The Merits of a Parallel Genetic Algorithm in Solving Hard Optimization Problems

[+] Author and Article Information
A. J. Knoek van Soest, L. J. R. Richard Casius

Faculty of Human Movement Sciences Institute for Fundamental and Clinical Human Movement Sciences, Free University Amsterdam, van der Boechorststraat 9, NL 1081 Amsterdam, The Netherlands

J Biomech Eng 125(1), 141-146 (Feb 14, 2003) (6 pages) doi:10.1115/1.1537735 History: Received July 01, 2000; Revised September 01, 2002; Online February 14, 2003
Copyright © 2003 by ASME
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Grahic Jump Location
ERROR as a function of NFE for objective function H1 for algorithms SQP, DS, SA, and GA. At any NFE, ERROR is the absolute difference between the best value found so far and the global optimum, averaged over 1000 independent optimizations.
Grahic Jump Location
ERROR as a function of NFE for objective function H2. See legend to Fig. 1 for further details.
Grahic Jump Location
(a) ERROR as a function of NFE for objective function H3(n=4). See legend to Fig. 1 for further details. (b) ERROR as a function of NFE for objective function H3(n=32). See legend to Fig. 1 for further details.
Grahic Jump Location
ERROR averaged over 100 independent optimizations as a function of NFE for the vertical jumping problem. See legend to Fig. 1 for further details.
Grahic Jump Location
ERROR, averaged over 10 independent optimizations for GA and over 40 for SA, as a function of NFE for the sprint cycling problem. See legend to Fig. 1 for further details.
Grahic Jump Location
Number of GA optimizations per hour for the sprint cycling problem as a function of the number of CPU’s accessed through a network connection. These CPU’s evaluate the test function and report the result back to the CPU running the GA. The separate data point for 0 CPU’s concerns the stand-alone situation. Pentium 500 CPU’s were used, connected through a 10 MBPS LAN.




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