The expected runtime of the (1+1) evolutionary algorithm on almost linear functions
This Thesis expands the theoretical research done in the area of evolutionary algorithms. The (1+1)EA is a simple algorithm which allows to gain some insight in the behaviour of these randomized search heuristics. This work shows ways to possible improve on existing bounds. The general good runtime...
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| Format: | Others |
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2011
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| Online Access: | http://cardinalscholar.bsu.edu/handle/handle/188087 http://liblink.bsu.edu/uhtbin/catkey/1356253 |
| Summary: | This Thesis expands the theoretical research done in the area of evolutionary algorithms. The (1+1)EA is a simple algorithm which allows to gain some insight in the behaviour of these randomized search heuristics. This work shows ways to possible improve on existing bounds. The general good runtime of the algorithm on linear functions is also proven for classes of quadratic functions. These classes are defined by the relative size of the quadratic and the linear weights. One proof of the paper looks at a worst case algorithm which always shows a worst case behaviour than many other functions. This algorithm is used as an upper bound for a lot of different classes. === Department of Computer Science |
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