A Fuzzy rule-based system to predict energy consumption of genetic programming algorithms

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A Fuzzy rule-based system to predict energy consumption of genetic programming algorithms

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Title: A Fuzzy rule-based system to predict energy consumption of genetic programming algorithms
Author: Díaz Álvarez, Josefa; Chávez de la O, Francisco; Castillo Martínez, Pedro Angel; Rodríguez Díaz, Francisco Javier; Fernández de Vega, Francisco
Abstract: En los últimos años, la conciencia energética se ha convertido en una de las áreas más interesantes en nuestra sociedad ambientalmente consciente. Los diseñadores de algoritmos han sido parte de esto, particularmente cuando se trata de dispositivos en red y, principalmente, cuando se trata de dispositivos de mano. Aunque los estudios en esta área han aumentado, no muchos de ellos se han centrado en los Algoritmos Evolutivos. Hasta donde sabemos, pocos intentos se han realizado antes para modelar su consumo de energía considerando diferentes dispositivos de ejecución. En este trabajo, proponemos un sistema difuso basado en reglas para predecir el consumo de energía de una especie de Algoritmo Evolutivo, la Programación Genética, dado el dispositivo en el que se ejecutará, sus principales parámetros, y una medida de la dificultad del problema abordado. Los resultados experimentales realizados muestran que el modelo propuesto puede predecir el consumo de energía con valores de error muy bajos.In recent years, the energy-awareness has become one of the most interesting areas in our environmentally conscious society. Algorithm designers have been part of this, particularly when dealing with networked devices and, mainly, when handheld ones are involved. Although studies in this area has increased, not many of them have focused on Evolutionary Algorithms. To the best of our knowledge, few attempts have been performed before for modeling their energy consumption considering different execution devices. In this work, we propose a fuzzy rulebased system to predict energy comsumption of a kind of Evolutionary Algorithm, Genetic Prohramming, given the device in wich it will be executed, its main parameters, and a measurement of the difficulty of the problem addressed. Experimental results performed show that the proposed model can predict energy consumption with very low error values.
URI: http://hdl.handle.net/10662/11020
Date: 2018


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