Evolutionary Computing: AISB Workshop, Brighton, U.K., April 1 - 2, 1996. Selected PapersThis book contains a selection of papers presented at a workshop on evolutionary computing sponsored by the Society for the Study of Artificial Intelligence and Simulation of Behaviour, AISB, at the University of Sussex in Brighton, UK, in April 1996. The 22 revised full papers included in the book, together with one invited contribution, were carefully reviewed by the program committee. Twelve contributions investigate applications of evolutionary computing in various areas, such as learning, scheduling, searching, genetic programming, image processing, and robotics. Eleven papers are devoted to evolutionary computing theory and techniques. |
Contents
Fast Evolutionary Learning of Minimal Radial Basis Function Neural Networks Using a Genetic Algorithm | 1 |
Evolutionary Design of Synthetic Routes in Chemistry | 23 |
A Genetic Algorithm for JobShop Problems with Various Schedule Quality Criteria | 39 |
Two Applications of Genetic Algorithms to Component Design | 50 |
Characterizing Signal Behaviour Using Genetic Programming | 62 |
Spatial Reasoning With Genetic Algorithms An Application in Planning of Safe Liquid Petroleum Gas Sites | 73 |
Restricted Evaluation Genetic Algorithms with Tabu Search for Optimising Boolean Functions as MultiLevel ANDEXOR Networks | 85 |
Generation of Structured Process Models Using Genetic Programming | 102 |
Global Selection Methods for Massively Parallel Computers | 175 |
Investigating Multiploidys Niche | 189 |
Evolutionary Divide and Conquer for the SetCovering Problem | 198 |
The Simulation of Localised Interaction and Learning in Artificial Adaptive Agents | 209 |
description intent and experimentation | 223 |
Adaptive Restricted Tournament Selection for the Identification of Multiple SubOptima in a MultiModal Function | 236 |
Analysis of Possible GenomeDependence of Mutation Rates in Genetic Algorithms | 257 |
Inoculation to Initialise Evolutionary Search | 269 |
Genetic Programming for Feature Detection and Image Segmentation | 110 |
A Temporal View of Selection and Populations | 126 |
Evolving Software Test Data GAs learn Self Expression | 137 |
Efficient Evolution Strategies for Exploration in Mobile Robotics | 147 |
Learning the Next Dimension | 162 |
CoEvolution of Operator Settings in Genetic Algorithms | 286 |
A Comparative Study of Steady State and Generational Genetic Algorithms for Use in Nonstationary Environments | 297 |
Author Index | |
Common terms and phrases
adaptation application approach approximation average behaviour best result binary blocks chromosome clustering co-evolution complexity Conference on Genetic convergence cooperation defined distributed effect encoded error evaluations Evolution Strategy evolutionary algorithms Evolutionary Computation evolved experiments Figure fitness function fitness landscape fitness values flowshop Fogarty genes genetic algorithm genetic operators genetic programming genome global haploid heuristic hidden layer nodes hybrid implemented individual initial population initialisation input interaction learning local search localisation method Moore machines Morgan Kaufmann multiploid mutation rate neighbourhood neural networks number of training optimisation optimization optimum output overall Parallel parameters parent Pc(i peaks performance pixels problem Proceedings processor RBF networks recombination representation route Royal Road RRR3 samples schedule schemata search space simulated simulated annealing solution stacks step string structure superquadric TABU TABU search techniques tournament selection trail training epochs variable ordering yield