages. Messages are sent by environmental sensors or by previously activated classifiers. Each classifier's effectiveness is evaluated by an internal service economy complete with bidding and action. Furthermore, new rules are created by an innovative search mechanism called a genetic algorithm. Genetic algorithms are search algorithms based on the mechanicsm of natural genetics. Results from computational experiments in both tasks are presented. In the internal object task, the LCS learns an effective set of rules to center the object repeatedly. In the pipeline task, the LCS learns to control the pipeline under normal summer and winter conditions. It also learns to alarm correctly for the presence or absence of a leak. These results demonstrate the effectiveness of the learning classifier system approach and suggest further refinements which are currently under Investigation.