14,745 research outputs found
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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
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An experimental comparison of a genetic algorithm and a hill-climber for term selection
Purpose â The term selection problem for selecting query terms in information filtering and routing has been investigated using hill-climbers of various kinds, largely through the Okapi experiments in the TREC series of conferences. Although these are simple deterministic approaches which examine the effect of changing the weight of one term at a time, they have been shown to improve the retrieval effectiveness of filtering queries in these TREC experiments. Hill-climbers are, however, likely to get trapped in local optima, and the use of more sophisticated local search techniques for this problem that attempt to break out of these optima are worth investigating. To this end, we apply a genetic algorithm (GA) to the same problem.
Design/Methodology/Approach â We use a standard TREC test collection from the TREC-8 filtering track, recording mean average precision and recall measures to allow comparison between the hillclimber and GA algorithms. We also vary elements of the GA, such as probability of a word being included, probability of mutation and population size in order to measure the effect of these variables. Different strategies such as Elitist and Non-Elitist methods are used, as well as Roulette Wheel and Rank selection GA algorithms.
Findings â The results of tests suggest that both techniques are, on average, better than the baseline, but the implemented GA does not match the overall performance of a hill-climber. The Rank selection algorithm does better on average than the Roulette Wheel algorithm. There is no evidence in this study that varying word inclusion probability, mutation probability or Elitist method make much difference to the overall results. Small population sizes do not appear to be as effective as larger population sizes.
Research limitations/implications â The evidence provided here would suggest that being stuck in a local optima for the term selection optimization problem does not appear to be detrimental to the overall success of the hill-climber. The evidence from term rank order would appear to provide extra useful evidence which hill-climbers can use efficiently and effectively to narrow the search space.
Originality/Value â The paper represents the first attempt to compare hill-climbers with GAs on a problem of this type
Optimal Parameter Choices Through Self-Adjustment: Applying the 1/5-th Rule in Discrete Settings
While evolutionary algorithms are known to be very successful for a broad
range of applications, the algorithm designer is often left with many
algorithmic choices, for example, the size of the population, the mutation
rates, and the crossover rates of the algorithm. These parameters are known to
have a crucial influence on the optimization time, and thus need to be chosen
carefully, a task that often requires substantial efforts. Moreover, the
optimal parameters can change during the optimization process. It is therefore
of great interest to design mechanisms that dynamically choose best-possible
parameters. An example for such an update mechanism is the one-fifth success
rule for step-size adaption in evolutionary strategies. While in continuous
domains this principle is well understood also from a mathematical point of
view, no comparable theory is available for problems in discrete domains.
In this work we show that the one-fifth success rule can be effective also in
discrete settings. We regard the ~GA proposed in
[Doerr/Doerr/Ebel: From black-box complexity to designing new genetic
algorithms, TCS 2015]. We prove that if its population size is chosen according
to the one-fifth success rule then the expected optimization time on
\textsc{OneMax} is linear. This is better than what \emph{any} static
population size can achieve and is asymptotically optimal also among
all adaptive parameter choices.Comment: This is the full version of a paper that is to appear at GECCO 201
State-of-the-art review on relevance of genetic algorithm to internet web search
People use search engines to find information they desire with the aim that their information needs will be met. Information
retrieval (IR) is a field that is concerned primarily with the searching and retrieving of information in the documents and also
searching the search engine, online databases, and Internet. Genetic algorithms (GAs) are robust, efficient, and optimizated
methods in a wide area of search problems motivated by Darwinâs principles of natural selection and survival of the fittest. This
paper describes information retrieval systems (IRS) components. This paper looks at how GAs can be applied in the field of IR and
specifically the relevance of genetic algorithms to internet web search. Finally, from the proposals surveyed it turns out that GA is
applied to diverse problem fields of internet web search
A review on the application of evolutionary computation to information retrieval
In this contribution, different proposals found in the specialized literature for the
application of evolutionary computation to the field of information retrieval will be
reviewed. To do so, different kinds of IR problems that have been solved by evolutionary
algorithms are analyzed. Some of the specific existing approaches will be specifically
described for some of these problems and the obtained results will be critically
evaluated in order to give a clear view of the topic to the reader.CICYT under project TIC2002-03276University of Granada under project ââMejora de Metaheur ısticas mediante Hibridaci on y sus
Aplicaciones
Monotonicity of Fitness Landscapes and Mutation Rate Control
A common view in evolutionary biology is that mutation rates are minimised.
However, studies in combinatorial optimisation and search have shown a clear
advantage of using variable mutation rates as a control parameter to optimise
the performance of evolutionary algorithms. Much biological theory in this area
is based on Ronald Fisher's work, who used Euclidean geometry to study the
relation between mutation size and expected fitness of the offspring in
infinite phenotypic spaces. Here we reconsider this theory based on the
alternative geometry of discrete and finite spaces of DNA sequences. First, we
consider the geometric case of fitness being isomorphic to distance from an
optimum, and show how problems of optimal mutation rate control can be solved
exactly or approximately depending on additional constraints of the problem.
Then we consider the general case of fitness communicating only partial
information about the distance. We define weak monotonicity of fitness
landscapes and prove that this property holds in all landscapes that are
continuous and open at the optimum. This theoretical result motivates our
hypothesis that optimal mutation rate functions in such landscapes will
increase when fitness decreases in some neighbourhood of an optimum, resembling
the control functions derived in the geometric case. We test this hypothesis
experimentally by analysing approximately optimal mutation rate control
functions in 115 complete landscapes of binding scores between DNA sequences
and transcription factors. Our findings support the hypothesis and find that
the increase of mutation rate is more rapid in landscapes that are less
monotonic (more rugged). We discuss the relevance of these findings to living
organisms
Evolution engine technology in exhaust gas recirculation for heavy-duty diesel engine
In this present year, engineers have been researching and inventing to get the optimum of less emission in every vehicle for a better environmental friendly. Diesel engines are known reusing of the exhaust gas in order to reduce the exhaust emissions such as NOx that contribute high factors in the pollution. In this paper, we have conducted a study that EGR instalment in the vehicle can be good as it helps to prevent highly amount of toxic gas formation, which NOx level can be lowered. But applying the EGR it can lead to more cooling and more space which will affect in terms of the costing. Throughout the research, fuelling in the engine affects the EGR producing less emission. Other than that, it contributes to the less of performance efficiency when vehicle load is less
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Interactive product catalogue with user preference tracking
In the context of m-commerce, small screen size poses serious difficulty for users to browse effectively through a product catalogue, given the limited number of products that may be presented on-screen. Despite the availability of search engines, filters and recommender systems to aid users, these techniques focus on a narrow segment of product offering. The users are thus denied the opportunity to do a more expansive exploration of the products available. This paper describes a novel approach to overcome the constraints of small screen size. Through integration of a product catalogue with a recommender system, an adaptive system has been created that guides users through the process of product browsing. An original technique has been developed to cluster similar positive examples together to identify areas of interest of a user. The performance of this technique has been evaluated and the results proved to be promising
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