PoK

Author:Dudy Lim,Yew Soon Ong,Yao chu Jin,Bernhard Sendhoff,Bu Sung Lee

Description:
In this paper, we present an efficient Hierarchical Parallel Genetic Algorithm frame work using Grid computing (GEHPGA). The framework is developed using stan dard Grid technologies and has two distinctive features, 1) an extended GridRPC API to conceal the high complexity of Grid environment, and 2) a metascheduler for seamless resource discovery and selection. To assess the practicality of the frame work, theoretical analysis on the possible speed up o®ered is presented. Empirical study on GEHPGA using a benchmark problem and a realistic aerodynamic airfoil shape optimization problem for diverse Grid environments having di®erent com munication protocols, cluster sizes, processing nodes, at geographically disparate locations also indicates that the proposed GE HPGA using Grid computing oers a credible framework for providing significant speed up to evolutionary design optimization in science and engineering.

Author:Hsinchun Chen

Description:
Information retrieval using probabilistic techniques has attracted significant attention on the part of researchers in information and computer science over the past few decades. In the 198Os, knowledge based techniques also made an impressive contribution to “intelligent” information retrieval and indexing. More recently, information science researchers have turned to other newer artificial intelligence based inductive learning techniques including neural networks, symbolic learning, and genetic algorithms. These newer techniques, which are grounded on diverse paradigms, have provided great opportunities for researchers to enhance the information processing and retrieval capabilities of current information storage and retrieval systems. In this article, we first provide an overview of these newer techniques and their use in information science research. To familiarize readers with these techniques,we present three popular methods: the connectionist Hopfield network; the symbolic ID3/ID5R and evolution based genetic algorithms. We discuss their knowledge representations and algorithms in the context of information retrieval. Sample implementation and testing results from our own research are also provided for each technique. We believe these techniques are promising in their ability to analyze user queries, identify users information needs, and suggest alternatives for search. With proper user system interactions, these methods can greatly complement the prevailing full-text, keywordbased,probabilistic, and knowledge based techniques.

Author:ENRIC PLAZA, LORRAINE MCGINTY

Description:
Distribution of resources within case based reasoning (CBR) architectures is beneficial in a variety of application contexts. This article briefly discusses some of the approaches that fall under the heading of distributed CBR, and their general impact.

Author:E.K.Burke, B.MacCarthy, S.Petrovic, R.Qu

Description:
An earlier Case based Reasoning (CBR) approach developed by the authors for educational course timetabling problems employed structured cases to represent the complex relationships between courses. The retrieval searches for structurally similar cases in the case base. In this paper, the approach is further developed to solve a wider range of problems. We also attempt to retrieve those cases that have common similar structures with some differences. Costs that are assigned to these differences have an input upon the similarity measure. A large number of experiments are performed consisting of different randomly produced timetabling problems and the results presented here strongly indicate that a CBR approach could provide a significant step forward in the development of automated systems to solve difficult timetabling problems. They show that using relatively little effort, we can retrieve these tructurally similar cases to provide high quality timetables for new timetabling problems.

Author:Stefania Montani, Riccardo Bellazzi

Description:
We present a Web based knowledge management and decision support system for Type I Diabetes patients care. The tool exploits the integration of two methodologies, Case Based Reasoning and Rule Based Reasoning, and supports physicians in the definition of therapeutic strategies. Such a work is being integrated in the EU funded T-IDDM project architecture. In this paper we report a first evaluation obtained on simulated patients.