A Genetic Algorithm for Solving Combinatorial Problems and the Effects of Experimental Error – Applied to Optimizing Catalytic Materials

Clegg, J.; Dawson, J.F.; Porter, S.J.; Barley, Mark H.

doi:10.1002/qsar.200910004

Cited by 3 publications

(1 citation statement)

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“…Genetic algorithms (GAs) were introduced by (Holland, 1992) to imitate the mechanism of genetic models of natural evolution and selection. GAs are powerful tools for solving complex for solving combinatorial problems, where a combinatorial problem involves choosing the best subset of components from a pool of possible components in order that the mixture has some desired quality (Clegg et al, 2009). GAs are computational models of evolution.…”

Section: Solution To Optimization Problem Using Genetic Algorithmmentioning

confidence: 99%

Feature Selection by Rank Aggregation and Genetic Algorithms

Bouaguel¹,

Brahim²,

Limam³

2013

Proceedings of the International Conference on Knowledge Discovery and Information Retrieval and the International Conference O

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Feature selection consists on selecting relevant features in order to focus the learning search. A simple and efficient setting for feature selection is to rank the features with respect to their relevance. When several rankers are applied to the same data set, their outputs are often different. Combining preference lists from those individual rankers into a single better ranking is known as rank aggregation. In this study, we develop a method to combine a set of ordered lists of feature based on an optimization function and genetic algorithm. We compare the performance of the proposed approach to that of well-known methods. Experiments show that our algorithm improves the prediction accuracy compared to single feature selection algorithms or traditional rank aggregation techniques.

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Section: Solution To Optimization Problem Using Genetic Algorithmmentioning

confidence: 99%