Summary and Conclusions
We are in the early stages of building a comprehensive, integrated optimization and modeling system to handle complex industrial problems. We believe a combination of machine-code-based, LGP (for modeling) and ES CDSA (for optimization) together provides the best combination of available tools and algorithms for this task.
By conceiving of design optimization projects as integrated modeling and optimization problems from the outset, we anticipate that engineers and researchers will be able to extend the range of problems that are solvable, given today\rss technology.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bäck T., Schwefel, H.P. (1993) An Overview of Evolutionary Algorithms for Parameter Optimization, Evolutionary Computation, 1(1):1–23.
Banzhaf, W., Nordin, P., Keller, R., Francone, F. (1998) Genetic Programming, An Introduction, Morgan Kauffman Publishers, Inc., San Francisco, CA.
Deschaine, L.M. (2000) Tackling real-world environmental challenges with linear genetic programming. PCAI Magazine, 15(5):35–37.
Deschaine, L.M., Patel, J.J., Guthrie, R.G., Grumski, J.T., Ades, M.J. (2001) “Using Linear Genetic Programming to Develop a C/C++ Simulation Model of a Waste Incinerator,” The Society for Modeling and Simulation International: Advanced Simulation Technology Conference, Seattle, WA, USA, ISBN: 1-56555-238-5, pp. 41–48.
Deschaine, L.M., Hoover, R.A. Skibinski, J. (2002) Using Machine Learning to Complement and Extend the Accuracy of UXO Discrimination Beyond the Best Reported Results at the Jefferson Proving Grounds, (in press), Proceedings of Society for Modeling and Simulation International.
Fausett, L.V. (2000). A Neural Network Approach to Modeling a Waste Incinerator Facility, Society for Computer Simulation’s Advanced Simulation Technology Conference, Washington, DC, USA.
Fogel, D.B. (1992) Evolving Artificial Intelligence. Ph.D. thesis, University of California, San Diego, CA.
Francone, F., Nordin, P., and Banzhaf. W. (1996) Benchmarking the Generalization Capabilities of a Compiling Genetic Programming System Using Sparse Data Sets. In Koza et al. Proceedings of the First Annual Conference on Genetic Programming, Stanford, CA.
Francone F. (2002) Comparison of Discipulus‡ Genetic Programming Software with Alternative Modeling Tools. Available at www.aimlearning.com.
Fukunaga, A., Stechert, A., Mutz, D. (1998) A Genome Compiler for High Performance Genetic Programming. In Proceedings of the Third Annual Genetic Programming Conference, pp. 86–94, Morgan Kaufman Publishers, Jet Propulsion Laboratories, California Institute of Technology, Pasadena, CA.
Government Accounting Office (2001) DOD Training Range Clean-up Cost Estimates are Likely Understated, Report to House of Representatives on Environmental Liabilities, USA General Accounting Office, April, Report no. GAO 01 479.
Hansen, N., Ostermeier, A. (2001) Completely derandomized self-adaptation in evolution strategies. Evolutionary Computation 9(2):159–195.
Jefferson Proving Grounds (1999) Jefferson Proving Grounds Phase IV Report: Graph ES-1, May, Report No: SFIM-AEC-ET-CR-99051.
Koza, J., Bennet, F., Andre, D., and Keane, M. (1999) Genetic Programming III. Morgan Kaufman, San Francisco, CA.
Nordin, J.P. (1994) A Compiling Genetic Programming System that Directly Manipulates the Machine Code. In Advances in Genetic Programming, K. Kinnear, Jr. (ed.), MIT Press: Cambridge MA.
Nordin, J.P. (1999). Evolutionary Program Induction of Binary Machine Code and its Applications, Krehl Verlag.
Nordin, J.P., Banzhaf, W. (1995). Complexity Compression and Evolution. In Proceedings of Sixth International Conference of Genetic Algorithms, Morgan Kaufmann Publishers, Inc.
Nordin, J.P., Banzhaf, W. (1995). Evolving Turing Complete Programs for a Register Machine with Self Modifying Code. In Proceedings of Sixth International Conference of Genetic Algorithms, Morgan Kaufmann Publishers, Inc.
Nordin, J.P., Francone, F., and Banzhaf, W. (1996) Explicitly Defined Introns and Destructive Crossover in Genetic Programming. Advances in Genetic Programming 2, K. Kinnear, Jr. (ed.), MIT Press: Cambridge, MA.
Nordin, J.P., Francone, F., and Banzhaf, W. (1998) Efficient Evolution of Machine Code for CISC Architectures Using Blocks and Homologous Crossover. In Advances in Genetic Programming 3, MIT Press, Cambridge, MA.
Quinlan, R. (1998) Data Mining Tools See5 and C5.0. Technical report, RuleQuest Research.
Register Machine Learning Technologies, Inc. (2002) Discipulus Users Manual, Version 3.0. Available at www.aimlearning.com.
Brian S. Rice and Robert L. Walton of Eastman Kodak. Company, Industrial Production Data Set.
Scientific American (Nov. 1999). Drop Experiment to Demonstrate a Chaotic Time Series.
Rechenberg, I. (1994). Evolutionsstrategie’ 93, Fromann Verlag, Stuttgart, Germany.
Schwefel, H.P. (1995). Evolution and Optimum Seeking. Sixth-Generation Computer Technology Series, John Wiley & Sons, New York.
Schwefel, H.P. and Rudolph, G. (1995). Contemporary Evolution Strategies. In Advances in Artificial Life, pp. 893–907, Springer-Verlag, Berlin.
Vapnick V. (1998) The Nature of Statistical Learning Theory, Wiley-Interscience Publishing.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag London Limited
About this chapter
Cite this chapter
Deschaine, L.M., Francone, F. (2005). Extending the Boundaries of Design Optimization by Integrating Fast Optimization Techniques with Machine Code Based, Linear Genetic Programming. In: Wu, X., Jain, L., Graña, M., Duro, R.J., d’Anjou, A., Wang, P.P. (eds) Information Processing with Evolutionary Algorithms. Advanced Information and Knowledge Processing. Springer, London. https://doi.org/10.1007/1-84628-117-2_2
Download citation
DOI: https://doi.org/10.1007/1-84628-117-2_2
Publisher Name: Springer, London
Print ISBN: 978-1-85233-866-4
Online ISBN: 978-1-84628-117-4
eBook Packages: Computer ScienceComputer Science (R0)