Jan Zegklitz

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We propose a new type of leaf node for use in Symbolic Regression (SR) that performs linear combinations of feature variables (LCF). Œese nodes can be handled in three di‚erent modes – an unsynchronized mode, where all LCFs are free to change on their own, a synchronized mode, where LCFs are sorted into groups in which they are forced to be identical(More)
Tato práce je zaměřena na výzkum evolučních algoritmů kombinujícíh moderní přístup v evolučním počítání, zvaný novelty search, s klasickým přístupem založeným na optimalizaci fitness. Novelty search je v této práci analyzován spolu s jedním dosavadním přístupem který již novelty search a fitness kombinuje a jsou zmíněny jejich výhody a nevýhody. Na základě(More)
Genetic Programming has been very successful in solving a large area of problems but its use as a machine learning algorithm has been limited so far. One of the reasons is the problem of overfitting which cannot be solved or suppresed as easily as in more traditional approaches. Another problem, closely related to overfitting, is the selection of the final(More)
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