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

Online Genetic Algorithms

This paper present a technique based on genetic algorithms for generating online adaptive services. Online adaptive systems provide flexible services to a mass of clients/users for maximising some system goals, they dynamically adapt the form and the content of the issued services while the population of clients evolve over time. The idea of online genetic algorithms (online GAs) is to use the online clients response behaviour as a fitness function in order to produce the next generation of services. The principle implemented in online GAs, “the application environment is the fitness”, allow modelling highly evolutionary domains where both services providers and clients change and evolve over time. The flexibility and the adaptive behaviour of this approach seems to be very relevant and promising for applications characterised by highly dynamical features such as in the web domain (online newspapers, e- markets, websites and advertising engines). Nevertheless the proposed technique has a more general aim for application environments characterised by a massive number of anonymous clients/users which require personalised services, such as in the case of many new IT applications

Towards the automation of book typesetting

This paper proposes a generative approach for the automatic typesetting of books in desktop publishing. The presented system consists in a computer script that operates inside a widely used design software tool and implements a generative process based on several typographic rules, styles and principles which have been identified in the literature. The performance of the proposed system is tested through an experiment which included the evaluation of its outputs with people. The results reveal the ability of the system to consistently create varied book designs from the same input content as well as visually coherent book designs with different contents while complying with fundamental typographic principles.Comment: 26 pages, 5 figures. Revised version published at Visual Informatics, 7(2), pp. 1\textendash{}1

Algoritmo Luciérnaga para optimización de layout de distribución en planta

This paper shows the result of a research about the applications of bio-inspired algorithms in the field of production engineering in the Distrital University Francisco José de Caldas, covering the topics of industrial layout distribution in manufacturing plant layout. It is intended to seek the optimization of some problems of those fields, using artificial intelligence from the implementation of a firefly algorithm as metaheuristic planning tool and optimization of layout problem. With the goal of finding the best spatial allocation of work stations or cells. Theoretical concepts explored and results are presented. First, a state-of-the-art review on the subject was made, and then the possible solution algorithms were evaluated to identify the objective function to be optimized, to finally apply the firefly algorithm, and evaluate the results of performance against the Initial layout as the plant.Este trabajo muestra el resultado de una investigación sobre las aplicaciones de los algoritmos bioinspirados en el campo de la ingeniería de producción en la Universidad Distrital Francisco José de Caldas, abarcando los temas de distribución de layout industrial en planta de fabricación. Se pretende buscar la optimización de algunos problemas de dichos campos, utilizando la inteligencia artificial a partir de la implementación de un algoritmo de luciérnaga como herramienta metaheurística de planificación y optimización del problema de layout. Con el objetivo de encontrar la mejor asignación espacial de los puestos de trabajo o celdas. Se presentan los conceptos teóricos explorados y los resultados obtenidos. Primero se hizo una revisión del estado del arte sobre el tema, y luego se evaluaron los posibles algoritmos de solución para identificar la función objetivo a optimizar, para finalmente aplicar el algoritmo de la luciérnaga, y evaluar los resultados de desempeño frente al layout Inicial como la planta

