Search for dissertations about: "genetic programming"

Showing result 1 - 5 of 43 swedish dissertations containing the words genetic programming.

  1. 1. Enhancing genetic programming for predictive modeling

    University dissertation from Örebro : Örebro universitet

    Author : Rikard König; Örebro universitet.; Högskolan i Borås.; [2014]
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; Datavetenskap; Computer Science; genetic programming; predictive modelsing; data mining; machine learning; rule extraction; regression; ensembles; comprehensibility; Genetic programming; Predictive Modeling; Data Mining; Machine Learning; Rule extraction; Classification; Regression; Ensembles; Comprehensibility;

    Abstract : See separate file, "Abstract.png"... READ MORE

  2. 2. Dynamics and Performance of a Linear Genetic Programming System

    University dissertation from Örebro : Örebro universitet

    Author : Frank D. Francone; [2009]
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; Mutation; Genetic Programming; Homologous Crossover.; Crossover; Introns; Linear Genetic Programming; Code Bloat;

    Abstract : Genetic Programming (“GP”) is a machine learning algorithm. Typically, Genetic Programming is a supervised learning algorithm, which trains on labeled training examplesprovided by the user. The solution output by GP maps known attributes to the known labels. READ MORE

  3. 3. Obtaining Accurate and Comprehensible Data Mining Models An Evolutionary Approach

    University dissertation from Institutionen för datavetenskap

    Author : Ulf Johansson; Linköpings universitet.; Linköpings universitet.; Högskolan i Borås.; [2007]
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; TECHNOLOGY Information technology Computer science Computer science; TEKNIKVETENSKAP Informationsteknik Datavetenskap Datalogi; Rule extraction; Ensembles; Data mining; Genetic programming; Artificial neural networks; rule extraction; ensembles; data mining; genetic programming; artificial neural networks;

    Abstract : When performing predictive data mining, the use of ensembles is claimed to virtually guarantee increased accuracy compared to the use of single models. Unfortunately, the problem of how to maximize ensemble accuracy is far from solved. READ MORE

  4. 4. Predictive Techniques and Methods for Decision Support in Situations with Poor Data Quality

    University dissertation from Örebro University

    Author : Rikard König; Högskolan i Skövde.; Högskolan i Skövde.; Högskolan i Borås.; [2009]
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; TECHNOLOGY Information technology; TEKNIKVETENSKAP Informationsteknik; Teknik; Technology; Rule Extraction; Genetic Programming; Uncertainty estimation; Machine Learning; Artificial Neural Networks; Data Mining; Information Fusion; rule extraction; genetic programming; uncertainty estimation; machine learning; artificial neural networks; information fusion; Computer Science; data mining;

    Abstract : Today, decision support systems based on predictive modeling are becoming more common, since organizations often collect more data than decision makers can handle manually. Predictive models are used to find potentially valuable patterns in the data, or to predict the outcome of some event. READ MORE

  5. 5. Integrated Software Pipelining

    University dissertation from Linköping : Linköping University Electronic Press

    Author : Mattias Eriksson; Linköpings universitet.; Linköpings universitet.; [2009]
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; Code generation; compilers; instruction scheduling; register allocation; spill code generation; modulo scheduling; integer linear programming; genetic programming.; TECHNOLOGY Information technology Computer science; TEKNIKVETENSKAP Informationsteknik Datavetenskap;

    Abstract : In this thesis we address the problem of integrated software pipelining for clustered VLIW architectures. The phases that are integrated and solved as one combined problem are: cluster assignment, instruction selection, scheduling, register allocation and spilling. READ MORE