Learning deterministic finite automata with a smart state labeling evolutionary algorithm

Lucas, Simon M.; Reynolds, T.J.

doi:10.1109/tpami.2005.143

Cited by 59 publications

(48 citation statements)

References 14 publications

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“…The system, Avida-MDE, generates a set of communicating state-based models of system behavior using model inference techniques that allow a finite state machine model to be synthesized from cases. A related approach was used by Lucas and Reynolds [2005], who presented an EA for learning deterministic finite automaton to optimally assign state labels and compare its performance with the evidence driven state merging algorithm.…”

Section: Design Tools and Techniquesmentioning

confidence: 99%

Search-based software engineering

2012

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In the past five years there has been a dramatic increase in work on Search Based Software Engineering (SBSE), an approach to Software Engineering (SE) in which Search Based Optimization (SBO) algorithms are used to address problems in SE. SBSE has been applied to problems throughout the SE life cycle, from requirements and project planning to maintenance and reengineering. The approach is attractive because it offers a suite of adaptive automated and semi-automated solutions in situations typified by large complex problem spaces with multiple competing and conflicting objectives.This paper 1 provides a review and classification of literature on SBSE. The paper identifies research trends and relationships between the techniques applied and the applications to which they have been applied and highlights gaps in the literature and avenues for further research.

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Section: Design Tools and Techniquesmentioning

confidence: 99%

Search-based software engineering

2012

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“…This approach is referred to as balanced sampling in this paper. Studies [11] have shown that balanced sampling can help improve the classification performance on certain unbalanced data sets, and it has been used in many applications [19] [18]. Though it is a widely accepted method, there is no hard evidence that balanced sampling is the optimal approach.…”

Section: Balanced Samplingmentioning

confidence: 99%

Informative sampling for large unbalanced data sets

Rughani

Tranmer

et al. 2008

Proceedings of the 10th Annual Conference Companion on Genetic and Evolutionary Computation

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Selective sampling is a form of active learning which can reduce the cost of training by only drawing informative data points into the training set. This selected training set is expected to contain more information for modeling compared to random sampling, thus making modeling faster and more accurate. We introduce a novel approach to selective sampling, which is derived from the Estimation-Exploration Algorithm (EEA). The EEA is a coevolutionary algorithm that uses model disagreement to determine the significance of a training datum, and evolves a set of models only on the selected data. The algorithm in this paper trains a population of Artificial Neural Networks (ANN) on the training set, and uses their disagreement to seek new data for the training set. A medical data set called the National Trauma Data Bank (NTDB) is used to test the algorithm. Experiments show that the algorithm outperforms the equivalent algorithm using randomly-selected data and sampling evenly from each class. Finally, the selected training data reveals which features most affect outcome, allowing for both improved modeling and understanding of the processes that gave rise to the data.

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“…In fact, our approach might be modelled as a single design point amongst those that were analyzed by hyper-heuristic evolutionary search in the design space of rule induction for classification [11]. While such a point of view may be useful, it is important to remark that text classification and text extraction are quite different problems: the former may allow partitioning input units in two classes, depending on whether they contain relevant slices (e.g., [12][13][14]); the latter also requires identifying the boundaries of the slice-or slices-to be extracted.…”

Section: Introductionmentioning

confidence: 99%

Learning Text Patterns Using Separate-and-Conquer Genetic Programming

Bartoli

Lorenzo

Medvet

et al. 2015

Lecture Notes in Computer Science

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Abstract. The problem of extracting knowledge from large volumes of unstructured textual information has become increasingly important. We consider the problem of extracting text slices that adhere to a syntactic pattern and propose an approach capable of generating the desired pattern automatically, from a few annotated examples. Our approach is based on Genetic Programming and generates extraction patterns in the form of regular expressions that may be input to existing engines without any post-processing. Key feature of our proposal is its ability of discovering automatically whether the extraction task may be solved by a single pattern, or rather a set of multiple patterns is required. We obtain this property by means of a separate-and-conquer strategy: once a candidate pattern provides adequate performance on a subset of the examples, the pattern is inserted into the set of final solutions and the evolutionary search continues on a smaller set of examples including only those not yet solved adequately. Our proposal outperforms an earlier state-of-theart approach on three challenging datasets.

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Learning deterministic finite automata with a smart state labeling evolutionary algorithm

Cited by 59 publications

References 14 publications

Search-based software engineering

Search-based software engineering

Informative sampling for large unbalanced data sets

Learning Text Patterns Using Separate-and-Conquer Genetic Programming

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