Crossword Construction using Constraint Satisfaction and Simulated Annealing

Selle töö eesmärk on luua programm, mis koostab ristsõnu, kasutades kahte meetodit: kitsenduste rahuldamist (KR) ahne algoritmiga ja libalõõmutamist (LL), ning võrrelda nende meetodite efektiivsust. Tööd hakatakse kasutama õppematerjalina aine Tehisintellekt I õpetamisel. Ristsõna koostamine on üks tehisintellekti probleemidest, mis kuulub NP-täielike klassi. Seega hea lahenduse leidmine nõuab palju ressursse ja aega. Aga eksisteerivad meetodid, mis võimaldavad lahenduse leidmise aega vähendada. Nende hulgas on ka KR ja LL. KR kasutades seatakse antud ülesandele kitsendusi, mis teevad lahendamise lihtsamaks. Ristsõna koostamisel kehtivad järgmised kitsendused: 1.Sõna ei saa olla lühem, kui ruutude järjend, kuhu seda pannakse. 2.Sõna ei saa olla pikem, kui ruutude järjend, kuhu seda pannakse. 3.Kui järjendis on mõned tähed juba olemas, siis sõna, mis pannakse sellesse järjendisse, peab neid tähti sisaldama täpselt nendel samadel positsioonidel ja ei saa sisaldada mingeid teisi tähti nendel positsioonidel. Kui sõna rahuldab neid tingimusi, siis teda võetakse vastu ning ahne algoritm otsustab, kasutades heuristilist funktsiooni, kas see sõna on parim lahendus selles olukorras.Niiviisi püüab programm lõpliku sammude hulgaga optimaalse lahenduseni jõuda. LL töötab nii: antud on suvaline algseisund s, leida tema naaberseisund s', kui uus seisund on jooksvast seisundist parem, siis valida see, aga kui leitud seisund on jooksvast seisundist halvem, siis kasutada tõenäosus funktsiooni P, et otsustada, kas valida seda seisundit või mitte. Sellist operatsiooni korratakse kuni rahuldav lahendus on leitud või algoritm on juba teinud lubatud arvu samme. Tõenäosus, et algoritm valib uueks seisundiks halvema seisundi väheneb aja jooksul (kooskõlas nn temperatuuri alanemisega). Meetodeid on testitud ja võrreldid, kasutades erinevaid heuristikuid.The main goal of this thesis is to create a program that allows constructing crosswords, using two different algorithms. Given a grid and a text file with words (dictionary), the program should search for suitable words from a dictionary to fill the grid. The program should be able to complete this task in two different ways, in this case using constraint satisfaction method (CSM) with greedy algorithm and simulated annealing. Afore-mentioned algorithms were chosen mainly for educational purposes, since construction of the fastest algorithm is not a goal of this work. Along with other similar Artificial Intelligence problems, like N queens problem, map colouring and Sudoku solving (which is also NP-complete problem), crossword construction is a good example of simple, yet nontrivial task. The choice of CSM with greedy algorithm is obvious. If there are no constraints, the program will simply try to fill each entry by placing up to all, and that means also the words that are of inappropriate length, words in vocabulary until it finds first suitable or runs out of words. For example, by putting constraints on words length and already filled letters, the construction time can be drastically reduced. The simulated annealing was chosen with intention to show that the same problem can be solved in different ways and also to illustrate the difference in algorithm processing and its effectiveness. In addition, simulated annealing is quite similar to greedy algorithm, thus making their comparison a bit easier, but more interesting

A Massively Parallel 2D Rectangle Placement Method

Layout design is a frequently occurring process that oftencombines human and computer reasoning. Because of the combinatorialnature of the problem, solving even a small size input involves searchinga prohibitively large state space. An algorithm PEMS (Pseudo-exhaustiveEdge Minimizing Search) is proposed for approximating a 2D rectanglepacking variant of the problem. The proposed method is inspiredby MERA (Minimum Enclosing of Rectangle Area) [1] and MEGA(Minimum Enclosing Under Gravitational Attraction) [2], yet produceshigher quality solutions, in terms of final space utilization. To addressthe performance cost, a CUDA based acceleration algorithm is developedwith significant speedup

An Overview of the Use of Neural Networks for Data Mining Tasks

In the recent years the area of data mining has experienced a considerable demand for technologies that extract knowledge from large and complex data sources. There is a substantial commercial interest as well as research investigations in the area that aim to develop new and improved approaches for extracting information, relationships, and patterns from datasets. Artificial Neural Networks (NN) are popular biologically inspired intelligent methodologies, whose classification, prediction and pattern recognition capabilities have been utilised successfully in many areas, including science, engineering, medicine, business, banking, telecommunication, and many other fields. This paper highlights from a data mining perspective the implementation of NN, using supervised and unsupervised learning, for pattern recognition, classification, prediction and cluster analysis, and focuses the discussion on their usage in bioinformatics and financial data analysis tasks