Data-driven structured modelling of a biotechnological fed-batch fermentation by means of genetic programming

Marenbach, Peter; Bettenhausen, Kurt Dirk

doi:10.1243/0959651971539858

Cited by 14 publications

(10 citation statements)

References 5 publications

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“…Because of its advantages, GP has successfully been used for engineering problems with good results such as: industrial process modeling and control [ Marenbach et al , 1997; McKay et al , 1997; Willis et al , 1997; Hong , 2001; Hong and Bhamidimarri , 2003; Hong , 2003], robot control [ Banzhaf et al , 1997], environmental modeling [ Babovic and Abbott , 1997; Whigham and Craper , 2001; Hong and Rosen , 2002], medical application [ Gray et al , 1996], and application for financial systems [ Oussaidene et al , 1996]. Koza [1992] and Babovic and Abbott [1997] compare the derivation of an optimal model using GP with a known model and synthetic data; GP derived a model that was similar to the known model.…”

Section: Methodsmentioning

confidence: 99%

Automatic rainfall recharge model induction by evolutionary computational intelligence

Hong

White

Scott

2005

Water Resources Research

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[1] Genetic programming (GP) is used to develop models of rainfall recharge from observations of rainfall recharge and rainfall, calculated potential evapotranspiration (PET) and soil profile available water (PAW) at four sites over a 4 year period in Canterbury, New Zealand. This work demonstrates that the automatic model induction method is a useful development in modeling rainfall recharge. The five best performing models evolved by genetic programming show a highly nonlinear relationship between rainfall recharge and the independent variables. These models are dominated by a positive correlation with rainfall, a negative correlation with the square of PET, and a negative correlation with PAW. The best performing GP models are more reliable than a soil water balance model at predicting rainfall recharge when rainfall recharge is observed in the late spring, summer, and early autumn periods. The ''best'' GP model provides estimates of cumulative sums of rainfall recharge that are closer than a soil water balance model to observations at all four sites.

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Section: Methodsmentioning

confidence: 99%

Automatic rainfall recharge model induction by evolutionary computational intelligence

Hong

White

Scott

2005

Water Resources Research

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“…A mathematical model of the social structure of honeybees will be used to enhance the incorporation of GP methodologies [14] since the bee colony represents a highly evolved biological system [17] which forms a basis to model the continuous expansion of Web pages.…”

Section: The Biological Modelmentioning

confidence: 99%

Dynamic Load Balancing Model: Preliminary Results for Parallel Pseudo-search Engine Indexers/Crawler Mechanisms Using MPI and Genetic Programming

Walker

2001

Vector and Parallel Processing — VECPAR 2000

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Abstract. Methodologies derived from Genetic Programming (GP) and Knowledge Discovery in Databases (KDD) were used in the parallel implementation of the indexer simulator to emulate the current World Wide Web (WWW) search engine indexers. This indexer followed the indexing strategies that were employed by AltaVista and Inktomi that index each word in each Web document. The insights gained from the initial implementation of this simulator have resulted in the initial phase of the adaption of a biological model. The biological model will offer a basis for future developments associated with an integrated Pseudo-Search Engine. The basic characteristics exhibited by the model will be translated so as to develop a model of an integrated search engine using GP. The evolutionary processes exhibited by this biological model will not only provide mechanisms for the storage, processing, and retrieval of valuable information but also for Web crawlers, as well as for an advanced communication system. The current Pseudo-Search Engine Indexer, capable of organizing limited subsets of Web documents, provides a foundation for the first simulator of this model. Adaptation of the model for the refinement of the Pseudo-Search Engine establishes order in the inherent interactions between the indexer, crawler and browser mechanisms by including the social (hierarchical) structure and simulated behavior of this complex system. The simulation of behavior will engender mechanisms that are controlled and coordinated in their various levels of complexity. This unique model will also provide a foundation for an evolutionary expansion of the search engine as WWW documents continue to grow. The simulator results were generated using Message Passing Interface (MPI) on a network of SUN workstations and an IBM SP2 computer system.

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“…In modelling GP can be used e.g. for the generation of block oriented mathematical models (Marenbach et al 1997).…”

Section: Evolutionary Design Of Fuzzy Systemsmentioning

confidence: 99%

Evolutionary versus inductive construction of neurofuzzy systems for bioprocess modelling

Marenbach

Brown²

1997

Second International Conference on Genetic Algorithms in Engineering Systems

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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.

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Data-driven structured modelling of a biotechnological fed-batch fermentation by means of genetic programming

Cited by 14 publications

References 5 publications

Automatic rainfall recharge model induction by evolutionary computational intelligence

Automatic rainfall recharge model induction by evolutionary computational intelligence

Dynamic Load Balancing Model: Preliminary Results for Parallel Pseudo-search Engine Indexers/Crawler Mechanisms Using MPI and Genetic Programming

Evolutionary versus inductive construction of neurofuzzy systems for bioprocess modelling

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