FIFTHTM: A Stack Based GP Language for Vector Processing

Holladay, Kenneth L.; Robbins, Kay A.; Ronne, Jeffery von

doi:10.1007/978-3-540-71605-1_10

Cited by 10 publications

(5 citation statements)

References 3 publications

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“…We recently introduced a new GP language called FIFTH [3] that addresses these deficiencies. Patterned after FORTH, FIFTH is a stack-based language in which a single stack container can hold a scalar, vector or matrix.…”

Section: The Fifth Gp Languagementioning

confidence: 98%

Evolution of Signal Processing Algorithms using Vector Based Genetic Programming

Holladay

Robbins

2007

2007 15th International Conference on Digital Signal Processing

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This paper demonstrates that FIFTH™, a new vector-based genetic programming (GP) language, can automatically derive very effective signal processing algorithms directly from signal data. Using symbol rate estimation as an example, we compare the performance of a standard algorithm against an evolved algorithm. The evolved algorithm uses a novel approach in developing a symbol transition feature vector and achieves an impressive 97.7% overall accuracy in the defined problem domain, far exceeding the performance of the standard algorithm. These results suggest that vector based GP approaches could be useful in developing more expressive features for a large class of signal processing and classification problems.

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Section: The Fifth Gp Languagementioning

confidence: 98%

Evolution of Signal Processing Algorithms using Vector Based Genetic Programming

Holladay

Robbins

2007

2007 15th International Conference on Digital Signal Processing

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“…The Zeus system evolves feature vectors as a forest of strongly-typed trees with each tree producing a single feature [19], [20]. Two systems, genetic programming environment for FIFTH (GPE5) [21], [22] and parallel algorithm discovery and orchestration (PADO) [23], [24], circumvent the need for a strongly-typed system by using a single data stack and functions designed to handle all possible input data types. Table I compares the solution structure for these methods and discusses how each structure addresses the four pattern recognition system design components described in section II.…”

Section: B Gp For Feature Designmentioning

confidence: 99%

Automated Feature Design for Numeric Sequence Classification by Genetic Programming

Harvey

Todd

2015

IEEE Trans. Evol. Computat.

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Abstract-Pattern recognition methods rely on maximuminformation, minimum-dimension feature sets to reliably perform classification and regression tasks. Many methods exist to reduce feature set dimensionality and construct improved features from an initial set; however, there are few general approaches for design of features from numeric sequences. Any information lost in pre-processing or feature measurement cannot be recreated during pattern recognition. General approaches are needed to extend pattern recognition to include feature design and selection for numeric sequences, such as time series, within the learning process itself. This study proposes a novel genetic programming approach to automated feature design called Autofead. In this method, a genetic programming variant evolves a population of candidate features built from a library of sequence-handling functions. Numerical optimization methods, included through a hybrid approach, ensure that the fitness of candidate algorithms is measured using optimal parameter values. Autofead represents the first automated feature design system for numeric sequences to leverage the power and efficiency of both numerical optimization and standard pattern recognition algorithms. Potential applications include the monitoring of electrocardiogram signals for indications of heart failure, network traffic analysis for intrusion detection systems, vibration measurement for bearing condition determination in rotating machinery, and credit card activity for fraud detection.

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“…In Langdon and Banzhaf (2008), Robilliard et al (2009), the authors directly use an RPN representation in the evolutionary algorithm. Other models of genetic programming algorithms use a flat representation directly, such as PushGP, FIFTH and Linear GP (Spector and Robinson 2002;Holladay et al 2007;Brameier and Banzhaf 2007).…”

Section: Easea Implementation Of Gpmentioning

confidence: 99%

EASEA: specification and execution of evolutionary algorithms on GPGPU

Maitre¹,

Krüger²,

Querry³

et al. 2011

Soft Comput

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EASEA is a framework designed to help nonexpert programmers to optimize their problems by evolutionary computation. It allows to generate code targeted for standard CPU architectures, GPGPU-equipped machines as well as distributed memory clusters. In this paper, EASEA is presented by its underlying algorithms and by some example problems. Achievable speedups are also shown onto different NVIDIA GPGPUs cards for different optimization algorithm families.

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FIFTHTM: A Stack Based GP Language for Vector Processing

Cited by 10 publications

References 3 publications

Evolution of Signal Processing Algorithms using Vector Based Genetic Programming

Evolution of Signal Processing Algorithms using Vector Based Genetic Programming

Automated Feature Design for Numeric Sequence Classification by Genetic Programming

EASEA: specification and execution of evolutionary algorithms on GPGPU

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