Abstract
This paper describes an alternative approach to Genetic Programming (GP) for engineering design model development. The algorithm is initially developed to solve Boolean induction and simple symbolic regression problems within a discrete search space. This technique, called “DRAM-GP” (i.e. Distributed, Rapid, Attenuated Memory GP), is based upon a steady state population utilising a novel constrained complexity crossover operator. Node complexity weightings are introduced to provide a basis for speciation. Separate species of solutions, classified by complexity can be established which act as discrete GP sub-populations communicating with each other via crossover. The technique is extended to incorporate both continuous and discrete search spaces (HDRAM-GP” i.e. Hybrid DRAM-GP). HDRAM-GP includes a real numbered Genetic Algorithm (GA) to aid search in the continuous space. Its application is demonstrated on engineering fluid dynamics systems.
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
Iba, H., DeGaris, H. & Sato, T. A. 1996; A Numerical Approach to Genetic Programming for System Identification. Evolutionary Computation 3 (4): p417–452.
Koza, J. 1992; Genetic Programming; MIT Press.
Koza,J. 1994; Genetic Programming II, MIT Press.
Kinnear Jr. K. E. 1993, Generality and difficulty in Genetic Programming: Evolving a Sort. Proc. of 5th International Joint Conference on Genetic Algorithms.
Iba, H.; Sato, T. & De Garis, H. 1993; System Identification Approach to Genetic Programming. Proceedings of the 5th international conference on genetic algorithms: p279–286. San Mateo, CA: Morgan Kaufmann.
Kinnear Jr. K. E. 1993, Evolving a Sort: Lessons in Genetic Programming Proc. of 1993 International Conference on Neural Networks. IEEE Press.
Kinnear Jr. K. E, Advances in Genetic Programming, MIT Press, 1994.
Watson, A. H.; Parmee, I. C. 1996, Systems Identification Using Genetic Programming. Proceedings of ACEDC: p248–255. University of Plymouth.
Watson, A. H.; Parmee, I. C. 1996, Identification Of Fluid Systems Using Genetic Programming. Proceedings EUFIT ‘96. Vol I, p395–399. ELITE Foundation.
Watson, A. H.; Parmee, I. C. 1997, Steady State Genetic Programming with Constrained Complexity Crossover. Proceedings of Genetic Programming ‘97 (GP97): in publication. MIT Press.
Parmee, I.C. 1996, The Development Of A Dual-Agent Strategy For Efficient Search Across Whole System Engineering Design Hierarchies. Proceedings of Parallel Problem Solving from Nature. (PPSN IV), Lecture notes in Computer Science No. 1141: p523–532. Springer-Verlag, Berlin.
Watson, A. H.; Parmee, I. C. 1997, An Improved Genetic Programming Strategy For Preliminary Design Model Development. Proceedings EUFIT ‘97. Vol I, p682–686. ELITE Foundation.
Haaland, S.E.; Simple And Explicit Formulas For The Friction Factor In Turbulent Pipe Flow. Journal of Fluids Engineering 105. 1983.
Watson, A.H.; Parmee, I. C. Steady State Genetic Programming with Constrained Complexity Crossover Using Species Sub-Populations. Procs. 7th International Conference on Genetic Algorithms. (ICGA 97). Morgan Kaufmann. 1997(b).
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1998 Springer-Verlag London Limited
About this paper
Cite this paper
Watson, A.H., Parmee, I.C. (1998). Improving Engineering Design Models Using An Alternative Genetic Programming Approach. In: Parmee, I.C. (eds) Adaptive Computing in Design and Manufacture. Springer, London. https://doi.org/10.1007/978-1-4471-1589-2_15
Download citation
DOI: https://doi.org/10.1007/978-1-4471-1589-2_15
Publisher Name: Springer, London
Print ISBN: 978-3-540-76254-6
Online ISBN: 978-1-4471-1589-2
eBook Packages: Springer Book Archive