Adaption of Operator Probabilities in Genetic Programming

Abstract. In this work we tried to reduce the number of free parameters within Genetic Programming without reducing the quality of the results. We developed three new methods to adapt the probabilities, different genetic operators are applied with. Using two problems from the areas of symbolic regression and classification we showed that the results in these cases were better than randomly chosen parameter sets and could compete with parameter sets chosen with empirical knowledge.

The Molonglo Southern 4 Jy sample (MS4). II. ATCA imaging and optical identification

Of the 228 sources in the Molonglo Southern 4 Jy Sample (MS4), the 133 with angular sizes < 35 arcsec have been imaged at 5 GHz at 2-4 arcsec resolution with the Australia Telescope Compact Array. More than 90% of the sample has been reliably optically identified, either on the plates of the UK Schmidt Southern Sky Survey or on R-band CCD images made with the Anglo-Australian Telescope. A subsample of 137 sources, the SMS4, defined to be a close southern equivalent of the northern 3CRR sample, was found to have global properties mostly consistent with the northern sample. Linear sizes of MS4 galaxies and quasars were found to be consistent with galaxy-quasar unification models of orientation and evolution.Comment: 102 pages; 6 figures in 21 Postscript files. To appear in Astronomical Journal. For higher-resolution versions of some figures, see http://www.physics.usyd.edu.au/astrop/rwh/ms4

Evolutionary versus Inductive Construction of Neurofuzzy Systems for Bioprocess Modelling

The control and optimization of biotechnological processes is a complex task of industrial relevance, due to the growing importance attached to biotechnology. Therefore, there is an increasing use of intelligent data analysis methods for the development and optimization of bioprocess modelling and control. Since a clear understanding of the underlying physics does not exist, nonlinear learning systems, which can accurately model exemplar data sets and explain their behaviour to the designer, are an attractive approach. This paper investigates applying neurofuzzy construction algorithms to this problem and in particular compares a Genetic Programming structuring approach with a more conventional forwards inductive learning-type algorithm. It is shown that for simple problems, the inductive learning technique generally outperforms the Genetic Programming, although for large complex problems, the latter may prove beneficial

Evolutionary verses Inductive Construction of Neurofuzzy Systems for Bioprocess Modelling

The control and optimization of biotechnical processes is a complex task of industrial relevance, due to the growing importance attached to biotechnology. Therefore, there is an increasing use of intelligent data analysis methods for the development and optimization of bioprocess modelling and control. Since a clear understanding of the underlying physics does not exist, nonlinear learning systems, which can accurately model exemplar data sets and explain their behaviour to the designer, are an attractive approach. This paper investigates applying neurofuzzy construction algorithms to this problem and in particular compares a Genetic Programming structuring approach with a more conventional forwards inductive learning-type algorithm. It is shown that for simple problems, the inductive learning techniques generally outperforms the Genetic Programming, although for large complex problems, the latter may prove beneficial

Self-Organizing Modelling of Biotechnological Batch and Fed-Batch Fermentations

this article

Self-organizing Structured Modelling of a Biotechnological Fedbatch Fermentation by Means of Genetic Programming

. The article at hand describes an approach for the self-organizing generation of models of complex and unknown processes by means of genetic programming and its application on a biotechnological fed-batch production. Key Words. Genetic programming; modelling; system identification; biotechnology; predictive control. 1. INTRODUCTION The natural biological metabolism of living organisms can be used for the production of food, medicines or basic materials for the chemical industries. The major task for optimization of those biotechnological processes is to overcome natural limitations by genetic manipulation of the organisms or variation of environmental conditions during the fermentation. This variation is part of the process engineers tasks and can be achieved by the use of advanced intelligent methods of control engineering. Almost any approach for the design of control systems is based on a model of the process behaviour. In predictive control systems -- fig. 1 --, which have prove..