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Generalised Regression GA for Handling Inseparable Function Interaction: Algorithm and Applications

  • Rajkumar Roy
  • Ashutosh Tiwari
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2439)

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.

Keywords

Decision Variable Pareto Front Multiobjective Optimisation Problem Evolutionary Computing Local Front 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Rajkumar Roy
    • 1
  • Ashutosh Tiwari
    • 1
  1. 1.Department of Enterprise Integration, School of Industrial and Manufacturing ScienceCranfield UniversityCranfield, BedfordUK

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