Subset Sensor Selection Optimization: A Genetic Algorithm Approach with Innovative Set Encoding Methods

The study presents a new approach for solving the sensor subset selection problem using set encoding and genetic algorithms, aiming to minimize the number of sensors while maintaining accurate spatial estimation. The proposed method, tested on groundwater data from the Savannah River Site, introduces novel crossover and mutation methods, outperforming a previous greedy method with an R² higher than 0.98 for reduced sensor counts.