Long-Term Evolution Experiment with Genetic Programming

We evolve floating point Sextic polynomial populations of genetic programming binary trees for up to a million generations. We observe continued innovation but this is limited by tree depth. We suggest that deep expressions are resilient to learning as they disperse information, impeding evolvability, and the adaptation of highly nested organisms, and we argue instead for open complexity. Programs with more than 2,000,000,000 instructions (depth 20,000) are created by crossover. To support unbounded long-term evolution experiments in genetic programming (GP), we use incremental fitness evaluation and both SIMD parallel AVX 512-bit instructions and 16 threads to yield performance equivalent to 1.1 trillion GP operations per second, 1.1 tera GPops, on an Intel Xeon Gold 6136 CPU 3.00GHz server

Long-term evolution experiment with genetic programming [hot of the press]

We evolve floating point Sextic polynomial populations of genetic programming binary trees for up to a million generations. We observe continued innovation but this is limited by their depth and suggest deep expressions are resilient to learning as they disperse information, impeding evolvability and the adaptation of highly nested organisms and instead we argue for open complexity. Programs with more than 2 000 000 000 instructions (depth 20 000) are created by crossover. To support unbounded long-term evolution experiments LTEE in GP we use incremental fitness evaluation and both SIMD parallel AVX 512 bit instructions and 16 threads to yield performance equivalent of up to 1.1 trillion GP operations per second, 1.1 tera-GPops, on an Intel Xeon Gold 6136 CPU 3.00GHz server

Genetisches Programmieren als neues Instrumentarium zur Prognose makroökonomischer Größen : Anwendungen auf Inflationsraten und Wechselkurse

Die vorliegende Dissertation untersucht, ob sich das GenetischeProgrammieren als ein neues Instrument zur Vorhersage monetärer Größen eignet. Hierzu wurde auf der Grundlage des Matrizenrechenprogramms GAUSS ein entsprechendes Computerprogramm entwickelt. Das mit GP GAUSS bezeichnete Programm wird zunächst auf nichtlineare dynamische Prozesse angewendet und mit den Ergebnissen anderer Genetischer Programme verglichen. Im Anschluß an eine Sensitivitätsanalyse wird GP GAUSS auf die Prognose von Inflations- und Wechselkursraten angewendet, wobei für die Inflationsprognosen der Mishkin Ansatz und für die Wechselkursprognosen monetaristische und portfoliobasierte Wechselkursmodelle zugrundeliegen

Genetisches Programmieren als neues Instrumentarium zur Prognose makroökonomischer Größen : Anwendungen auf Inflationsraten und Wechselkurse

Die vorliegende Dissertation untersucht, ob sich das GenetischeProgrammieren als ein neues Instrument zur Vorhersage monetärer Größen eignet. Hierzu wurde auf der Grundlage des Matrizenrechenprogramms GAUSS ein entsprechendes Computerprogramm entwickelt. Das mit GP GAUSS bezeichnete Programm wird zunächst auf nichtlineare dynamische Prozesse angewendet und mit den Ergebnissen anderer Genetischer Programme verglichen. Im Anschluß an eine Sensitivitätsanalyse wird GP GAUSS auf die Prognose von Inflations- und Wechselkursraten angewendet, wobei für die Inflationsprognosen der Mishkin Ansatz und für die Wechselkursprognosen monetaristische und portfoliobasierte Wechselkursmodelle zugrundeliegen

Pattern recognition systems design on parallel GPU architectures for breast lesions characterisation employing multimodality images

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University London.The aim of this research was to address the computational complexity in designing multimodality Computer-Aided Diagnosis (CAD) systems for characterising breast lesions, by harnessing the general purpose computational potential of consumer-level Graphics Processing Units (GPUs) through parallel programming methods. The complexity in designing such systems lies on the increased dimensionality of the problem, due to the multiple imaging modalities involved, on the inherent complexity of optimal design methods for securing high precision, and on assessing the performance of the design prior to deployment in a clinical environment, employing unbiased system evaluation methods. For the purposes of this research, a Pattern Recognition (PR)-system was designed to provide highest possible precision by programming in parallel the multiprocessors of the NVIDIA’s GPU-cards, GeForce 8800GT or 580GTX, and using the CUDA programming framework and C++. The PR-system was built around the Probabilistic Neural Network classifier and its performance was evaluated by a re-substitution method, for estimating the system’s highest accuracy, and by the external cross validation method, for assessing the PR-system’s unbiased accuracy to new, “unseen” by the system, data. Data comprised images of patients with histologically verified (benign or malignant) breast lesions, who underwent both ultrasound (US) and digital mammography (DM). Lesions were outlined on the images by an experienced radiologist, and textural features were calculated. Regarding breast lesion classification, the accuracies for discriminating malignant from benign lesions were, 85.5% using US-features alone, 82.3% employing DM-features alone, and 93.5% combining US and DM features. Mean accuracy to new “unseen” data for the combined US and DM features was 81%. Those classification accuracies were about 10% higher than accuracies achieved on a single CPU, using sequential programming methods, and 150-fold faster. In addition, benign lesions were found smoother, more homogeneous, and containing larger structures. Additionally, the PR-system design was adapted for tackling other medical problems, as a proof of its generalisation. These included classification of rare brain tumours, (achieving 78.6% for overall accuracy (OA) and 73.8% for estimated generalisation accuracy (GA), and accelerating system design 267 times), discrimination of patients with micro-ischemic and multiple sclerosis lesions (90.2% OA and 80% GA with 32-fold design acceleration), classification of normal and pathological knee cartilages (93.2% OA and 89% GA with 257-fold design acceleration), and separation of low from high grade laryngeal cancer cases (93.2% OA and 89% GA, with 130-fold design acceleration). The proposed PR-system improves breast-lesion discrimination accuracy, it may be redesigned on site when new verified data are incorporated in its depository, and it may serve as a second opinion tool in a clinical environment