Nonlinear model structure identification using genetic programming

This thesis investigates the evolution and use of abstract data types within Genetic Programming (GP). In genetic programming the principles of natural evolution (fitness based selection and recombination) acts on program code to automatically generate computer programs. The research in this thesis is motivated by the observation from software engineering that data abstraction (e.g. via abstract data types) is essential in programs created by human programmers. We investigate whether abstract data types can be similarly beneficial to the automatic production of programs using GP. GP can automatically "evolve" programs which solve non-trivial problems but few experiments have been reported where the evolved programs explicitly manipulate memory and yet memory is an essential component of most computer programs. So far work on evolving programs that explicitly use memory has principally used either problem specific memory models or a simple indexed memory model consisting of a single global shared array. Whilst the latter is potentially sufficient to allow any computation to evolve, it is unstructured and allows complex interaction between parts of programs which weaken their modularity. In software engineering this is addressed by controlled use of memory using scoping rules and abstract data types, such as stacks, queues and files. This thesis makes five main contributions: (1) Proving that abstract data types (stacks, queues and lists) can be evolved using genetic programming. (2) Demonstrating GP can evolve general programs which recognise a Dyck context free language, evaluate Reverse Polish expressions and GP with an appropriate memory structure can solve the nested brackets problem which had previously been solved using a hybrid GP. (3) In these three cases (Dyck, expression evaluation and nested brackets) an appropriate data structure is proved to be beneficial compared to indexed memory. (4) Investigations of real world electrical network maintenance scheduling problems demonstrate that Genetic Algorithms can find low cost viable solutions to such problems. (5) A taxonomy of GP is presented, including a critical review of experiments with evolving memory. These contributions support our thesis that data abstraction can be beneficial to automatic program generation via artificial evolution.

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Genetic programming and data structures

Genetic programming is an automatic method for creating a computer program or other complex structure to solve a problem. This paper first reviews various instances where genetic programming has previously produced human-competitive results. It then presents new human-competitive results involving the automatic synthesis of the design of both the parameter values (i.e., tuning) and the topology of controllers for two illustrative problems. Both genetically evolved controllers are better than controllers designed and published by experts in the field of control using the criteria established by the experts. One of these two controllers infringes on a previously issued patent. Other evolved controllers duplicate the functionality of other previously patented controllers. The results in this paper, in conjunction with previous results, reinforce the prediction that genetic programming is on the threshold of routinely producing human-competitive results and that genetic programming can potentially be used as an “invention machine” to produce patentable new inventions.

Automatic Creation of Human-Competitive Programs and Controllers by Means of Genetic Programming

The aim of this paper is to provide an introduction to the rapidly developing field of genetic programming (GP). Particular emphasis is placed on the application of GP to engineering problem solving. First, the basic methodology is introduced. This is followed by a review of applications in the areas of systems modelling, control, optimisation and scheduling, design and signal processing. The paper concludes by suggesting potential avenues of research.

Genetic programming: an introduction and survey of applications

Based on the standard genetic programming (GP) paradigm, we introduce a new probability measure of time series' predictability. It is computed as a ratio of two fitness values (SSE) from GP runs. One value belongs to a subject series, while the other belongs to the same series after it is randomly shuffled. Theoretically, the boundaries of the measure are between zero and 100, where zero characterizes stochastic processes while 100 typifies predictable ones. To evaluate its performance, we first apply it to experimental data. It is then applied to eight Dow Jones stock returns. This measure may reduce model search space and produce more reliable forecast models. Copyright © 1999 John Wiley & Sons, Ltd.

A measure of time series' predictability using genetic programming applied to stock returns

The article describes an approach for the self organizing generation of models of complex and unknown processes by means of genetic programming and its application in a biotechnological fed batch production. The approach described combines novel results of computer science-genetic programming-with well known and proven techniques of control and system theory-block diagrams and Z transformation. The synthesis of these approaches is a powerful tool for data driven modelling that offers a large number of possibilities to integrate existing knowledge e.g. on submodels or expected elements. The models received by the use of this tool provide a transparent insight into the structure of the process and a basis for long term prediction of the process behaviour and therefore for the determination of optimal setpoint profiles. That means that this approach may overcome the specific difficulties that are bound to the use of adaptive or learning-in the sense of neural networks-methods.

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

The article at hand discusses a tool for automatic generation of structured models for complex dynamic processes by means of genetic programming. In contrast to other techniques which use genetic programming to find an appropriate arithmetic expression in order to describe the input-output behaviour of a process, this tool is based on a block oriented approach with a transparent description of signal paths. A short survey on other techniques for computer based system identification is given and the basic concept of SMOG (Structured MOdel Generator) is described. Furthermore latest extensions of the system are presented in detail, including automatically defined sub-models and qualitative fitness criteria.

https://www1.rtr.tu-darmstadt.de/pdf/marenbach-bettenhausen-1996.pdf

Signal path oriented approach for generation of dynamic process models

This paper describes an approach for data-driven generation of structured models of complex and unknown processes by means of genetic programming. The basic approach which is used to generate and to modify symbolic model descriptions represented as block diagrams is introduced and an application for modelling of an industrial biotechnological fed-batch fermentation process is presented.

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

Selbstorganisierte Generierung strukturierter Prozeßmodelle

The paper at hand deals with an approach for the automatic generation of dynamic nonlinear process models. These models are obtained from experimental process data as well as from theoretical biological and chemical reeections using the ideas of Genetic Programming for the supervision and coordination of the symbolic model structure during the automatic development.

Kurt Dirk Bettenhausen

Papers

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

Signal path oriented approach for generation of dynamic process models

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

Selbstorganisierte Generierung strukturierter Prozeßmodelle

Self-organizing modelling of biotechnological batch and fed-batch fermentations