Evolving pattern recognition systems

Rizki, Mateen M.; Zmuda, Michael A.; Tamburino, Louis A.

doi:10.1109/tevc.2002.806167

Cited by 60 publications

(26 citation statements)

References 17 publications

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“…Former research on such systems is rather limited (Teller and Veloso, 1997;Rizki et al, 2002;Howard et al, 2006;Tackett, 1993). Bhanu et al (2005) as well as Krawiec and Bhanu (2005) proposed a methodology that evolved feature extraction procedures encoded either as tree-like or linear genetic programming individuals.…”

Section: Motivations and Related Workmentioning

confidence: 99%

Cross-task code reuse in genetic programming applied to visual learning

Jaśkowski¹,

Krawiec²,

Wieloch³

2014

International Journal of Applied Mathematics and Computer Science

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We propose a method that enables effective code reuse between evolutionary runs that solve a set of related visual learning tasks. We start with introducing a visual learning approach that uses genetic programming individuals to recognize objects. The process of recognition is generative, i.e., requires the learner to restore the shape of the processed object. This method is extended with a code reuse mechanism by introducing a crossbreeding operator that allows importing the genetic material from other evolutionary runs. In the experimental part, we compare the performance of the extended approach to the basic method on a real-world task of handwritten character recognition, and conclude that code reuse leads to better results in terms of fitness and recognition accuracy. Detailed analysis of the crossbred genetic material shows also that code reuse is most profitable when the recognized objects exhibit visual similarity.

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Section: Motivations and Related Workmentioning

confidence: 99%

Cross-task code reuse in genetic programming applied to visual learning

Jaśkowski¹,

Krawiec²,

Wieloch³

2014

International Journal of Applied Mathematics and Computer Science

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“…These are perhaps the most ambitious research projects. Two efforts are worthy of special mention: HELPR [14] and CellNet [1], [15]. HELPR evolves feature detectors, but the classification module is completely separate from the feature extraction module.…”

Section: B Feature Creationmentioning

confidence: 99%

Evolving novel image features using Genetic Programming-based image transforms

Kowaliw

Banzhaf

Kharma

et al. 2009

2009 IEEE Congress on Evolutionary Computation

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Abstract-In this paper, we use Genetic Programming (GP) to define a set of transforms on the space of greyscale images. The motivation is to allow an evolutionary algorithm means of transforming a set of image patterns into a more classifiable form. To this end, we introduce the notion of a Transformbased Evolvable Feature (TEF), a moment value extracted from a GP-transformed image, used in a classification task. Unlike many previous approaches, the TEF allows the whole image space to be searched and augmented. TEFs are instantiated through Cartesian Genetic Programming, and applied to a medical image classification task, that of detecting muscular dystrophy-indicating inclusions in cell images. It is shown that the inclusion of a single TEF allows for significantly superior classification relative to predefined features alone.

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“…Our earlier work as shown that additional discriminating capability can be obtained by allowing multiple operations to be applied sequentially and/or combined using set operations [48,60]. By allowing arbitrary combinations, the flexibility of the representation is [36] can be used to search for useful expressions.…”

Section: Morphological Sequencesmentioning

confidence: 99%

HELPR: Hybrid Evolutionary Learning for Pattern Recognition

Rizki¹,

Tamburino²

2005

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Evolving pattern recognition systems

Cited by 60 publications

References 17 publications

Cross-task code reuse in genetic programming applied to visual learning

Cross-task code reuse in genetic programming applied to visual learning

Evolving novel image features using Genetic Programming-based image transforms

HELPR: Hybrid Evolutionary Learning for Pattern Recognition

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