Title :
Estimation of Distribution algorithm for sensor selection problems
Author :
Naeem, M. ; Lee, D.C.
Author_Institution :
Sch. of Eng. Sci., Simon Fraser Univ., Burnaby, BC, Canada
Abstract :
In this paper, we apply Estimation-of-Distribution Algorithms (EDAs) to the problem of selecting a set of k sensors from m sensors for the purpose of parameter estimation. Unlike other evolutionary algorithms, in EDAs a new population of individuals in each iteration is generated without crossover and mutation operators; instead, a new population is generated based on a probability distribution, which is estimated form the best selected individuals of previous iteration. Our results indicate that EDA is a good candidate for solving the sensor selection problems.
Keywords :
evolutionary computation; parameter estimation; probability; estimation of distribution algorithm; evolutionary algorithms; parameter estimation; probability distribution; sensor selection problems; Chemical sensors; Electronic design automation and methodology; Estimation error; Evolutionary computation; Genetic mutations; Maximum likelihood estimation; Parameter estimation; Probability distribution; Sensor systems; Virtual manufacturing; EDA; Sensor Selection;
Conference_Titel :
Radio and Wireless Symposium (RWS), 2010 IEEE
Conference_Location :
New Orleans, LA
Print_ISBN :
978-1-4244-4725-1
Electronic_ISBN :
978-1-4244-4726-8
DOI :
10.1109/RWS.2010.5434261
