To locate and to retrieve the needed information from the Internet is an important issue. Existing search engines may give too much useless and redundancy information. Due to the search feature is different for different search engines, it¡¦s very difficult to find an optimal search scheme for all subjects. In this paper, we propose a genetic programming web search system (GPWS) to generate exact query according to a user¡¦s interests. The system can retrieve the information from the search engines, filter the retrieved results and remove the redundancy and useless results. The filtered results are displayed on a uniform user interface. Compared with the queries generated by randomly, the degree of similarity of results and user¡¦s interests are improved.

Thesis (M. Phil.)--University of Hong Kong, 2001. / Includes bibliographical references (leaves 71-76).

Classification under streaming data constraints implies that training must be performed continuously, can only access individual exemplars for a short time after they arrive, must adapt to dynamic behaviour over time, and must be able to retrieve a current classifier at any time. A coevolutionary genetic programming framework is adapted to operate in non-stationary streaming data environments. Methods to generate synthetic datasets for benchmarking streaming classification algorithms are introduced, and the proposed framework is evaluated against them. The use of Pareto archiving is evaluated as a mechanism for retaining access to a limited number of useful exemplars throughout training, and several fitness sharing heuristics for archiving are evaluated. Fitness sharing alone is found to be most effective under streams with continuous (incremental) changes, while the addition of an aging heuristic is preferred when the stream has stepwise changes. Tapped delay lines are explored as a method for explicitly incorporating sequence context in cyclical data streams, and their use in combination with the aging heuristic suggests a promising route forward. / Hyperref'd copy available at: https://web.cs.dal.ca/~atwater/

Classification under streaming data conditions requires that the machine learning approach operate interactively with the stream content. Thus, given some initial machine learning classification capability, it is not possible to assume that the process `generating' stream content will be stationary. It is therefore necessary to first detect when the stream content changes. Only after detecting a change, can classifier retraining be triggered. Current methods for change detection tend to assume an entropy filter approach, where class labels are necessary. In practice, labeling the stream would be extremely expensive. This work proposes an approach in which the behavior of GP individuals is used to detect change without} the use of labels. Only after detecting a change is label information requested. Benchmarking under three computer network traffic analysis scenarios demonstrates that the proposed approach performs at least as well as the filter method, while retaining the advantage of requiring no labels.

This thesis proposes two new variations on the genetic algorithm. The first attempts to improve clustering problems by optimizing the structure of a genetic string dynamically during the run of the algorithm. This is done by using a permutation on the allele which is inherited by the next generation. The second is a multiple pool technique which ensures continuing convergence by maintaining unique lineages and merging pools of similar age. These variations will be tested against two well-known graph theory problems, the Traveling Salesman Problem and the Maximum Clique Problem. The results will be analyzed with respect to string rates, child improvement, pool rating resolution, and average string age. / Department of Computer Science

Thesis (M.S.)--Rochester Institute of Technology, 2009. / Typescript. Includes bibliographical references (leaves 46-47).

Thesis (M.S.)--Worcester Polytechnic Institute. / Title from title screen. Keywords: genetic programming; approximations; machine learning; artificial intelligence. Includes bibliographical references (p. 92-94).

Automatic text classification using current approaches is known to perform poorly when documents are noisy or when limited amounts of textual content is available. Yet, many users need access to such documents, which are found in large numbers in digital libraries and in the WWW. If documents are not classified, they are difficult to find when browsing. Further, searching precision suffers when categories cannot be checked, since many documents may be retrieved that would fail to meet category constraints. In this work, we study how different types of evidence from multiple sources can be intelligently fused to improve classification of text documents into predefined categories. We present a classification framework based on an inductive learning method -- Genetic Programming (GP) -- to fuse evidence from multiple sources. We show that good classification is possible with documents which are noisy or which have small amounts of text (e.g., short metadata records) -- if multiple sources of evidence are fused in an intelligent way. The framework is validated through experiments performed on documents in two testbeds. One is the ACM Digital Library (using a subset available in connection with CITIDEL, part of NSF's National Science Digital Library). The other is Web data, in particular that portion associated with the Cadê Web directory. Our studies have shown that improvement can be achieved relative to other machine learning approaches if genetic programming methods are combined with classifiers such as kNN. Extensive analysis was performed to study the results generated through the GP-based fusion approach and to understand key factors that promote good classification. / Ph. D.

Process Algebras are a Formal Modelling methodology which are an effective tool for defining models of complex systems, particularly those involving multiple interacting processes. However, describing such a model using Process Algebras requires expertise from both the modeller and the domain expert. Finding the correct model to describe a system can be difficult. Further more, even with the correct model, parameter tuning to allow model outputs to match experimental data can also be both difficult and time consuming. Evolutionary Algorithms provide effective methods for finding solutions to optimisation problems with large and noisy search spaces. Evolutionary Algorithms have been proven to be well suited to investigating parameter fitting problems in order to match known data or desired behaviour. It is proposed that Process Algebras and Evolutionary Algorithms have complementary strengths for developing models of complex systems. Evolutionary Algorithms require a precise and accurate fitness function to score and rank solutions. Process Algebras can be incorporated into the fitness function to provide this mathematical score. Presented in this work is the Evolving Process Algebra (EPA) framework, designed for the application of Evolutionary Algorithms (specifically Genetic Algorithms and Genetic Programming optimisation techniques) to models described in Process Algebra (specifically PEPA and Bio-PEPA) with the aim of evolving fitter models. The EPA framework is demonstrated using multiple complex systems. For PEPA this includes the dining philosophers resource allocation problem, the repressilator genetic circuit, the G-protein cellular signal regulators and two epidemiological problems: HIV and the measles virus. For Bio-PEPA the problems include a biochemical reactant-product system, a generic genetic network, a variant of the G-protein system and three epidemiological problems derived from the measles virus. Also presented is the EPA Utility Assistant program; a lightweight graphical user interface. This is designed to open the full functionality and parallelisation of the EPA framework to beginner or naive users. In addition, the assistant program aids in collating and graphing after experiments are completed.

Dans le cadre de cette thèse, nous nous intéresseons à l'amélioration des techniques de programmation génétique (PG), en particulier nous avons essayer d'améliorer la performance de la PG en cas d'utilisation de grammaire riche, où l'ensemble de terminaux contient plus que nécessaire pour représenter des solutions optimales. Pour cela, nous avons présenté le problème de la sélection d'attributs en rappelant les principales approches, et nous avons utilisé la technique de mesure de poids des terminaux pour affiner la sélection d'attributs. En second lieu, nous présentons des travaux sur un autre algorithme qui s'inspire de la boucle évolutionnaire : l'évolution différentielle (ED), et nous étudions la performance de cette technique sur la branche de la programmation génétique linéaire. Nous présentons et comparons les performances de cette dernière technique sur un ensemble de "benchmarks" classique de la PG. / This thesis mainly deals with genetic programming. In this work, we are interested in improving the overall performance of genetic programming (GP) when dealing with rich grammar when the terminal set is very large. We introduce the problem of attributes selection and in our work we introduce a scheme based on the weight (based on the frequency) to refine the attribute selection. In the second part of this work, we try to improve the evolution engine with the help of the differential evolution (DE) algorithm. This new engine is applied to linear genetic programming. We then present some experiments and make some comparisons on a set of classical benchmarks.