Estimating Energy Consumption in Evolutionary Algorithms by Means of FRBS Towards Energy-Aware Bioinspired Algorithms

• Published in: Progress in Artificial Intelligence pp. 229 - 240
• Main Authors: Díaz Álvarez, Josefa, Chávez de La O, Francisco, García Martínez, Juan Ángel, Castillo Valdivieso, Pedro Ángel, de Vega, Francisco Fernández
• Format: Book Chapter
• Language: English
• Published: Cham Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Energy-aware computing; Evolutionary algorithms; Green computing; Performance measurements
• DOI: 10.1007/978-3-319-65340-2_19
• ISBN: 9783319653396; 3319653393
• ISSN: 0302-9743

During the last decades, energy consumption has become a topic of interest for algorithm designers, particularly when devoted to networked devices and mainly when handheld ones are involved. Moreover energy consumption has become a matter of paramount importance in nowadays environmentally conscious society. Although a number of studies are already available, not many have focused on Evolutionary Algorithms (EAs). Moreover, no previous attempt has been performed for modeling energy consumption behavior of EAs considering different hardware platforms. This paper thus aims at not only analyzing the influence of the main EA parameters in their energy related behavior, but also tries for the first time to develop a model that allows researchers to know how the algorithm will behave in a number of hardware devices. We focus on a specific member of the EA family, namely Genetic Programming (GP), and consider several devices when employed as the underlying hardware platform. We apply a Fuzzy Rules Based System to build the model that allows then to predict energy required to find a solution, given a previously chosen hardware device and a set of parameters for the algorithm.