Intelligent automated control of life support systems using proportional representations
Abbreviated Journal Title
IEEE Trans. Syst. Man Cybern. Part B-Cybern.
genetic algorithm (GA); life support system control; resource; allocation; proportional genetic algorithm; gene expression; proportional representation; stochastic hill-climbing (SH); ALLOCATION; Automation & Control Systems; Computer Science, Artificial Intelligence; Computer Science, Cybernetics
Effective automatic control of Advanced Life Support Systems (ALSS) is a crucial component of space exploration. An ALSS is a coupled dynamical system which can be extremely sensitive and difficult to predict. As a result, such systems can be difficult to control using deliberative and deterministic methods. We investigate the performance of two machine learning algorithms, a genetic algorithm (GA) and a stochastic hill-climber (SH), on the problem of learning how to control an ALSS, and compare the impact of two different types of problem representations on the performance of both algorithms. We perform experiments on three ALSS optimization problems using five strategies with multiple variations of a proportional representation for a total of 120 experiments. Results indicate that although a proportional representation can effectively boost GA performance, it does not necessarily have the same effect on other algorithms such as SH. Results also support previous conclusions  that multivector control strategies are an effective method for control of coupled dynamical systems.
Ieee Transactions on Systems Man and Cybernetics Part B-Cybernetics
"Intelligent automated control of life support systems using proportional representations" (2004). Faculty Bibliography 2000s. 4891.