Estimation of Distribution algorithm for sensor selection problems
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.