From 5168512967371f06ac8492ae5c64e5acc65aff82 Mon Sep 17 00:00:00 2001 From: coolneng Date: Tue, 22 Jun 2021 15:00:08 +0200 Subject: [PATCH] Add summary --- docs/Summary.org | 143 +++++++++++++++++++++++++++++++++++++++++++++++ docs/Summary.pdf | Bin 0 -> 502267 bytes 2 files changed, 143 insertions(+) create mode 100644 docs/Summary.org create mode 100644 docs/Summary.pdf diff --git a/docs/Summary.org b/docs/Summary.org new file mode 100644 index 0000000..d81eabc --- /dev/null +++ b/docs/Summary.org @@ -0,0 +1,143 @@ +#+TITLE: Práctica 1 +#+SUBTITLE: Metaheurísticas +#+AUTHOR: Amin Kasrou Aouam +#+DATE: 2021-06-22 +#+PANDOC_OPTIONS: template:~/.pandoc/templates/eisvogel.latex +#+PANDOC_OPTIONS: listings:t +#+PANDOC_OPTIONS: toc:t +#+PANDOC_METADATA: lang=es +#+PANDOC_METADATA: titlepage:t +#+PANDOC_METADATA: listings-no-page-break:t +#+PANDOC_METADATA: toc-own-page:t +#+PANDOC_METADATA: table-use-row-colors:t +#+PANDOC_METADATA: colorlinks:t +#+PANDOC_METADATA: logo:/home/coolneng/Photos/Logos/UGR.png +#+LaTeX_HEADER: \usepackage[ruled, lined, linesnumbered, commentsnumbered, longend]{algorithm2e} +* Práctica 2 + +** Introducción + +En esta práctica, usaremos distintos algoritmos de búsqueda, basados en poblaciones, para resolver el problema de la máxima diversidad (MDP). Implementaremos: + +- Algoritmo genético +- Algoritmo memético + +** Algoritmos + +*** Genético + +Los algoritmos genéticos se inspiran en la evolución natural y la genética. Generan un conjunto de soluciones inicial (i.e. población), seleccionan un subconjunto de individuos sobre los cuales se opera, hacen operaciones de recombinación y mutación, y finalmente reemplazan la población anterior por una nueva. + +El procedimiento general del algoritmo queda ilustrado a continuación: + +\begin{algorithm} + \KwIn{A list $[a_i]$, $i=1, 2, \cdots, n$, that contains the population of individuals} + \KwOut{Processed list} + + $P(t) \leftarrow initializePopulation()$ + $P(t) \leftarrow evaluatePopulation()$ + + \While{$\neg stop condition $}{ + $t = t + 1$ + + $parents \leftarrow selectParents(P(t-1))$ + + $offspring \leftarrow recombine(parents)$ + + $offspring \leftarrow mutate(offspring)$ + + $P(t) \leftarrow replacePopulation(P(t-1), offspring)$ + + $P(t) \leftarrow evaluatePopulation()$ + } + \KwRet{$P(t)$} +\end{algorithm} + +Procedemos a la implementación de 4 variantes distintas, según 2 criterios: + +**** Criterio de reemplazamiento +- *Generacional*: la nueva población reemplaza totalmente a la población anterior +- *Estacionario*: los dos mejores hijos reemplazan los dos peores individuos en la población anterior +**** Operador de cruce +- *Uniforme*: mantiene las posiciones comunes de ambos padres, las demás se eligen de forma aleatoria de cada padre (requiere reparador) +- *Posición*: mantiene las posiciones comunes de ambos padres, elige el resto de elementos de cada padre y los baraja. Genera 2 hijos. + +*** Memético + +Los algoritmos meméticos surgen de la hibridación de un algoritmo genético, con un algoritmo de búsqueda local. El resultado es un algoritmo que posee un buen equilibrio entre exploración y explotación. + +El procedimiento general del algoritmo queda ilustrado a continuación: + +\begin{algorithm} + \KwIn{A list $[a_i]$, $i=1, 2, \cdots, n$, that contains the population of individuals} + \KwOut{Processed list} + + $P(t) \leftarrow initializePopulation()$ + $P(t) \leftarrow evaluatePopulation()$ + + \While{$\neg stop condition $}{ + \If{$certain iteration$}{ + $P(t) <- localSearch(P(t-1))$ + } + $t = t + 1$ + + $parents \leftarrow selectParents(P(t-1))$ + + $offspring \leftarrow recombine(parents)$ + + $offspring \leftarrow mutate(offspring)$ + + $P(t) \leftarrow replacePopulation(P(t-1), offspring)$ + + $P(t) \leftarrow evaluatePopulation()$ + } + \KwRet{$P(t)$} +\end{algorithm} + +Procedemos a la implementación de 3 variantes distintas: +- Búsqueda local sobre todos los cromosomas +- Búsqueda local sobre un subconjunto aleatorio de cromosomas +- Búsqueda local sobre un el subconjunto de los mejores cromosomas + +** Implementación + +La práctica ha sido implementada en /Python/, usando las siguientes bibliotecas: + +- NumPy +- Pandas + +*** Instalación + +Para ejecutar el programa es preciso instalar Python, junto con las bibliotecas *Pandas* y *NumPy*. + +Se proporciona el archivo shell.nix para facilitar la instalación de las dependencias, con el gestor de paquetes [[https://nixos.org/][Nix]]. Tras instalar la herramienta Nix, únicamente habría que ejecutar el siguiente comando en la raíz del proyecto: + +#+begin_src shell +nix-shell +#+end_src + +** Ejecución + +La ejecución del programa se realiza mediante el siguiente comando: + +#+begin_src shell +python src/main.py +#+end_src + +Los parámetros posibles son: + +| dataset | algoritmo | parámetros | +| Cualquier archivo de la carpeta data | genetic | uniform/position generation/stationary | +| | memetic | all/random/best | + +También se proporciona un script que ejecuta 1 iteración de cada algoritmo, sobre cada uno de los /datasets/, y guarda los resultados en una hoja de cálculo. Se puede ejecutar mediante el siguiente comando: + +#+begin_src shell +python src/execution.py +#+end_src + +*Nota*: se precisa instalar la biblioteca [[https://xlsxwriter.readthedocs.io/][XlsxWriter]] para la exportación de los resultados a un archivo Excel. + +* Análisis de los resultados + +Desafortunadamente, debido a un tiempo de ejecución excesivamente alto (incluso tras ajustar los metaparámetros) no podemos proporcionar resultados de la ejecución de los algoritmos. diff --git a/docs/Summary.pdf b/docs/Summary.pdf new file mode 100644 index 0000000000000000000000000000000000000000..c3d0096ce67e443d16eea902997e0aca2e54d0bb GIT binary patch literal 502267 zcmeFacU%))_b)m~Z=#|E1!*=^2t}nR%@RdX5k)}>Er=p5p-69niaID@1CgRwP!SZR z1gQZ-ktU*m)X+gX2%&TL5T5sCl=q%{Kj;3=x#t}67fxo@UVVMn+I!-r69*1T%VBqM zZTkA1TFs>>CM!lHSaPYTaLF7sx4r0SA+|$SL5%U?k~w(E+R@xzOy;1qsiXM;b2H+3 zb1pSCE(b?@b5k2Gmzs!^$Fy^0c{1pFW7Q&IKacn9%okT<;ov+$4A{RbIbr{!%7j_% z8rfGqoR?CS3WwdDBK_VkyBf~I&4O}RO)*kC&z@34Zss2MY=Z!ok*^a*K+-e 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