Showing papers on "Genetic algorithm published in 1987"

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...

Genetic Placement

Abstract: A placement algorithm, Genie, is presented for the assignment of modules to locations on chips. Genie is an adaptation of the genetic algorithm technique that has traditionally been a tool of the artificial intelligence community. The technique is a paradigm for examining a state space. It produces its solutions through the simultaneous consideration and manipulation of a set of possible solutions. The manipulations resemble the mechanics of natural evolution. For example, solutions are "mated" to produce "offspring" solutions. Genie has been extensively run on a variety of small test instances. Its solutions were observed to be quite good and in several cases optimal.

Parallel genetic algorithms for a hypercube

Abstract: This paper discusses a parallel genetic algorithm for a medium-grained hypercube computer. Each processor runs the genetic algorithm on its own sub-population, periodically selecting the best individuals from the sub-population and sending copies of them to one of its neighboring processors. The performance of the parallel algorithm on a function maximization problem is compared to the performance of the serial version. The parallel algorithm achieves comparable results with near-linear speed-up. In addition, some experiments were performed to study the effects of varying the parameters for the parallel model.

Using the genetic algorithm to generate LISP source code to solve the prisoner's dilemma

Abstract: The genetic algorithm is adapted to manipulate Lisp S-expressions. The traditional genetic operators of crossover, inversion, and mutation are modified for the Lisp domain. The process is tested using the Prisoner's Dilemma. The genetic algorithm produces solutions to the Prisoner's Dilemma as Lisp S-expressions and these results are compared to other published solutions.

Computer-aided pipeline operation using genetic algorithms and rule learning. PART I: Genetic algorithms in pipeline optimization

Abstract: In this two-paper series, techniques connected with artificial intelligence and genetics are applied to achieve computer-based control of gas pipeline systems. In this, the first paper, genetic algorithms are developed and applied to the solution of two classical pipeline optimization problems, the steady serial line problem, and the single transient line problem. Simply stated, genetic algorithms are canonical search procedures based on the mechanics of natural genetics. They combine a Darwinian survival of the fittest with a structured, yet randomized, information exchange between artificial chromosomes (strings). Despite their reliance on stochastic processes, genetic algorithms are no simple random walk; they carefully and efficiently exploit historic information to guide future trials. In the two pipeline problems, a simple three-operator genetic algorithm consisting of reproduction, crossover, and mutation finds near-optimal performance quickly. In, the steady serial problem, near-optimal performance is found after searching less than 1100 of 1.1(1012) alternatives. Similarly, efficient performance is demonstrated in the transient problem. Genetic algorithms are ready for application to more complex engineering optimization problems. They also can serve as a searning mechanism in a larger rule learning procedure. This application is discussed in the sequal.

Stochastic iterated genetic hillclimbing

Abstract: In the "black box function optimization" problem, a search strategy is required to find an extremal point of a function without knowing the structure of the function or the range of possible function values. Solving such problems efficiently requires two abilities. On the one hand, a strategy must be capable of learning while searching: It must gather global information about the space and concentrate the search in the most promising regions. On the other hand, a strategy must be capable of sustained exploration: If a search of the most promising region does not uncover a satisfactory point, the strategy must redirect its efforts into other regions of the space. This dissertation describes a connectionist learning machine that produces a search strategy called stochastic iterated genetic hillclimbing (SIGH). Viewed over a short period of time, SIGH displays a coarse-to-fine searching strategy, like simulated annealing and genetic algorithms. However, in SIGH the convergence process is reversible. The connectionist implementation makes it possible to diverge the search after it has converged, and to recover coarse-grained information about the space that was suppressed during convergence. The successful optimization of a complex function by SIGH usually involves a series of such converge/diverge cycles. SIGH can be viewed as a generalization of a genetic algorithm and a stochastic hillclimbing algorithm, in which genetic search discovers starting points for subsequent hillclimbing, and hillclimbing biases the population for subsequent genetic search. Several search stratgies--including SIGH, hillclimbers, genetic algorithms, and simulated annealing--are tested on a set of illustrative functions and on a series of graph partitioning problems. SIGH is competitive with genetic algorithms and simulated annealing in most cases, and markedly superior in a function where the uphill directions usually lead away from the global maximum. In that case, SIGH's ability to pass information from one coarse-to-fine search to the next is crucial. Combinations of genetic and hillclimbing techniques can offer dramatic performance improvements over either technique alone.

Computer-aided pipeline operation using genetic algorithms and rule learning. PART II: Rule learning control of a pipeline under normal and abnormal conditions

Abstract: In this two-paper series, techniques connected with artificial intelligence and genetics are applied to the problem of gas pipeline control. In the first paper, genetic algorithms were applied to two pipeline optimization problems. In this, the second paper, genetic algorithms are used as a basic learning mechanism in a larger rule learning system called a learning classifier system. The learning classifier system is developed and applied to the control of a gas pipeline under normal summer and winter operations as well as abnormal operations during leak events.

