Abstract
A modified Genetic Algorithm (GA) based search strategy is presented here that is computationally more efficient than the conventional GA. Here the idea is to start a GA with the chromosomes of small length. Such chromosomes represent possible solutions with coarse resolution. A finite space around the position of solution in the first stage is subject to the GA at the second stage. Since this space is much smaller than the original search space, chromosomes of same length now represent finer resolution. In this way, the search progresses from coarse to fine solution in a cascaded manner. Since chromosomes of small size are used at each stage, the overall approach becomes computationally more efficient than a single stage algorithm with the same degree of final resolution. Also, since at the lower stage we work on low resolution, the algorithm can avoid local spurious extrema. The effectiveness of the proposed GA has been demonstrated for the optimization of some synthetic functions and on pattern recognition problems namely dot pattern matching and object matching with edge map.
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© 2001 Springer-Verlag Berlin Heidelberg
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Garai, G., Chaudhuri, B.B. (2001). A Cascaded Genetic Algorithm for efficient optimization and pattern matching. In: Singh, S., Murshed, N., Kropatsch, W. (eds) Advances in Pattern Recognition — ICAPR 2001. ICAPR 2001. Lecture Notes in Computer Science, vol 2013. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44732-6_4
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DOI: https://doi.org/10.1007/3-540-44732-6_4
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