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Proceedings Paper

Using local correction and mutation with memory to improve convergence of evolutionary algorithm in image registration
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Paper Abstract

The modified versions of the basic genetic operations - reproduction, crossover and mutation - in evolutionary algorithm are proposed in relation to 2D grayscale image registration problem. Two modifications of the reproduction phase include deletion of clones and genes with the same or similar parameter values, and local correction of the reproduction pool. Local correction is implemented as two consecutive stages - random search and local refinement. The RC-crossover is introduced that takes advantage of the best genes of the population while avoiding a direct replacement of the worse parameter values with their better counterparts. Mutation with memory aims to explore all poorly represented areas of the search space in order to eliminate the possibility of overlooking a better (or the best) solution. Computational experiments show that proposed modifications can improve convergence of evolutionary procedure when they are applied to 2D grayscale image registration problem.

Paper Details

Date Published: 25 July 2002
PDF: 12 pages
Proc. SPIE 4726, Automatic Target Recognition XII, (25 July 2002); doi: 10.1117/12.477032
Show Author Affiliations
Izidor Gertner, CUNY/City College (United States)
Igor V. Maslov, CUNY/Graduate Center (United States)


Published in SPIE Proceedings Vol. 4726:
Automatic Target Recognition XII
Firooz A. Sadjadi, Editor(s)

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