Suboptimal MAP estimates using A* and genetic algorithms

Allen Himler; Harry Wechsler

doi:10.1117/12.57044

1 February 1992 Suboptimal MAP estimates using A* and genetic algorithms

Allen Himler, Harry Wechsler

Proceedings Volume 1607, Intelligent Robots and Computer Vision X: Algorithms and Techniques; (1992) https://doi.org/10.1117/12.57044
Event: Robotics '91, 1991, Boston, MA, United States

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.

Citation Download Citation

Allen Himler and Harry Wechsler "Suboptimal MAP estimates using A* and genetic algorithms", Proc. SPIE 1607, Intelligent Robots and Computer Vision X: Algorithms and Techniques, (1 February 1992); https://doi.org/10.1117/12.57044

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