Sedimentology and micromorphology of drumlin sediments at Chimney Bluffs State Park, New York, United States of America

The drumlin sediments at Chimney Bluffs, New York appear to represent a block-inmatrix style glacial melange. This melange comprises sand stringers, lenses and intraclasts juxtaposed in an apparently massive diamicton. Thin section examination of these glacigenic deposits has revealed microstructures indicative of autokinetic subglacial defonnation which are consistent with a deformable bed origin for the diamicton. These features include banding and. necking of matrix grains, oriented plasma fabrics and the formation of pressure shadows at the long axis ends of elongate clasts. Preservation of primary stratification within the sand intraclasts appears to suggest that these features were pre-existing up-ice deposits that were frozen, entrained, then deposited as part of a defonning till layer beneath an advancing ice sheet. Multi-directional micro-shearing within the sand blocks is thought to reflect the frozen nature of the sand units in such a high strain environment. It is also contended that dewatering of the sediment pile leading to the eventual immobilisation of the defonning till layer was responsible for opening sub-horizontal fissures within the diamicton. These features were subsequently infilled with mass flow poorly sorted sands and silts which were subjected to ductile defonnation during the waning stages of an actively deforming till layer. Microstructures indicative of the dewatering processes in the sand units include patches of fine-grained particles within a coarser-grained matrix and the presence of concentrated zones of translocated clays. However, these units were probably confined within an impermeable diamicton casing that prevented massive pore water influxes from the deforming till layer~ Hence, these microstructures probably reflect localised dewatering of the sand intraclasts. A layered subglacial shear zone model is proposed for the various features exhibited by the drumlin sediments. The complexity of these structures is explained in terms of ii superposing deformation styles in response to changing pore water pressures. Constructional glaciotectonics, as implied by the occurrence of sub-horizontal fissuring, is suggested as the mechanism for the stacking of the sand intraclast units within the diamicton. The usefulness of micromorphology in complimenting the traditional sedimentology of glacigenic deposits is emphasised by the current study. An otherwise massive diamicton was shown to contain microstructures indicative of the very high strain rates expected in a complexly deforming till layer. . It is quite obvious from this investigation that the classification of diamictons needs to be re-examined for evidence of microstructures that could lead to the re-interpretation of diamicton forming processes. RESUME Le pacquet de sediments drumlinaire de Chimney Bluffs, New York, represent un "bloc-en-matrice" genre de melange glaciale. Des structures microscopique comprennent l'evidence pour la defonnation intrinseque attribuee a l'origine lit non resistant du drumlin. PreselVation des structures primaires au coeur des blocs arenaces suggere que ceux sont des depots preexistant qui furent geles, entraines et par la suite sedimentes au milieu d'une couche de debris sous-glaciaires en voie de deformation. Des failles microscopiques a l'interieur des blocs arenaces appuient aussi l'idee d'un bloc cohesif (c'est-a-dire gele) au centre d'un till non resistant. Des implications significatives s'emergent de cette etude pour les conditions sous-glaciaire et les processus de la formation des drumlin.

A Study of Ordered Gene Problems Featuring DNA Error Correction and DNA Fragment Assembly with a Variety of Heuristics, Genetic Algorithm Variations, and Dynamic Representations

Ordered gene problems are a very common classification of optimization problems. Because of their popularity countless algorithms have been developed in an attempt to find high quality solutions to the problems. It is also common to see many different types of problems reduced to ordered gene style problems as there are many popular heuristics and metaheuristics for them due to their popularity. Multiple ordered gene problems are studied, namely, the travelling salesman problem, bin packing problem, and graph colouring problem. In addition, two bioinformatics problems not traditionally seen as ordered gene problems are studied: DNA error correction and DNA fragment assembly. These problems are studied with multiple variations and combinations of heuristics and metaheuristics with two distinct types or representations. The majority of the algorithms are built around the Recentering- Restarting Genetic Algorithm. The algorithm variations were successful on all problems studied, and particularly for the two bioinformatics problems. For DNA Error Correction multiple cases were found with 100% of the codes being corrected. The algorithm variations were also able to beat all other state-of-the-art DNA Fragment Assemblers on 13 out of 16 benchmark problem instances.

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.