An information theoretic interface for a stochastic model management system

Abstract: Groups using group decision support systems GDSS for addressing organizational problems is an evolutionary process. An analytical model incorporating evolutionary processes exists, capturing this adaptation in the group decision-making . process. This model is based on the genetic algorithm GA and can be used to estimate GA parameter values from experimental data. This research effort examines possible relationships between the GA crossover and mutation parameters and the group context variables of leadership. Both the presence of and the activity level of group leaders are considered. Particular attention is paid to model implementation for a specific instance of GDSS use. The results of this effort are generally encouraging, hinting at the need to conduct further research in this area. q 2000 Elsevier Science B.V. All rights reserved.

Genetic algorithms and their applications : proceedings of the Second International Conference on Genetic Algorithms, July 28-31, 1987, at the Massachusetts Institute of Technology, Cambridge, MA

Abstract: ContentsGenetic Search Theory. D.E. Goldberg, P. Segrest, Finite Markov Chain Analysis of Genetic Algorithms. C.L. Bridges, D.E. Goldberg, An Analysis of Reproduction and Crossover in a Binary-Coded Genetic Algorithm. J.E. Baker, Reducing Bias and Inefficiency in the Selection Algorithm. T.H. Westerdale, Adaptive Search Operators I.Altruism in the Bucket Brigade. I. StadnykSchema Recombination in Pattern Recognition Problems. J.D. Schaffer, A. Morishima, An Adaptive Crossover Distribution Mechanism for Genetic Algorithms. D.E. Goldberg, J. Richardson, Representation Issues.Genetic Algorithms With Sharing for Multimodal Function Optimization. C.G. Shaefer, The ARGOT Strategy: Adaptive Representation Genetic Optimizer Technique. D.E. Goldberg, R.E. Smith, Nonstationary Function Optimization Using Genetic Algorithms With Dominance and Diploidy. H.J. Antonisse, K.S. Keller, Genetic Operators for High-Level Knowledge Representations. A.S. Bickel, R.W. Bickel, Keynote Address.Tree Structured Rules in Genetic Algorithms. J.H. Holland, Adaptive Search Operators II.Genetic Algorithms and Classifier Systems: Foundations and Future Directions. G.E. Liepins, M.R. Hilliard, M. Palmer, M. Morrow, Greedy Genetics. J.Y. Suh, D. Van Gucht, Incorporating Heuristic Information Into Genetic Search. D. Whitley, Using Reproductive Evaluation to Improve Genetic Search and Heuristic Discovery. D.J. Sirag, P.T. Weisser, Connectionism and Parallelism I.Toward a Unified Thermodynamic Genetic Operator. C.P. Dolan, M.G. Dyer, Toward the Evolution of Symbols. D.G. Oosthuizen, SUPERGRAN: A Connectionist Approach to Learning, Integrating, Genetic Algorithms and Graph Induction. G.G. Robertson, Parallel Implementation of Genetic Algorithms in a Classifier System. J.P. Cohoon, S.U. Hegde, W.N. Martin, D. Richards, Parallelism II.Punctuated Equilibria: A Parallel Genetic Algorithm. C.B. Pettey, M.R. Leuze, J.J. Grefenstette, A Parallel Genetic Algorithm. A.V. Sannier, II, E.D. Goodman, Genetic Learning Procedures in Distributed Environments. P. Jog, D. Van Gucht, Parallelisation of Probablistic Sequential Search Algorithms. R. Tanese, Credit Assignment and Learning.Parallel Genetic Algorithms for a Hypercube. R.L. Riolo, Bucket Brigade Performance: I. Long Sequences of Classifiers. R.L. Riolo, Bucket Brigade Performance: II. Default Hierarchies. J.J. Grefenstette, Multilevel Credit Assignment in a Genetic Learning System. K.A. De Jong, Applications I.On Using Genetic Algorithms to Search Program Spaces. D.P. Greene, S.F. Smith, A Genetic System for Learning Models of Consumer Choice. I.M. Oliver, D.J. Smith, J.R.C. Holland, A Study of Permutation Crossover Operators on the Traveling Salesman Problem. M.R. Hilliard, G.E. Liepins, M. Palmer, M. Morrow, J. Richardson, A Classifier Based System for Discovering Scheduling Heuristics. C. Fujiko, J. Dickinson, Applications II.Using the Genetic Algorithm to Generate LISP Source Code to Solve the Prisoner's Dilemma. V.V. Raghavan, B. Agarwal, Optimal Determination of User- Oriented Clusters: An Application for the Reproductive Plan. S.W. Wilson, The Genetic Algorithm and Biological Development. L. Davis, S. Coombs, Genetic Algorithms and Communication Link Speed Design: Theoretical Considerations. S. Coombs, L. Davis, Genetic Algorithms and Communication Link Speed Design: Constraints and Operators.