From c6118e2d866b486a6ec1480a36be14e2416f8788 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 -> 502260 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..a6e8df0 --- /dev/null +++ b/docs/Summary.org @@ -0,0 +1,143 @@ +#+TITLE: Práctica 2 +#+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..5492c7e93465576482ef2eeb725e075e41425356 GIT binary patch literal 502260 zcmeFad0b5Y|2KTnz6sGpX|siz7PQbth6sr$nzkX*Hq)YgQwS$2TS*I9LW*da_NGjW zHi@Kl+Dm(>Y43iYrqB1=q3?a&_v5;M*L7dl)%=w+=e*vp{q=mk-tRLu9p8UIS`NF5 zYtxr+)M_q8FZKQHdq z+yCXs`fZLP!e^+~rav#Ne1G7+68nMiTTcxsyWK;p8_z_PVx^g$t`fiQHht>;ZC=-9 zBit%&bfdj$CW5gc*Yc-4_gsA`$tf4poSMZc&$IjZ+vE55O7h0uQs%6SV>8~nn`o@> 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