Abstract
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.
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Roy, R., Tiwari, A. (2002). Generalised Regression GA for Handling Inseparable Function Interaction: Algorithm and Applications. In: Guervós, J.J.M., Adamidis, P., Beyer, HG., Schwefel, HP., Fernández-Villacañas, JL. (eds) Parallel Problem Solving from Nature — PPSN VII. PPSN 2002. Lecture Notes in Computer Science, vol 2439. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45712-7_44
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DOI: https://doi.org/10.1007/3-540-45712-7_44
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