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Paper III.1

Cluster Orientated Genetic Algorithms for the Decomposition of Multi-Dimensional Engineering Design Spaces

doi:10.4203/ccp.53.3.1

C.R. Bonham, I.C. Parmee, "Cluster Orientated Genetic Algorithms for the Decomposition of Multi-Dimensional Engineering Design Spaces", in B.H.V. Topping, (Editor), "Advances in Engineering Computational Technology", Civil-Comp Press, Edinburgh, UK, pp 87-95, 1998. doi:10.4203/ccp.53.3.1

Abstract

This paper presents an empirical investigation of the integration of the adaptive filter (developed for the COGA strategy) with a series of evolutionary search algorithms. These techniques are compared with the variable mutation COGA strategy using a suite of two-dimensional test functions. A series of performance measures are introduced which further compare the relative merits of each algorithm.

The generic capabilities of COGA are demonstrated by illustrating its ability to rapidly decompose multidimensional search spaces described by continuous and discrete parameter, real-world design models into regions of high performance. This further supports the use of the COGA strategy as a preliminary engineering design support tool.

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Front Page Browse CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Proceedings ISSN 1759-3433 CCP: 53 ADVANCES IN ENGINEERING COMPUTATIONAL TECHNOLOGY Edited by: B.H.V. Topping Paper III.1 Cluster Orientated Genetic Algorithms for the Decomposition of Multi-Dimensional Engineering Design Spaces C.R. Bonham and I.C. Parmee Plymouth Engineering Design Centre,University of Plymouth, Plymouth, United Kingdom doi:10.4203/ccp.53.3.1 purchase the full-text of this paper Full Bibliographic Reference for this paper C.R. Bonham, I.C. Parmee, "Cluster Orientated Genetic Algorithms for the Decomposition of Multi-Dimensional Engineering Design Spaces", in B.H.V. Topping, (Editor), "Advances in Engineering Computational Technology", Civil-Comp Press, Edinburgh, UK, pp 87-95, 1998. doi:10.4203/ccp.53.3.1 Abstract This paper presents an empirical investigation of the integration of the adaptive filter (developed for the COGA strategy) with a series of evolutionary search algorithms. These techniques are compared with the variable mutation COGA strategy using a suite of two-dimensional test functions. A series of performance measures are introduced which further compare the relative merits of each algorithm. The generic capabilities of COGA are demonstrated by illustrating its ability to rapidly decompose multidimensional search spaces described by continuous and discrete parameter, real-world design models into regions of high performance. This further supports the use of the COGA strategy as a preliminary engineering design support tool. purchase the full-text of this paper (price £20) go to the previous paper go to the next paper return to the table of contents return to the book description purchase this book (price £75 +P&P)
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