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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
Tracking moving optima using Kalman-based predictions
The dynamic optimization problem concerns finding an optimum in a changing environment. In the field of evolutionary algorithms, this implies dealing with a timechanging fitness landscape. In this paper we compare different techniques for integrating motion information into an evolutionary algorithm, in the case it has to follow a time-changing optimum, under the assumption that the changes follow a nonrandom law. Such a law can be estimated in order to improve the optimum tracking capabilities of the algorithm. In particular, we will focus on first order dynamical laws to track moving objects. A vision-based tracking robotic application is used as testbed for experimental comparison
Exploration vs. Exploitation in the Information Filtering Problem
We consider information filtering, in which we face a stream of items too
voluminous to process by hand (e.g., scientific articles, blog posts, emails),
and must rely on a computer system to automatically filter out irrelevant
items. Such systems face the exploration vs. exploitation tradeoff, in which it
may be beneficial to present an item despite a low probability of relevance,
just to learn about future items with similar content. We present a Bayesian
sequential decision-making model of this problem, show how it may be solved to
optimality using a decomposition to a collection of two-armed bandit problems,
and show structural results for the optimal policy. We show that the resulting
method is especially useful when facing the cold start problem, i.e., when
filtering items for new users without a long history of past interactions. We
then present an application of this information filtering method to a
historical dataset from the arXiv.org repository of scientific articles.Comment: 36 pages, 5 figure
Computational intelligence approaches to robotics, automation, and control [Volume guest editors]
No abstract available
Optimisation of Mobile Communication Networks - OMCO NET
The mini conference âOptimisation of Mobile Communication Networksâ focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University.
The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing
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