Generalised Regression GA for Handling Inseparable Function Interaction: Algorithm and Applications
Interaction among decision variables is inherent to a number of reallife engineering design optimisation problems. There are two types of variable interaction: inseparable function interaction and variable dependence. The aim of this paper is to present an Evolutionary Computing (EC) technique for handling complex inseparable function interaction, and to demonstrate its effectiveness using three case studies. The paper begins by devising a definition of inseparable function interaction, identifying the challenges and presenting a review of relevant literature. It then briefly describes Generalised Regression GA (GRGA) for handling complex inseparable function interaction in multiobjective optimisation problems. GRGA is applied to a complex test problem and two real-life engineering design optimisation case studies that exhibit complex inseparable function interaction. It is shown that GRGA exhibits better convergence and distribution of solutions than NSGA-II, which is a highperforming evolutionary-based multi-objective optimisation algorithm. The paper concludes by presenting the future research directions.
KeywordsDecision Variable Pareto Front Multiobjective Optimisation Problem Evolutionary Computing Local Front
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- 2.Taguchi, G.: System of experimental design. Clausing, D. (ed.), UNIPUB/Kraus International Publications, vol. 1 and 2, New York, USA (1987)Google Scholar
- 3.Phadke, M. S.: Quality engineering using robust design. Prentice-Hall International Inc., London, UK (1989)Google Scholar
- 4.Tiwari, A. and Roy, R.: Variable dependence interaction and multi-objective optimisation. Accepted for publication: Genetic and Evolutionary Computation Conference (GECCO-2002), New York (USA), 9–13 July (2002)Google Scholar
- 5.Harik, G. R.: Learning gene linkage to efficiently solve problems of bounded difficulty using genetic algorithms. PhD. thesis, Computer science and engineering, University of Michigan, USA (1997)Google Scholar
- 7.Beasley, D., Bull, D. and Martin, R.: An overview of genetic algorithms: Part 2, research topics. University computing, 15(4) (1993) 170–181Google Scholar
- 8.Muhlenbein, H. and Paab, G.: From recombination of genes to the estimation of distributions I. Binary parameters. In: Parallel Problem Solving from Nature IV (PPSN-IV), Lecture notes in computer science, 46-55, Springer-Verlag, Berlin, Germany (1996)Google Scholar
- 9.Tiwari, A., Roy, R., Jared, G. and Munaux, O.: Interaction and multi-objective optimisation. In: Spector, L., Goodman, E., Wu, A., Langdon, W. B., Voigt, H.-M., Gen, M., Sen, S., Dorigo, M., Pezeshk, S., Garzon, M. and Burke, E. (eds.). Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001), 671–678, Morgan Kaufmann Publishers, San Francisco, USA (2001)Google Scholar
- 10.Tiwari, A.: Evolutionary computing techniques for handling variable interaction in engineering design optimisation. PhD Thesis, School of Industrial and Manufacturing Science, Cranfield University, UK (2001)Google Scholar
- 11.Deb, K., Pratap, A., Agarwal, S. and Meyarivan, T.: A fast and elitist multi-objective genetic algorithm: NSGA-II. KanGAL Report No. 200002, Kanpur Genetic Algorithms Laboratory (KanGAL), Indian Institute of Technology (IIT), Kanpur, India (2000)Google Scholar
- 12.Tiwari, A., Roy, R., Jared, G. and Munaux, O.: Evolutionary-based techniques for real-life optimisation: Development and testing. Accepted for publication: Applied Soft Computing (ASC) Journal, Elsevier Science, Netherlands (2002)Google Scholar
- 13.Deb, K., Pratap, A. and Moitra, S.: Mechanical component design for multi-objective using elitist non-dominated sorting GA. In: Parallel Problem Solving from Nature V (PPSN-V), Lecture notes in computer science, 859-868, Springer-Verlag, Germany (2000)Google Scholar
- 14.Coello, C. A. C.: An empirical study of evolutionary techniques for multiobjective optimization in engineering design. PhD Thesis, Department of Computer Science, Tulane University, New Orleans, LA, USA (1997)Google Scholar