Optimization for the Maximum Rectangular Block from an Arbitrary Closed Region Using GA

Jen Shen Tsai; Chi Cheng Cheng

doi:10.4028/www.scientific.net/MSF.505-507.517

Paper Titles

Electronic Circuit Simulation and Digital PC-Based Implementation of Dynamic MRR Optimization
p.493

A Time Optimized Process Planning System for Mold/Die Polishing
p.499

A Model for Acquisition of Implicit Design Knowledge Based on KDD
p.505

An Immune Algorithm for Least Cost Advanced Tolerance Design Problem
p.511

Optimization for the Maximum Rectangular Block from an Arbitrary Closed Region Using GA
p.517

The Predicted Model of Surface Roughness in Resharpening End-Mill for Tool Grinder
p.523

Auto-Tuning Digital Control Based on Parameters Identification and Disturbance Observer
p.529

A Web-Based Embedded Sequential Controller with USB 1-N Wireless I/O Modules
p.535

A Study on Fuzzy Logic Theory to Predict the Process Parameters in GMA Welding Process
p.541

HomeMaterials Science ForumMaterials Science Forum Vols. 505-507Optimization for the Maximum Rectangular Block...

Optimization for the Maximum Rectangular Block from an Arbitrary Closed Region Using GA

Abstract:

Methods of standard genetic algorithm (SGA) and adaptive genetic algorithm (AGA) are employed to improve performance of global cutting for an arbitrary closed region. Normal conditions and special types of the closed region are also analyzed and discussed by the area map. It appears that the presented GA frameworks are superior to the blind search algorithm (BSA) and are suitable for the special types of remaining closed space (RCS). By comparing three experimental results, it can be concluded that area efficiency and time reduction are trade-offs.

You might also be interested in these eBooks

Progress on Advanced Manufacture for Micro/Nano Technology 2005

View Preview

Info:

Periodical:

Materials Science Forum (Volumes 505-507)

Pages:

517-522

DOI:

https://doi.org/10.4028/www.scientific.net/MSF.505-507.517

Citation:

Cite this paper

Online since:

January 2006

Authors:

Jen Shen Tsai, Chi Cheng Cheng

Keywords:

Adaptive Genetic Algorithm (AGA), Blind Search Algorithm BSA, Gray Coding, Standard Genetic Algorithm SGA

Export:

RIS, BibTeX

Price:

Permissions:

Request Permissions

Сopyright:

Citation:

References

[1] A. Namaad, W. L. Hsu, and D. T. Lee, , Applied Discrete Mathematics, 8, 267(1984).

Google Scholar

[2] M. J. Atallah and G. N. Frederickson, Discrete Applied Mathematics, 13, 87(1986). Fig. 3(a). Area map of the item 25. Fig. 3(b). The MRB of the item 25.

Google Scholar

[3] B. Chazelle, R. F. Drysdale and D. T. Lee, SIAM Journal of Computing, 15, 300(1986).

Google Scholar

[4] A. Aggarwal and S. Suri, ACM, 6, 278(1987).

Google Scholar

[5] M. Orlowski, Algorithmica, 5, 65(1990).

Google Scholar

[6] C. C. Cheng and J. S. Tsai, International journal of Machine Tools & Manufacture, 44(14), 1423(2004).

Google Scholar

[7] J. H. Holland, University of Michigan Press, Ann Arbot, MI (1975).

Google Scholar

[8] D. Goldberg, Genetic algorithm in search, optimization and machine learning, Addition-Wesley, Reading, MA (1989).

Google Scholar