Content-targeted advertising, the task of automatically associating ads to a Web page, constitutes a key Web monetization strategy nowadays. Further, it introduces new challenging technical problems and raises interesting questions. For instance, how to design ranking functions able to satisfy conflicting goals such as selecting advertisements (ads) that are relevant to the users and suitable and profitable to the publishers and advertisers? In this paper we propose a new framework for associating ads with web pages based on Genetic Programming (GP). Our GP method aims at learning functions that select the most appropriate ads, given the contents of a Web page. These ranking functions are designed to optimize overall precision and minimize the number of misplacements. By using a real ad collection and web pages from a newspaper, we obtained a gain over a state-of-the-art baseline method of 61.7% in average precision. Further, by evolving individuals to provide good ranking estimations, GP was able to discover ranking functions that are very effective in placing ads in web pages while avoiding irrelevant ones.

http://www.dcc.ufmg.br/~nivio/papers/sigir06.pdf

Learning to advertise

Genetic-based evolutionary learning algorithms, such as genetic algorithms (GAs) and genetic programming (GP), have been applied to information retrieval (IR) since the 1980s. Recently, GP has been applied to a new IR task-discovery of ranking functions for Web search-and has achieved very promising results. However, in our prior research, only one fitness function has been used for GP-based learning. It is unclear how other fitness functions may affect ranking function discovery for Web search, especially since it is well known that choosing a proper fitness function is very important for the effectiveness and efficiency of evolutionary algorithms. In this article, we report our experience in contrasting different fitness function designs on GP-based learning using a very large Web corpus. Our results indicate that the design of fitness functions is instrumental in performance improvement. We also give recommendations on the design of fitness functions for genetic-based information retrieval experiments.

The effects of fitness functions on genetic programming-based ranking discovery for Web search

Hybrid evolutionary algorithms in a SVR traffic flow forecasting model

As we know, simulated annealing algorithm with large neighborhoods has greater probability of arriving at a global optimum than a small one has, if the other conditions, i.e., the initial configuration, initial temperature and temperature decreasing rate, are the same. However, the large neighborhood is not always beneficial, such as when the distance between the global optimum and the current solution is smaller than the step size. Therefore a simulated annealing algorithm with adaptive neighborhood is proposed in this paper. The adaptive non-uniform mutation operation borrowed from evolutionary algorithm is incorporated into simulated annealing for new solution generation. The neighborhood size reduces in probability with the progress of algorithm. It nearly covers the whole search space in the initial stage of algorithm in the sense of probability. The search engine only searches a very local neighborhood at the later stage of algorithm. Why to hybridize non-uniform mutation with simulated annealing is also analyzed and demonstrated. The numerical experiments show that the hybridization can greatly enhance the performance and the reliability of simulated annealing algorithm. Further experiments are made for benchmarks with expanding search domains. Satisfiable results are obtained again even if the variable bounds are enlarged to 1000 times. Theoretical analysis and simulation experiments illustrate the consistent excellent performance and the possible application of nonu-SA algorithm.

Simulated annealing algorithm with adaptive neighborhood

A hybrid evolutionary search algorithm is developed to optimize the classical single-criterion operation of multi-reservoir systems. The proposed improved genetic algorithm-simulated annealing (IGA-SA) which combines genetic algorithms (GAs) and the simulated annealing (SA) is a new global optimization algorithm. The algorithm is capable of overcoming the premature convergence of GAs and escaping from local optimal solutions. In addition, it is faster than a traditional unimproved GA-SA algorithm. A case study of optimization operation on generation electricity of a 3-reservoir system in series over 41-year (from May 1940 to April 1981) time periods in Wujiang River, one branch of Yangtze River in China, was performed. The objective is to maximize generation output from the system over each 12-month operating periods. Trade-off analyses on binary coding representation and real-value coding representation of GAs are performed. Sensitivity to some parameters of the GA, the SA and the IGA-SA is analyzed, respectively, and the appropriate values of parameters are suggested. The performance of the proposed algorithm is compared with that of the existing genetic algorithm, the simulated annealing and the dynamic programming (DP). Results demonstrate that the GA is better than the DP, the SA performs better than the GA and the IGA-SA is more efficient than SA. The IGA-SA produces higher quality solutions and costs less computation time compared with the traditional GA-SA. The results obtained from these applications have proved that the IGA-SA has the ability of addressing large and complex problems and is a new promising search algorithm for multi-reservoir optimization problems.

