Proceedings of the Second Nordic
Workshop on Genetic Algorithms and
their Applications
(2NWGA)

21. - 23. August 1996, Vaasa, Finland

Editor: Jarmo T. Alander

Department of Information Technology and Production Economics
University of Vaasa
P. O. Box 700, FIN-65101 Vaasa, Finland

to 1.866in

In collaboration with the Finnish Artificial Intelligence Society

©1996 University of Vaasa and the authors.

This book was printed and bound in Finland.

Available via anonymous ftp from ftp.uwasa.fi as compressed POSTSCRIPT -files in directory cs/2NWGA. Each article is in a separate file named by one of the authors + file main.ps.Z, which contains titlepages, table of contents etc.

Available via www as http://lipas.uwasa.fi/cs/publications/2NWGA.html

All rights reserved. No part of this book may be reproduced in any form - by photoprinting, microfilm, or any other means - nor transmitted or translated into a machine language without permission from the corresponding authors.

It is OK to print a personal copy for private use, however.

Neither University of Vaasa nor Finnish Artificial Intelligence Society shall be responsible for statements or opinions printed in this publication.

main

• Contents
• List of Figures
• List of Tables
• Contributed Papers
  • Population Size in GAs for TSP
    • Introduction
      • Related work
    • The Population Size Estimate
    • A GA for the Traveling Salesman Problem
    • Testing the population size estimate
    • Conclusion
    • Bibliography
  • Adaptive Operator Probabilities
    • Introduction
    • ADOPP - Adaptive Operator Probabilities
    • Performance
      • Test Problems
      • Varying the Initial Operator Probabilities
      • Changing the Definition of Improved
      • Changing the Assignment of Credit
    • Conclusion
    • Bibliography
  • Crossover Landscape for Onemax
    • Introduction
    • Notation
    • Genetic algorithm and neighbourhood search
      • Crossover and complementary crossover
      • Linear recombination
    • Multimodal problems
    • The secondary effect of recombination
    • Hamming landscape versus CX landscape
    • Conclusions
      • Acknowledgement
    • Bibliography
  • Empirical comparison of stochastic algorithms
    • Introduction
    • Stochastic algorithms
    • Experimental setting
      • The problem
      • The algorithms
    • Results
    • Conclusions and future
      • Acknowledgments
    • Bibliography
  • Parallel GAs for Multiobjective Functions
    • Introduction and overview
    • Optimising multiobjective functions
    • GA methods
    • Parallel (diffusion) GAs
    • The sensitivity analysis problem
    • Experimental results
    • Conclusions
    • Bibliography
  • Hardware Implementation of GA
    • Introduction
      • Related work
    • Hardware implementation
      • Random number generation
      • Mutation
      • Operation of the Pipeline
    • Simulations and performance
    • Conclusions and future
    • Bibliography
  • Prediction of sunspots by GP
    • Introduction
      • Related work
    • Time series prediction
    • Genetic Programming
    • Training
    • Experiments
    • Conclusions and future
      • Acknowledgements
    • Bibliography
  • A Genetic Approach to a Nesting Problem
    • Introduction
      • Related work
    • Problem formulation and fitness function
      • The MMP
      • Objective Function
      • Shape description
    • Genetic approach
      • Linear structure associated to tree structure
      • Initial population
      • Operators
      • Mutation
      • Population renewal and selection
    • Experiments
      • Discussion
    • Conclusions and future
    • Figures
    • Bibliography
  • Searching for Shortest Common Supersequences
    • Introduction
    • The genetic algorithm
    • Results
      • Results on random strings
      • Results on similar strings
      • Results on special sets of strings
    • Conclusion and future work
      • Acknowledgement
    • Bibliography
  • The effectiveness of recombination
    • Introduction
    • Methods
      • Building block processing.
      • The structure elucidation problem.
      • Simulated annealing.
      • Statistical evaluation of results.
      • The random crossover operator (RXO).
    • Results and discussion
    • Conclusion
      • Acknowledgments
    • Bibliography
  • Ligand Recognition with GAs
    • Introduction
    • GA-based concept learning and multimodality
    • The ligand recognition problem
    • Knowledge representation
      • The Language Template.
      • Constructing the bit strings.
      • Expressing disjuncts.
    • JGA's architecture
    • Genetic and family operators
      • Genetic operators
      • Family operators
      • Fitness Function
    • Learning ligand recognition concepts
      • Restricting the language for the ligand recognition domain
