Sökning: "Evolutionära algoritmer"

Visar resultat 1 - 5 av 13 uppsatser innehållade orden Evolutionära algoritmer.

  1. 1. Evolutionary algorithms in statistical learning : Automating the optimization procedure

    Master-uppsats, Umeå universitet/Institutionen för matematik och matematisk statistik

    Författare :Niklas Sjöblom; [2019]
    Nyckelord :evolutionary algorithms; statistical learning; gradient boosting; automation; artificial intelligence; evolutionära algoritmer; statistisk inlärning; gradient boosting; automation; artificiell intelligens;

    Sammanfattning : Scania has been working with statistics for a long time but has invested in becoming a data driven company more recently and uses data science in almost all business functions. The algorithms developed by the data scientists need to be optimized to be fully utilized and traditionally this is a manual and time consuming process. LÄS MER

  2. 2. A comparison of Intelligent Water Drops and Genetic Algorithm for maze solving

    Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS); KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Jesper Lundholm; Johan Ledéus; [2018]
    Nyckelord :;

    Sammanfattning : Evolutionary and swarm based algorithms are subsets of bio-inspired algorithms where    Genetic Algorithm (GA) belongs to the former and Intelligent Water Drops (IWD) to the latter.      In this report we investigate their ability to solve mazes with different complexity. LÄS MER

  3. 3. A comparison of differential evolution and a genetic algorithm applied to the longest path problem

    Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS); KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Marcus Hamilton; Jacob Nyman; [2018]
    Nyckelord :;

    Sammanfattning : Genetic algorithms and differential evolution are two well-established types of generic algorithms that can be applied to a great numberof optimization problems. Both are subgroups of evolutionary algorithms that are inspired by nature, with many practical implementations in for instance research and the industry. LÄS MER

  4. 4. Performance differences between multi-objective evolutionary algorithms in different environments

    Kandidat-uppsats, KTH/Skolan för datavetenskap och kommunikation (CSC); KTH/Skolan för datavetenskap och kommunikation (CSC)

    Författare :Shyhwang Ong; Anton Täcklind; [2016]
    Nyckelord :;

    Sammanfattning : The time required to find the optimal solution to a problem increases exponentially as thesize and amount of parameters increases. Evolutionary algorithms tackle this problemheuristically by generating better solutions over time. LÄS MER

  5. 5. Linking the dynamics of genetic algorithms to the encoding of information

    Kandidat-uppsats, Lunds universitet/Beräkningsbiologi och biologisk fysik; Lunds universitet/Institutionen för astronomi och teoretisk fysik

    Författare :Henrik Åhl; [2016]
    Nyckelord :Genetic algorithms; evolution; dynamics; encoding; encoding of information; genetic; string; binary; gray code; consensus; algorithm; optimisation; genotype; phenotype; genome; evolutionary algorithms; fitness; search space; Physics and Astronomy;

    Sammanfattning : Genetic algorithms are complex constructs often used as heuristic search methods in contexts ranging from combinatorial optimisation to in silico evolution. They draw inspiration from the principles of biological evolution by utilizing the concepts of mutation, reproduction and selection in order to improve a population of solutions. LÄS MER