An Improved Genetic Algorithm-Simulated Annealing Hybrid Algorithm for the Optimization of Multiple Reservoirs

Relevance feedback techniques have demonstrated to be a powerful means to improve the results obtained when a user submits a query to an information retrieval system as the world wide web search engines. These kinds of techniques modify the user original query taking into account the relevance judgements provided by him on the retrieved documents, making it more similar to those he judged as relevant. This way, the new generated query permits to get new relevant documents thus improving the retrieval process by increasing recall. However, although powerful relevance feedback techniques have been developed for the vector space information retrieval model and some of them have been translated to the classical Boolean model, there is a lack of these tools in more advanced and powerful information retrieval models such as the fuzzy one. In this contribution we introduce a relevance feedback process for extended Boolean (fuzzy) information retrieval systems based on a hybrid evolutionary algorithm combining simulated annealing and genetic programming components. The performance of the proposed technique will be compared with the only previous existing approach to perform this task, Kraft et al.'s method, showing how our proposal outperforms the latter in terms of accuracy and sometimes also in time consumption. Moreover, it will be showed how the adaptation of the retrieval threshold by the relevance feedback mechanism allows the system effectiveness to be increased.

/pdf/a-new-evolutionary-algorithm-combining-simulated-annealing-4a1nla0cd2.pdf

A new evolutionary algorithm combining simulated annealing and genetic programming for relevance feedback in fuzzy information retrieval systems

In this contribution, a new Inductive Query by Example process is proposed to automatically derive extended Boolean queries for fuzzy information retrieval systems from a set of relevant documents provided by a user. The novelty of our approach is that it is able to simultanously generate several queries with a different precision-recall tradeoff in a single run. To do so, it is based on an advanced. evolutionary algorithm, GA-P, specially designed to tackle with multiobjective problems by means of a Pareto-based multiobjective technique. The performance of the new proposal will be tested on the usual Cranfield collection and compared to the well-known Kraft et al.’s process.

Automatic Learning of Multiple Extended Boolean Queries by Multiobjective GA-P Algorithms

The fuzzy information retrieval model was proposed some years ago to solve several limitations of the Boolean model without a need of a complete redesign of the information retrieval system. However, the complexity of the fuzzy query language makes it difficult to formulate user queries. Among other proposed approaches to solve this problem, we find the Inductive Query by Example (IQBE) framework, where queries are automatically derived from sets of documents provided by the user. In this work we test the applicability of a multiobjective evolutionary IQBE technique for fuzzy queries in a machine learning environment. To do so, the Cranfield documentary collection is divided into two different document sets, labeled training and test, and the algorithm is run on the former to obtain several queries that are then validated on the latter.

Analyzing the performance of a multiobjective GA-P algorithm for learning fuzzy queries in a machine learning environment

The eventual adaptation of archives to new technological possibilities could begin with the creation of digital versions of archival finding aids, which would allow the international diffusion of descriptive information. The Standard Generalized Markup Language (SGML), document type definition (DTD) for archival description known as encoded archival description (EAD) is an appropriate tool for this purpose. Presents a methodological strategy that begins with an analysis of EAD and the informational object to be marked up, allowing the semiautomatic creation of a digital version.

Encoded archival description (EAD) conversion: a methodological proposal

Present management of information resources is inconceivable without the web. For the handling, preservation, dissemination and access to electronic information on the Net, a standard is needed to ensure independence from manufacturers and to take full advantage of the hypertext and hypermedia capabilities of the web. This paper presents the sgml standard and its "family" of variations (xml, xsl, xlink) as potentially ideal for information service applications.

Félix de Moya

Papers

A new evolutionary algorithm combining simulated annealing and genetic programming for relevance feedback in fuzzy information retrieval systems

Automatic Learning of Multiple Extended Boolean Queries by Multiobjective GA-P Algorithms

Analyzing the performance of a multiobjective GA-P algorithm for learning fuzzy queries in a machine learning environment

Encoded archival description (EAD) conversion: a methodological proposal

Sgml y servicios de información Por Eduardo Peis y Félix de Moya