Ribbed moraines and subglacial geomorphological signatures of interior-sector palaeo-ice sheet dynamics

Transverse, subglacial bedforms (ribbed moraines) occur frequently in southern Keewatin, Nunavut, Canada, where they record a complex glacial history, including shifting centers of ice dispersal and fluctuating basal thermal regimes. Comprehensive mapping and quantitative morphometric analysis of the subglacial bedform archive in this sector reveals that ribbed moraines are spatially clustered by size and assume a broad range of visually distinct forms. Results suggest that end-member morphologies are consistent with a dichotomous polygenetic origin, and that a continuum of forms emerged through subsequent reshaping processes of variable intensity and duration. Translocation of mobile, immobile and quasi-mobile beds throughout the last glacial cycle conditioned the development of a subglacial deforming bed mosaic, and is likely responsible for the patchy zonation of palimpsest and inherited landscape signatures within this former core region of the Laurentide Ice Sheet. Comparison against field evidence collected from central Norway suggests that bedforming processes can be locally mediated by pre-existing topography.

Photograph - House Interior, Staircase and Landing

Black and white photographs, 19 cm x 24 cm of the interior of an unidentified house showing the staircase and landing, looking down a hallway to an exterior door. The photograph was taken by Wurts Brothers General Photographers of New York City (2 copies).

Photograph - House Interior, Dining Room

Black and white photographs, 19 cm x 24 cm of the interior of an unidentified house showing a dining room with a model ship on the table. The photograph was taken by Wurts Brothers General Photographers of New York City (2 copies).

Photograph - House Interior, Sitting Room

Black and white photographs, 19 cm x 24 cm of the interior of an unidentified house showing a sitting room. The photograph was taken by Wurts Brothers General Photographers of New York City (2 copies).

Photograph - House Interior, Sitting Room (Angle Two)

Black and white photographs, 19 cm x 24 cm of the interior of an unidentified house the sitting room which was mentioned above, but this shot is taken from farther away. A fireplace is visible in the room. The photograph was taken by Wurts Brothers General Photographers of New York City (2 copies).

Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub–optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and deterministic search algorithms. On the other hand, population based stochastic algorithms often suffer from premature convergence on mediocre sub–optimal solutions. The Age Layered Population Structure (ALPS) is a novel metaheuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. The ALPS paradigm uses an age–measure to control breeding and competition between individuals in the population. This thesis uses a modification of the ALPS GP strategy called Feature Selection ALPS (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal–symbol selection for the construction of GP trees/sub-trees. The FSALPS metaheuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non–converging evolutionary process that favors selection of features with high discrimination of class labels. We investigated and compared the performance of canonical GP, ALPS and FSALPS on high–dimensional benchmark classification datasets, including a hyperspectral image. Using Tukey’s HSD ANOVA test at a 95% confidence interval, ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.

Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub–optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and determinis- tic search algorithms. On the other hand, population based stochastic algorithms often suffer from premature convergence on mediocre sub–optimal solutions. The Age Layered Population Structure (ALPS) is a novel meta–heuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. The ALPS paradigm uses an age–measure to control breeding and competition between individuals in the population. This thesis uses a modification of the ALPS GP strategy called Feature Selection ALPS (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal–symbol selection for the construction of GP trees/sub-trees. The FSALPS meta–heuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non–converging evolutionary process that favors selection of features with high discrimination of class labels. We investigated and compared the performance of canonical GP, ALPS and FSALPS on high–dimensional benchmark classification datasets, including a hyperspectral image. Using Tukey’s HSD ANOVA test at a 95% confidence interval, ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.