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

A Multi-Gene Genetic Programming Application for Predicting Students Failure at School

Several efforts to predict student failure rate (SFR) at school accurately still remains a core problem area faced by many in the educational sector. The procedure for forecasting SFR are rigid and most often times require data scaling or conversion into binary form such as is the case of the logistic model which may lead to lose of information and effect size attenuation. Also, the high number of factors, incomplete and unbalanced dataset, and black boxing issues as in Artificial Neural Networks and Fuzzy logic systems exposes the need for more efficient tools. Currently the application of Genetic Programming (GP) holds great promises and has produced tremendous positive results in different sectors. In this regard, this study developed GPSFARPS, a software application to provide a robust solution to the prediction of SFR using an evolutionary algorithm known as multi-gene genetic programming. The approach is validated by feeding a testing data set to the evolved GP models. Result obtained from GPSFARPS simulations show its unique ability to evolve a suitable failure rate expression with a fast convergence at 30 generations from a maximum specified generation of 500. The multi-gene system was also able to minimize the evolved model expression and accurately predict student failure rate using a subset of the original expressionComment: 14 pages, 9 figures, Journal paper. arXiv admin note: text overlap with arXiv:1403.0623 by other author

REFELCTIONS ON THE (RE)POSITIONING OF THE FINANCIAL ADMINISTRATION CONTROL

The financial administration control offers the managing staff the possibility to decide on the entrusted methods and means and to direct its action in order to reach the objectives that were set out according to the main interests of each company. The objectives are set down by taking into consideration both managing and operative functions. However, the desired results are obtained only after a certain period of time, during which there may occur changes of the initial context within which the objectives and action plans were drawn up. In this case the readjustment of the financial administration control within the managerial control structure becomes an essential condition for successfully reaching the established targets. A condition as such comes from the description of the desired outcome, of the indicators that will be used to assess it and the necessary means to obtain it. The objectives also have to express the agreement reached between hierarchic levels on dialogue grounds, thus ensuring the harmonization of the collective interests with those that determine the functioning of the enterprise. A very important part in this dialogue is played by the instrumental panel and also by the other devices of the financial administration control, which will have to be readjusted in a proper manner according to the changes occurred within the economic and business environment.financial administration control, managing and operative functions, performance, instrumental panel.

Using attribute construction to improve the predictability of a GP financial forecasting algorithm

Financial forecasting is an important area in computational finance. EDDIE 8 is an established Genetic Programming financial forecasting algorithm, which has successfully been applied to a number of international datasets. The purpose of this paper is to further increase the algorithm’s predictive performance, by improving its data space representation. In order to achieve this, we use attribute construction to create new (high-level) attributes from the original (low-level) attributes. To examine the effectiveness of the above method, we test the extended EDDIE’s predictive performance across 25 datasets and compare it to the performance of two previous EDDIE algorithms. Results show that the introduction of attribute construction benefits the algorithm, allowing EDDIE to explore the use of new attributes to improve its predictive accuracy

A Comparative Study on the Use of Classification Algorithms in Financial Forecasting

Financial forecasting is a vital area in computational finance, where several studies have taken place over the years. One way of viewing financial forecasting is as a classification problem, where the goal is to find a model that represents the predictive relationships between predictor attribute values and class attribute values. In this paper we present a comparative study between two bio-inspired classification algorithms, a genetic programming algorithm especially designed for financial forecasting, and an ant colony optimization one, which is designed for classification problems. In addition, we compare the above algorithms with two other state-of-the-art classification algorithms, namely C4.5 and RIPPER. Results show that the ant colony optimization classification algorithm is very successful, significantly outperforming all other algorithms in the given classification problems, which provides insights for improving the design of specific financial forecasting algorithms

Using Eco-schemes in the new CAP: a guide for managing authorities

This guide has been developed primarily for policy makers and Member State officials involved in the national and regional programming processes of the CAP Strategic Plans (CSPs). This process might involve different administrative levels (national, regional, local), different political fields (agriculture, environmental, food and health ministries), different public bodies (paying agencies, environmental agencies, rural development offices) depending on the administrative setting of each MS. In addition, the guide provides support to other stakeholders and practitioners from the public and private sectors and civil society (including agricultural, environmental, food, health and consumer NGOs), with a direct or indirect involvement in the programming and evaluation process of the CSPs. Since these new plans will have a strong impact on MS environments, agricultural sectors, rural areas, etc., the engagement of all stakeholders will be an important asset for supporting an effective implementation of the CSP objectives. There are many others with potential interests in the contents of this guide. EU citizens have demonstrated their increasing interest in the contents of the CAP objectives and policy framework, as demonstrated both by civil society initiatives and consumption decisions. The contents of this guide may therefore also be of interest to other societal actors with interests in agricultural and environmental policies, such as researchers, journalists, trade unions, and civil society organizations. However, the guide is intentionally more focused on the technical needs of those involved in CSP development and implementation