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Proceedings Paper

Suboptimal MAP estimates using A* and genetic algorithms
Author(s): Allen Himler; Harry Wechsler
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Paper Abstract

We address the restoration problem for noisy and degraded signals. Novel algorithms for suboptimal MAP estimates have been developed using the A* and genetic algorithms (GAs). The experiments carried out have shown suboptimal A* (SA*) and suboptimal genetic (SGA*) algorithms to be competitive with dynamic programming (DP) for MAP estimation, and that the use of GAs (in SGA*) provides limited gains over SA*. In terms of restoration quality, the suboptimal approaches yield a solution that on the average is only 5% worse than that provided by DP as the noise and/or signal size increase. Our experiments suggest that for limited amounts of noise (about 10%) suboptimal MAP estimates compare favorably against DP in terms of runtime complexity.

Paper Details

Date Published: 1 February 1992
PDF: 11 pages
Proc. SPIE 1607, Intelligent Robots and Computer Vision X: Algorithms and Techniques, (1 February 1992); doi: 10.1117/12.57044
Show Author Affiliations
Allen Himler, George Mason Univ. (United States)
Harry Wechsler, George Mason Univ. (United States)


Published in SPIE Proceedings Vol. 1607:
Intelligent Robots and Computer Vision X: Algorithms and Techniques
David P. Casasent, Editor(s)

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