Thematic issue on evolutionary algorithms in water resources

Special Issue on Evolutionary Algorithms.H.R. Maier, Z. Kapelan, J. Kasprzyk, L.S. Matot

Local search: A guide for the information retrieval practitioner

There are a number of combinatorial optimisation problems in information retrieval in which the use of local search methods are worthwhile. The purpose of this paper is to show how local search can be used to solve some well known tasks in information retrieval (IR), how previous research in the field is piecemeal, bereft of a structure and methodologically flawed, and to suggest more rigorous ways of applying local search methods to solve IR problems. We provide a query based taxonomy for analysing the use of local search in IR tasks and an overview of issues such as fitness functions, statistical significance and test collections when conducting experiments on combinatorial optimisation problems. The paper gives a guide on the pitfalls and problems for IR practitioners who wish to use local search to solve their research issues, and gives practical advice on the use of such methods. The query based taxonomy is a novel structure which can be used by the IR practitioner in order to examine the use of local search in IR

Sparse experimental design : an effective an efficient way discovering better genetic algorithm structures

The focus of this paper is the demonstration that sparse experimental design is a useful strategy for developing Genetic Algorithms. It is increasingly apparent from a number of reports and papers within a variety of different problem domains that the 'best' structure for a GA may be dependent upon the application. The GA structure is defined as both the types of operators and the parameters settings used during operation. The differences observed may be linked to the nature of the problem, the type of fitness function, or the depth or breadth of the problem under investigation. This paper demonstrates that advanced experimental design may be adopted to increase the understanding of the relationships between the GA structure and the problem domain, facilitating the selection of improved structures with a minimum of effort

Results of Evolution Supervised by Genetic Algorithms

A series of results of evolution supervised by genetic algorithms with interest to agricultural and horticultural fields are reviewed. New obtained original results from the use of genetic algorithms on structure-activity relationships are reported.Comment: 6 pages, 1 Table, 2 figure

A comprehensive survey on cultural algorithms

Peer reviewedPostprin

Update of an early warning fault detection method using artificial intelligence techniques

This presentation describes a research investigation to access the feasibility of using an Artificial Intelligence (AI) method to predict and detect faults at an early stage in power systems. An AI based detector has been developed to monitor and predict faults at an early stage on particular sections of power systems. The detector for this early warning fault detection device only requires external measurements taken from the input and output nodes of the power system. The AI detection system is capable of rapidly predicting a malfunction within the system. Artificial Neural Networks (ANNs) are being used as the core of the fault detector. In an earlier paper [11], a computer simulated medium length transmission line has been tested by the detector and the results clearly demonstrate the capability of the detector. Today’s presentation considers a case study illustrating the suitability of this AI Technique when applied to a distribution transformer. Furthermore, an evolutionary optimisation strategy to train ANNs is also briefly discussed in this presentation, together with a ‘crystal ball’ view of future developments in the operation and monitoring of transmission systems in the next millennium