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A bioreactor benchmark for adaptive network-based process control
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A neural network baseline problem for control of aircraft flare and touchdown
Neural networks for control
A challenging set of control problems
Neural networks for control
Genetic programming (videotape): the movie
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IEEE Transactions on Evolutionary Computation
Bioreactor control by genetic programming
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II
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The automatic construction of controllers would be ideal in situations where traditional control theory and algorithms fail, as it is the case with certain dynamical systems. Genetic programming, a field under the "umbrella" of evolutionary computation, is capable of creating computer programs given a high level description of a problem. The evolution of such computer programs is driven by their fitness. The fitness is defined by an objective function, which measures how well a particular program performs for the specific problem that tries to solve. Any controller can be described in terms of a computer program and thus, at least in theory, genetic programming offers an ideal candidate for the automatic construction of controllers. This paper considers the application of genetic programming on two different problems: the aircraft autolanding problem and the bioreactor control problem, both of which have been suggested in the literature as challenging benchmarks in the quest for building automatic controllers. The results presented here show that successful control laws in analytic form are derived for both cases.