]> BibSonomy publications for/author/Hinchliffe Computers in Chemical EngineeringSupplementalS1161--1166Systems Modelling Using Genetic Programming211997algorithms, genetic programming 2008-06-19 17:35:00.0GP empirical model of vacuum distillation column and a twin screw extruder for processing corn flour. Comparison of artifical neural network and GP Computers \& Chemical Engineering121841--1854Dynamic systems modelling using genetic programming272003algorithms, genetic programming 2008-06-19 17:35:00.0genetic programming (GP) is used to evolve dynamic process models. An innovative feature of the GP algorithm is its ability to automatically discover the appropriate time history of model terms required to build an accurate model. Two case studies are used to compare the performance of the GP algorithm with that of filter-based neural networks (FBNNs). Although the models generated using GP have comparable prediction performance to the FBNN models, a disadvantage is that they required greater computational effort to develop. However, we show that a major benefit of the GP approach is that additional model performance criteria can be included during the model development process. The parallel nature of GP means that it can evolve a set of candidate solutions with varying levels of performance in each objective. Although any combination of model performance criteria could be used as objectives within a multi-objective GP (MOGP) framework, the correlation tests outlined by Billings and Voon (Int. J. Control 44 (1986) 235) were used. Barcelona, SpainProceedings of the 15th IFAC World CongressDynamic Modelling Using Genetic Programming2002algorithms, genetic programming 2008-06-19 17:35:00.0 Innsbruck, AustriaThe Pennsylvania State University CiteSeer ArchivesNineteenth IASTED International Conference, Modelling, Identification and ControlFebruary 14-17Dynamic Chemical Process Modelling Using a Multiple Basis Function Genetic Programming Algorithm2000algorithms, genetic programming 2008-06-19 17:35:00.0 UKSeptemberDynamic Modelling Using Genetic Programming2001MOGA, MOGP, SOGP algorithms, genetic programming, 2008-06-19 17:35:00.0Genetic 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. Orlando, Florida, USAProceedings of the Genetic and Evolutionary Computation Conference13-17 July1782Dynamic Chemical Process Modelling Using a Multiple Basis Function Genetic Programming Algorithm21999NARMAX algorithms, applications, genetic papers, poster programming, real world 2008-06-19 17:35:00.0 University of Wisconsin, Madison, Wisconsin, USAGenetic Programming 1998: Proceedings of the Third Annual Conference22-25 July134--139Chemical Process Sytems Modelling Using Multi-objective Genetic Programming1998MOGP algorithms, genetic programming, 2008-06-19 17:35:00.0 Stanford University, CA, USALate Breaking Papers at the Genetic Programming 1996 Conference Stanford University July 28-31, 199628--31 July56--65Modelling Chemical Process Systems Using a Multi-Gene Genetic Programming Algorithm1996algorithms, genetic programming 2008-06-19 17:35:00.0In this contribution a multi-gene Genetic Programming (Gp) Algorithm is used to evolve input output models of chemical process systems. Three case studies are used to demonstrate the performance of the method when compared to a standard GP algorithm. A statistical analysis procedure is used to aid in the assessment of the results and suggest the number of independent runs required to obtain a successful result. It is concluded that the multi-gene algorithm provides superior performance, as partitioning the problem into sub-groups incorporates basic heuristic knowledge of the search space. UKExtended Abstract, submitted to: ICANNGA '97, Norwick, UKA comparison of two Genetic Programming Algorithms Applied to Chemical Process Systems Modelling1996algorithms, genetic programming 2008-06-19 17:35:00.0Previous work by McKay et al (1996a,b,c) has shown that the Genetic programming (GP) methodology can be successfully applied to the development of non-linear steady state models of industrial chemical processes. Although a GP algorithm can identify the relevant input variables and evolve parsimonious system representations, the resulting model structures tend to contain little or no information relating to the mechanisms of the process itself. In this respect, the performance of the GP methodology is comparable to that of other black-box modelling techniques such as neural networks. Chemical process systems are often extremely complex and non-linear in nature. Phenomenological models are time consuming to develop and can be subject to inaccuracies caused by any simplifying assumptions made. Consequently, mechanistic models are costly to construct; an aspect which would make an automated procedure highly desirable. Phenomenological models are usually derived by applying the laws of conservation of mass, energy and momentum to the system. An examination of a number of steady-state mechanistic models shows that they tend to be made up of distinct sub-groups which, when added together, give the overall model structure. In the search for an automatic model generating algorithm, it would be extremely useful if the GP methodology could be used to identify these sub-groups. This could potentially enhance the GP algorithm's ability to evolve accurate chemical process models and also help to reveal hidden process knowledge. To achieve this goal, the standard GP algorithm used by McKay et al (1996a) was modified to accommodate the multiple gene model structure. The multiple gene structure was introduced by Altenberg (1994) in an attempt to enhance the learning capabilities of GA and GP algorithms. The work was inspired by the observation that, in nature, genetic information is stored on more than one gene. To demonstrate the feasibility of this new approach, real world examples are used to compare the performance of the algorithm with that of the standard GP algorithm. Computer Physics Communications4300-304A standard format for Les Houches Event Files.1762007dblp 2008-04-03 00:00:00.0