Title

Intelligent Automated Control Of Life Support Systems Using Proportional Representations

Keywords

Gene expression; Genetic algorithm (GA); Life support system control; Proportional genetic algorithm; Proportional representation; Resource allocation; Stochastic hill-climbing (SH)

Abstract

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 [23] that multivector control strategies are an effective method for control of coupled dynamical systems.

Publication Date

6-1-2004

Publication Title

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Volume

34

Issue

3

Number of Pages

1423-1434

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/TSMCB.2004.824522

Socpus ID

2942604380 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/2942604380

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