Optimizando e as-meta

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  • La imagen está tomada de http://www.flickr.com/photos/dahlstroms/4083220012/
  • Imagen obtenida de http://www.flickr.com/photos/sebilden/3429805384/
  • Devel::NYTProf, un profiler para Perl desarrollado inicialmente para trabajar en el NYTimes (de ahí el nombre) y liberado; actualmente disponible en CPAN; es la mejor herramienta y más detallada que hay ahora mismo en Perl, produciendo una salida en HTML.
  • Imagen CC obtenida de http://www.flickr.com/photos/rbanks/2292915/
  • En el repositorio se pueden ver como diferentes versiones del mismo programa todos los programas que se han usado en la serie, usando la herramienta de diferencia entre programas que da Launchpad.
  • The use of caché decreases running time by a constant, which is approximately the percentage of chromosomes that are generated repeatedly during the evolutionary algorithm. This gives also some insight over the algorithm itself: the one we're using is generating repeated chromosomes, independently of size.
    On the other hand, using tr, which is a function that translates letters in a string to other letters; in this case, 1 to nothing. As a side effect, it returns the number of translations. This is quite efficient since it runs over the string only once, and it does so in machine code, not language code. This means that, even as running time increases linearly with chromosome size, it does so less steeply
  • No es la única mejora, se probaron también otros algoritmos como mergesort, y otros que no tuvieron tanto éxito. El comportamiento de los lenguajes de programación
  • Optimizando e as-meta

    1. 1. Optimizing evolutionary algorithms at program level JJ Merelo, A. M. Mora, Pedro Castillo, Juan L. Jiménez Laredo, Carlos Fernandes GeNeura team: http://geneura.wordpress.com Departamento de Arquitectura y Tecnología de Computadores: http://atc.ugr.es University of Granada: http://www.ugr.es
    2. 2. Evolutionary Algorithms  EAs are population-based algorithms that rely on a certain number of parameters and operators Best parameter values and competence of operators are well studied. We know the limits of the EAs scalability However…
    3. 3. Little attention is devoted to evolutionary algorithm implementation Even as it allows to design better algorihtms and obtain substantial improvement at the algorithm and runtime level
    4. 4. Used tools Monitors Running time, used memory, resource usage Profilers Parts that are responsible for waisting resources
    5. 5. Profiler usage
    6. 6. Problem setup Fitness = MaxOnes Canonical evolutionary algorithm, élite = 2 Varied population and chromosome size Free software: http://bit.ly/bOk3z3
    7. 7. Evolving an evolutionary algorithm program Eliminated Added
    8. 8. Size always matters Caché for fitness computations Use of the tr Perl-specific function for computing fitness
    9. 9. If we are more, we'll take more Using a profiler for finding a bottleneck: the sort function Change by Sort::Key It improves worst-case behavior. Blue uses previous slide function
    10. 10. Conclusions Running time improves up to two orders of magnitude (for some population sizes) through changes in implementation. It is convenient to always bear in mind usual programming techniques and good practices. Future work: apply this to complex EA libraries such as Algorithm::Evolutionary, written in Perl, and other languages (Lua)
    11. 11. Thank you Questions?

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