Use Of Genetic Programming Research Articles

A hierarchical clustering methodology based on genetic programming for the solution of simple cell-formation problems

The problem of identifying machine cells and corresponding part families in cellular manufacturing has been extensively researched over the last thirty years. However, the complexity of the problem and the considerable number of issues involved in its solution create the need for increasingly efficient algorithms. In this paper the use of genetic programming for the solution of a simple version of the problem is investigated. The methodology is tested on a number of problems taken from the literature and comparative results are presented.

Identifying fuzzy models utilizing genetic programming

Fuzzy models offer a convenient way to describe complex nonlinear systems. Moreover, they permit the user to deal with uncertainty and vagueness. Due to these advantages fuzzy models are employed in various fields of applications, e.g. control, forecasting, and pattern recognition. Nevertheless, it has to be emphasized that the identification of a fuzzy model is a complex optimization task with many local minima. Genetic programming provides a way to solve such complex optimization problems. In this work, the use of genetic programming to identify the input variables, the rule base and the involved membership functions of a fuzzy model is proposed. For this purpose, several new reproduction operators are introduced.

Probabilistic Pattern Matching and the Evolution of Stochastic Regular Expressions

The use of genetic programming for probabilistic pattern matching is investigated. A stochastic regular expression language is used. The language features a statistically sound semantics, as well as a syntax that promotes efficient manipulation by genetic programming operators. An algorithm for efficient string recognition based on approaches in conventional regular language recognition is used. When attempting to recognize a particular test string, the recognition algorithm computes the probabilities of generating that string and all its prefixes with the given stochastic regular expression. To promote efficiency, intermediate computed probabilities that exceed a given cut-off value will pre-empt particular interpretation paths, and hence prune unconstructive interpretation. A few experiments in recognizing stochastic regular languages are discussed. Application of the technology in bioinformatics is in progress.

Genetic programming as an analytical tool for non-linear dielectric spectroscopy

By modelling the non-linear effects of membranous enzymes on an applied oscillating electromagnetic field using supervised multivariate analysis methods, Non-Linear Dielectric Spectroscopy (NLDS) has previously been shown to produce quantitative information that is indicative of the metabolic state of various organisms. The use of Genetic Programming (GP) for the multivariate analysis of NLDS data recorded from yeast fermentations is discussed, and GPs are compared with previous results using Partial Least Squares (PLS) and Artificial Neural Nets (NN). GP considerably outperforms these methods, both in terms of the precision of the predictions and their interpretability.

Developing Mobile Robot Wall-Following Algorithms Using Genetic Programming

This paper demonstrates the use of genetic programming (GP) for the development of mobile robot wall-following behaviors. Algorithms are developed for a simulated mobile robot that uses an array of range finders for navigation. Navigation algorithms are tested in a variety of differently shaped environments to encourage the development of robust solutions, and reduce the possibility of solutions based on memorization of a fixed set of movements. A brief introduction to GP is presented. A typical wall-following robot evolutionary cycle is analyzed, and results are presented. GP is shown to be capable of producing robust wall-following navigation algorithms that perform well in each of the test environments used.

Generating diverse software versions with genetic programming: an experimental study

Software fault-tolerance schemes often employ multiple software versions developed to meet the same specification. If the versions fail independently of each other, they can be combined to give high levels of reliability. Although design diversity is a means to develop these versions, it has been questioned because it increases development costs and because reliability gains are limited by common-mode failures. The use of genetic programming is proposed to generate multiple software versions by varying parameters of the genetic programming algorithm. An environment is developed to generate programs for a controller in an aircraft arrestment system. Eighty programs have been developed and tested on 10 000 test cases. The experimental data show that failure diversity is achieved, but for the top performing programs its levels are limited.

The Use of Genetic Programming to Build Boolean Queries for Text Retrieval through Relevance Feedback

The Use of Genetic Programming to Build Boolean Queries for Text Retrieval through Relevance Feedback

The use of genetic programming to build Boolean queries for text retrieval through relevance feedback

We report here on preliminary work on the use of evolutionary computing techniques which aims to improve Boolean information retrieval system performance through relevance feedback. There are many evolutionary techniques in computing, such as neural networks and genetic algorithms. One specific form of genetic algorithm technique has been used in our study: that of genetic programming. Terms from relevant documents are used to randomly create Boolean queries. Boolean queries are thought of as genetic programming organisms and, as such, are used for breeding to produce new organisms. Organisms which perform well, in terms of how good they are at retrieval, are given a better chance of being selected for breeding, with the result being that the overall fitness of the organisms improve to some extent. The aim is to develop the best Boolean query for an information need, given a small corpus of test documents, and then to use that query on the full collection to retrieve yet more relevant documents.

Robots playing soccer? RoboCup poses a new set of AI research challenges

The paper discusses the RoboCup challenge which is intended to stimulate innovations in a wide range of technologies and the integration of technologies into a fully functional robotic soccer team. Given how primitive today's robots are compared to a good soccer team of 11 players, the inherent technical challenges are plentiful. The paper considers the use of genetic programming and multi-agent systems for a robot soccer team.

Steady-state modelling of chemical process systems using genetic programming

Complex processes are often modelled using input-output data from experimental tests. Regression and neural network modelling techniques are commonly used for this purpose. Unfortunately, these methods provide minimal information about the model structure required to accurately represent process characteristics. In this contribution, we propose the use of Genetic Programming (GP) as a method for developing input-output process models from experimental data. GP performs symbolic regression, determining both the structure and the complexity of the model during its evolution. This has the advantage that no a priori modelling assumptions have to be made. Moreover, the technique can discriminate between relevant and irrelevant process inputs, yielding parsimonious model structures that accurately represent process characteristics. Following a tutorial example, the usefulness of the technique is demonstrated by the development of steady-state models for two typical processes, a vacuum distillation column and a chemical reactor system. A statistical analysis procedure is used to aid in the assessment of GP algorithm settings and to guide in the selection of the final model structure.

Evolving Input-Output Models of Chemical Process Systems Using Genetic Programming

Complex processes are often modelled using input-output data from experimental tests. Regression and neural network modelling techniques are commonly used for this purpose. Unfortunately, these methods provide minimal structural insight into process characteristics. In this contribution, we propose the use of Genetic Programming (GP) as a method for developing input-output process models from experimental data. GP performs symbolic regression, determining both the structure and the complexity of the model during its evolution. This lias the advantage that no a priori modelling assumptions have to be made. Moreover, the technique can discriminate between relevant and irrelevant process inputs, yielding parsimonious model structures that accurately represent process characteristics. Two examples are used to demonstrate the utility of the GP technique as a process modelling tool.