Self-organizing structured modelling of a biotechnological fed-batch fermentation by means of genetic programming

Bettenhausen, Kurt Dirk; Marenbach, Peter; Freyer, Stephan; Rettenmaier, H.; Nieken, Ulrich

doi:10.1049/cp:19951095

Cited by 20 publications

(8 citation statements)

References 5 publications

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“…By applying random variation followed by fitness based selection, the algorithm searches the space of possible mathematical expressions and converges on a functional form that accurately models the available data. Bettenhausen et al (1995) looked at the application of GP to the modelling of fed-batch fermenters. GP was used to evolve dynamic models of the biomass in the fermenter using a function set including locally recursive functions (such as first-order plus dead-time transfer functions and feedforward and feedback loops).…”

Section: Introductionmentioning

confidence: 99%

Evolving a hybrid model of a fed-batch fermentation process

McKay

Sanderson

Willis

et al. 1998

Transactions of the Institute of Measurement and Control

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This paper presents a novel method for the system identification of the fed-batch fermentation process defined in the problem statement of the Biotechnological Control Forum Modelling and Control Competition. The identification methodology involves a hybrid of mechanistic modelling and Genetic Programming techniques. It provides an accurate model of the system which should be extremely useful in both the optimisation and control of this process.

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Section: Introductionmentioning

confidence: 99%

Evolving a hybrid model of a fed-batch fermentation process

McKay

Sanderson

Willis

et al. 1998

Transactions of the Institute of Measurement and Control

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“…Genetic programming has been applied to solve a variety of optimization problems including the identification of nonlinear systems [33]; [34] and in the identification of chemical processes [35]. It has also been used in the development of signal processing algorithms [36].…”

Section: Genetic Programmingmentioning

confidence: 99%

Evolution of Software Reliability Growth Models: A Comparison of Auto-Regression and Genetic Programming Models

Alweshah¹,

Ahmed²,

Aldabbas³

2015

IJCA

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Building reliability growth models to predict software reliability and identify and remove errors is both a necessity and a challenge for software testing engineers and project managers. Being able to predict the number of faults in software helps significantly in determining the software release date and in effectively managing project resources. Most of the growth models consider two or three parameters to estimate the accumulated faults in the testing process. Interest in using evolutionary computation to solve prediction and modeling problems has grown in recent years. In this paper, we explore the use of genetic programming (GP) as a tool to help in building growth models that can accurately predict the number of faults in software early on in the testing process. The proposed GP model is based on a recursive relation derived from the history of measured faults. The developed model is tested on real-time control, military, and operating system applications. The dataset was developed by John Musa of Bell Telephone Laboratories. The results of a comparison of the GP model with the traditional and simpler auto-regression model are presented.

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“…Evett and Fernandez (1998) claim that this technique is easy to implement and can improve the ability of GP to evolve numerical constants without significantly increasing computational requirements. Bettenhausen, et al (1995) argue that any conventional optimisation procedure is valid, and use a gradient descent algorithm to determine constant values. In this work, the Levenberg-Marquardt (L-M) non-linear least squares routine was used to optimise the parameter values present in each population member.…”

Section: Standard Gp Algorithm Detailsmentioning

confidence: 99%

“…Engineering applications include signal processing , electrical circuit design (Koza et al, 1999) and scheduling (Montana and Czerwinski, 1996). Applications with particular relevance to chemical engineering include polymer design (Porter, et al, 1996), process controller evolution (Searson et al, 1998) and modelling of both steady-state and dynamic processes , Bettenhausen et al, 1995.…”

Section: Introductionmentioning

confidence: 99%

Dynamic systems modelling using genetic programming

Hinchliffe¹,

Willis²

2003

Computers & Chemical Engineering

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Genetic programming (GP) is an evolutionary algorithm that attempts to evolve solutions to a problem by using concepts taken from the naturally occurring evolutionary process. This thesis introduces the concepts of GP model development by applying the technique to steady-state model evolution. A variation of the algorithm known as the multiple basis function GP (MBF-GP) algorithm is described and its performance compared with the standard algorithm. Results show that the MBF-GP algorithm requires significantly less computational effort to evolve models of comparable accuracy to the standard algorithm. The steady-state algorithm is then modified to enable the evolution of dynamic process models. Three case studies are used to demonstrate algorithm performance and show how the MBF-GP algorithm produces performance benefits similar to those observed in the steady-state modelling work. A comparison with neural networks reveals that GP is able to match the accuracy of the network predictions but is more expensive computationally. However, a significant advantage of the GP algorithm is that it can automatically evolve the time history of model terms required to account for process characteristics such as the system time delay.The model development process is not simply a case of reducing the error between the predicted and actual process output. The parallel nature of GP means that it is ideally suited to solving multi-objective problems. The MBF-GP algorithm is modified to incorporate a Pareto based ranking scheme that allows models to be compared using multiple performance criteria. The ranking scheme allows preference information in the form of goals and priorities to be specified in order to guide the search towards the desired region of the search space. Two case studies are used to demonstrate the performance of this technique. The first example uses the multi-objective algorithm to improve the parsimony of the evolved model structures. The second example demonstrates how a set residual correlation tests can be combined and used as an additional performance measure. In each case, the multi-objective algorithm performs significantly better than the single objective version. In addition, the inclusion of preference information overcomes some of the difficulties associated with conventional Pareto ranking and produces a greater number of acceptable solutions. Prefaceii

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Self-organizing structured modelling of a biotechnological fed-batch fermentation by means of genetic programming

Abstract: 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.

Cited by 20 publications

References 5 publications

Evolving a hybrid model of a fed-batch fermentation process

Evolving a hybrid model of a fed-batch fermentation process

Evolution of Software Reliability Growth Models: A Comparison of Auto-Regression and Genetic Programming Models

Dynamic systems modelling using genetic programming

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