      • Two learning modes
      • The experiments
    • Description of the results
    • Conclusions
    • Bibliography
  • Identification of multivariate Volterra series using GA
    • Introduction
    • Volterra series approximation
    • Genetic Algorithm
      • Coding scheme
      • Population initialization
      • Fitness evaluation
      • Selection method
      • Crossover and mutation
    • Test problem
    • Results
    • Conclusions
    • Bibliography
  • Lean Artificial Neural Networks
    • Introduction
    • Complexity regularization and fitness
      • Complexity Regularization
      • Fitness Functions
    • The netGEN System
    • ANN Genotype Representation
      • GA and ANN Parameters
    • Benchmark Problem
    • Discussion
    • Conclusions and Future
      • Acknowledgements
    • Bibliography
  • Predicting sunspot numbers
    • Introduction
    • Methods
    • Results
    • Conclusions
    • Bibliography
• Extended Abstracts
  • Industrial applications at Siemens AG
    • Core Reload Design for Pressurized Water Reactors
      • The problem
      • The solution concept
      • Working in parallel on workstation clusters
      • The evolutionary algorithm
      • Results and Know-How Transfer
      • Future work
    • Evolution of Neuro-Fuzzy controllers
    • Conclusions
    • Bibliography
  • Shape optimization by GA
    • Introduction
      • Diesel fuel injection equipment
      • Cam design
      • Related work
    • Cam shape design using genetic algorithm
      • Evolutionary cam shape optimization process
      • The genetic algorithm
    • Computing environment
      • Hardware
      • Software
    • Preliminary results
    • Conclusions
    • Bibliography
  • GA in program testing
    • Introduction
      • Related work
    • Theory
    • Pilot program
    • Conclusions and future
    • Bibliography
  • 2nd order equation
    • Introduction
    • lil-gp genetic programming system
    • Results
    • Conclusions
    • Bibliography
  • Separation Process Synthesis with GA
    • Introduction
    • Separation system synthesis
    • Genetic algorithm
    • Results of the genetic optimization
    • Conclusions
    • Bibliography
  • Genes, Codes,
    and Dynamic Systems
    • Introduction
      • Genetic algorithms
      • About this paper
      • Models of computation
    • `Programmable neural network'
      • About computability
      • Network realization
      • Construction of the matrix A
    • Program example
      • `Program gradient'
    • Discussion
      • GA applications
      • The role of control engineering
    • Bibliography
  • Wood procurement planning
    • Introduction
      • Object of the study
    • Aim of the study
    • Material and methods
    • Timetable
    • Conclusions
    • Bibliography
  • Electric Machine Design by Fuzzy Fitness
    • Introduction
    • Dimensioning electrical machines
    • Electric machine optimization
    • Fuzzy fitness function
      • Membership function as fuzzy constraint
      • Emphasizing important parameters
      • Forming the Fitness
    • Experiments
    • Conclusion and future plans
    • Bibliography
  • Unit commitment
    • Introduction
      • Problem formulation
    • Different solution methods
      • Simplex algorithm with exclusive condition
      • Simplex algorithm with penalty function
      • Lp_solve
      • Interior point algorithm
      • Genetic algorithms
    • Results
    • Conclusions
    • Bibliography
  • Risk-Sensitive Control of ATM Traffic
    • Aggregated Flows in ATM Networks
    • Time Series Modelling of Traffic Flows
      • Entropy Reformulation of Network Flows
      • Multivariate Smoothing of Statistical ATM Flows
    • Genetic Algorithm Mapping
      • Risk-Sensitive Analysis of Batch Control
      • Smoothing Control of Multiclass Flows
    • Experiment
    • Concluding Remarks
    • Bibliography
  • Time in Genetic Algorithms
    • Introduction
    • Temporal Probabilistic Logic
      • Temporal Logic
      • Probabilistic Logic
    • Genetic algorithm objects
    • Population characteristics
    • Convergence and optima
    • Conclusions
    • Bibliography
• Abstracts
  • Pareto Optimization
  • Optimal population size
    • Discussion
  • Permanent magnet modelling
  • Fuzzy controller
    • Acknowledgements
    • Bibliography
• Bibliography
  • GA research volume
    • Introduction
    • Methods
    • Annual research volumes
      • Discussion
    • Conclusions and future
    • Bibliography
  • Nordic GA research
    • Introduction
    • Statistical summaries
      • Geographical distribution
      • Annual distribution
      • Subjects of research
    • Indexes
      • Books
      • Journal articles
      • Theses
      • Report series
    • Nordic GA authors
    • Subject index
    • Geographical index
    • Annual index
• References
• Index