Natural and Evolutionary Computation Group
Academic Staff in Computing and Electronic Systems:
Group's Research Interests:
Natural Computation is the study of computational systems that use ideas and get inspirations from natural systems, including biological, ecological and physical systems. Examples of nature inspired algorithms include evolutionary algorithms (see below), neural networks, ant systems (click on the picture on the right for an illustration of an ant system solving a travelling salesman problem), etc. Our research covers a variety of areas of natural computation.
Genetic and Evolutionary Computation (GEC) is a field the main objective of which draws inspiration from genetics and natural selection to solve engineering problems such as optimisation, search and machine learning. Genetic Algorithms (GAs) and Genetic Programming (GP) are the most famous G EC techniques. GAs use vectors of numbers (chromosomes) to represent the solutions of problems (adult individual). GAs maintain a population of such solutions and evolve it by applying an artificial selective pressure on the solutions (survival of the fittest) and by making them mate (crossover). GP is an evolutionary technique for the automatic discovery of programs. GP is a special GA in which solutions are syntax trees representing computer programs (click on the picture on the right for an illustration). The syntax trees are executed by an interpreter/compiler and their performance (fitness) is evaluated. The fittest programs survive and mate. Our work has focused both on the applications of evolutionary technology and on the theoretical foundations of genetic programming and genetic algorithms.
Estimation of distribution algorithms. Population-based algorithms using estimation of distribution, often called estimation of distribution algorithms (EDAs), are another major paradigm in evolutionary computation. Some EDA-like algorithms have achieved state-of-the-art performance in applications. However, most of the existing EDA-like algorithms have been developed on an ad-hoc basis. Both theory and implementation in this area are far from complete. This project will work towards establishing a sound theory for characterizing and explaining EDA-like algorithms. We will extend our previous results and seek to obtain global convergence conditions for the factorised distribution algorithm and ant-colony optimisation algorithms. With a better understanding of the working mechanisms of EDA-like algorithms, we will embed experimental design methods in EDA to develop more powerful and statistically sound search and optimisation algorithms. We will also exploit the potential benefits of combining EDA-like algorithms with GLS (Guided Local Search). We propose to use continuous optimisation problems and quadratic assignment problems as our test problems. This project is funded by the EPSRC and University of Essex.
Finance, Prediction, Classification and Modelling. Click on the picture on the right to see how a genetic programming system can discover an expression that bests fit a set of datapoints (symbolic regression).
Automatic Engineering Design. Click on the picture on the right to discover how an evolutionary algorithm can routinely discover parallel digital circuits implementing a set of user-specified requirements
Image and Signal Processing. Many image analysis tasks can be seen as hard optimisation problems. Evolutionary algorithms are natural candidates to solve this kind of problems. The objective of this research stream is the development of new algorithms for the solution of some of the main problems in image understanding such as image enhancement and segmentation, content-based image retrieval, 2-D and 3-D shape representation. In previous research we have successfully applied evolutionary techniques, in particular genetic algorithms and genetic programming, to the analysis of medical images and signals and to image enhancement problems. Click on the pictures on the right to discover how genetic programming can routinely discover image processing algorithms for a variety of complex tasks.
Control, Robotics. The Associative Experience Engine (AEE) (British Patent 99-10539.7) is a fuzzy genetic system (click on the diagram on the right to see its structure) that has been applied to difficult and challenging problems such as controlling a mobile robot in an outdoor environment, controlling intelligent building and even controlling large diesel engines (see image on the left).
For more information contact:
Professor R Poli
Department of Computing and Electronic Systems, University of Essex, Colchester CO4 3SQ UK
Tel: +44 1206 872338
Fax: +44 1206 872788
Email: rpoli @ essex . ac . uk