The Practical Handbook of Genetic Algorithms: New Frontiers, Volume 2
Lance D. Chambers
CRC Press, Aug 15, 1995 - Mathematics - 448 pages
The mathematics employed by genetic algorithms (GAs)are among the most exciting discoveries of the last few decades. But what exactly is a genetic algorithm? A genetic algorithm is a problem-solving method that uses genetics as its model of problem solving. It applies the rules of reproduction, gene crossover, and mutation to pseudo-organisms so those "organisms" can pass beneficial and survival-enhancing traits to new generations. GAs are useful in the selection of parameters to optimize a system's performance. A second potential use lies in testing and fitting quantitative models. Unlike any other book available, this interesting new text/reference takes you from the construction of a simple GA to advanced implementations. As you come to understand GAs and their processes, you will begin to understand the power of the genetic-based problem-solving paradigms that lie behind them.
What people are saying - Write a review
We haven't found any reviews in the usual places.
Artificial Neural Network Evolution Learning to Steer a Land Vehicle
Locating Putative Protein Signal Sequences
Selection Methods for Evolutionary Algorithms
Parallel Cooperating Genetic Algorithms An Application to Robot Motion Planning
The Boltzmann Selection Procedure
Structure and Performance of FineGrain Parallelism in Genetic Search
Parameter Estimation for a Generalized Parallel Loop Scheduling Algorithm
A Hybrid Approach Using Neural Networks Simulation Genetic Algorithms and Machine Learning for RealTime Sequencing and Scheduling Problems
Vehicle Routing with Time Windows using Genetic Algorithms
Evolutionary Algorithms and Dialogue
Incorporating Redundancy and Gene Activation Mechanisms in Genetic search for Adapting to NonStationary Environments
Input Space Segmentation with a Genetic Algorithm for Generation of Rule Based Classifier Systems
An Indexed Bibliography of Genetic Algorithms
Controlling a Dynamic Physical Systems Using Genetic Based Learning Methods
adaptive ALVINN application approach Artificial Intelligence artificial neural networks average backpropagation binary bits Boltzmann selection chromosome chunk complex Computer Conference on Genetic convergence crossover customers D. E. Goldberg data sets dialogue dynamic encoding Engineering error metric evaluation evolution strategies evolutionary algorithms evolutionary programming evolved fgpGA fitness function fitness values fragments genetic algorithms genetic search GIDEON system Heuristic IEEE Transactions implementation individuals input International Conference Journal linear ranking LOLITA loop Machine Learning matches method mutation neural networks objective function operators optimization optimum output parallel genetic algorithm parameters partitioning performance Ph.D pole-cart system population probability vector problem Proceedings processors proportional selection proteins random recombination rules scaling scheduling algorithms scheduling overhead SEARCH algorithm search space selection pressure selection procedure sequence shown in Figure simulated annealing solutions obtained solve string structure subpopulations Tabu Search technique thesis total number University vector Vehicle Routing VRPTW Whitley
Page 24 - This work was supported in part by the UK Science and Engineering Research Council, under grant GR/D97757, and in part by the Applied Mathematical Sciences subprogram of the Office of Energy Research, US Department of Energy, under contract W-31-109-Eng-38. References  Khayri AM Ali. Or-parallel execution of Prolog on BC-Machine.