Multi-Objective Genetic Algorithms for the Single Allocation Hub Location Problem

Hub Location Problems play vital economic roles in transportation and telecommunication networks where goods or people must be efficiently transferred from an origin to a destination point whilst direct origin-destination links are impractical. This work investigates the single allocation hub location problem, and proposes a genetic algorithm (GA) approach for it. The effectiveness of using a single-objective criterion measure for the problem is first explored. Next, a multi-objective GA employing various fitness evaluation strategies such as Pareto ranking, sum of ranks, and weighted sum strategies is presented. The effectiveness of the multi-objective GA is shown by comparison with an Integer Programming strategy, the only other multi-objective approach found in the literature for this problem. Lastly, two new crossover operators are proposed and an empirical study is done using small to large problem instances of the Civil Aeronautics Board (CAB) and Australian Post (AP) data sets.

A stochastic dynamic programming approach for pricing options on stock-index futures

The aim of this thesis is to price options on equity index futures with an application to standard options on S&P 500 futures traded on the Chicago Mercantile Exchange. Our methodology is based on stochastic dynamic programming, which can accommodate European as well as American options. The model accommodates dividends from the underlying asset. It also captures the optimal exercise strategy and the fair value of the option. This approach is an alternative to available numerical pricing methods such as binomial trees, finite differences, and ad-hoc numerical approximation techniques. Our numerical and empirical investigations demonstrate convergence, robustness, and efficiency. We use this methodology to value exchange-listed options. The European option premiums thus obtained are compared to Black's closed-form formula. They are accurate to four digits. The American option premiums also have a similar level of accuracy compared to premiums obtained using finite differences and binomial trees with a large number of time steps. The proposed model accounts for deterministic, seasonally varying dividend yield. In pricing futures options, we discover that what matters is the sum of the dividend yields over the life of the futures contract and not their distribution.

Daily Physical Activity as an Intervention Strategy for Anxious Elementary Students

The focus of this project was twofold: a comprehensive examination of provincially mandated, school-based physical activity programming beyond physical education, as well as an exploration of the potential relationship between school-based physical activity and student anxiety. The data were collected using a descriptive research methodology consisting of a qualitative document analysis of provincial government publications pertaining to school-based physical activity programming and the literature on the relationship between physical activity and student anxiety. The findings revealed inconsistencies between the Canadian provinces and territories in providing mandated school-based physical activity beyond physical education. It was also revealed that regular school-based physical activity has the potential to make a positive impact on students’ lives in many ways. Students are living more sedentary lives, and evidence shows that regular physical activity could prevent and treat student anxiety.

Object-Oriented Genetic Programming for the Automatic Inference of Graph Models for Complex Networks

Complex networks are systems of entities that are interconnected through meaningful relationships. The result of the relations between entities forms a structure that has a statistical complexity that is not formed by random chance. In the study of complex networks, many graph models have been proposed to model the behaviours observed. However, constructing graph models manually is tedious and problematic. Many of the models proposed in the literature have been cited as having inaccuracies with respect to the complex networks they represent. However, recently, an approach that automates the inference of graph models was proposed by Bailey [10] The proposed methodology employs genetic programming (GP) to produce graph models that approximate various properties of an exemplary graph of a targeted complex network. However, there is a great deal already known about complex networks, in general, and often specific knowledge is held about the network being modelled. The knowledge, albeit incomplete, is important in constructing a graph model. However it is difficult to incorporate such knowledge using existing GP techniques. Thus, this thesis proposes a novel GP system which can incorporate incomplete expert knowledge that assists in the evolution of a graph model. Inspired by existing graph models, an abstract graph model was developed to serve as an embryo for inferring graph models of some complex networks. The GP system and abstract model were used to reproduce well-known graph models. The results indicated that the system was able to evolve models that produced networks that had structural similarities to the networks generated by the respective target models.

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.

Perceived effectiveness of alternative programming : a case study /

The topic of this research was alternative programming in secondary public education. The purpose of this research was to explore the perceived effectiveness of two public secondary programs that are aJternative to mainstream or "regular" education. Two case study sites were used to research diverse ends of the aJtemative programming continuum. The first case study demonstrated a gifted program and the second demonstrated a behavioral program. Student needs were examined in terms of academic needs, emotional needs, career needs, and social needs. Research conducted in these sites examined how the students, teachers, onsite staff, and program administrators perceived that individual needs were met and unmet in these two programs. The study was qualitative and exploratory, using deductive and inductive research techniques. Similar themes of best practice that were identified in the case study sites aided in the development of a teaching and learning model. Four themes were identified as important within the case study sites. These themes included the commitment and motivation of teachers and the support of administration in the gifted program, and the importance of location and the flow of information and communication in the behavior program. Six themes emerged that were similar across the case study sites. These themes included the individual nature of programming, recognition of student achievement, the alternative program as a place of safety and community, importance of interpersonal capacity, priority of basic needs, and, finally, matching student capacity with program expectations. The model incorporates these themes and is designed as a resource for teachers, program administrators, parents, and policy makers of alternative educational programs.

Implementing a question-generation strategy with bilingual students: exploring the perspectives of a beginning teacher /

This study examined the challenges associated with the explicit delivery of questiongeneration strategy with 8 Arab Canadian students from the perspective of a bilingual beginning teacher. This study took place in a private school and involved 2 stages consisting of 9 instructional sessions, and individual interviews with the students. Data gathered from these interviews and the researcher's field notes from the sessions were used to gain insights about the participants' understanding and use of explicit instruction. The themes that emerged from the data included "teacher attitude," "students' enhanced metacognitive awareness and strategy use," "listening skills," and "instructional challenges." Briefly, teacher's attitude demonstrated how teacher's beliefs and knowledge influenced her willingness and perseverance to teach explicitly. Students' enhanced metacognitive awareness and strategy use included students' understanding and use of the question-generation strategy. The students' listening skills suggested that culture may influence their response to the delivery of explicit instruction. Here, the cultural expectations associated with being a good listener reinforced students' willingness to engage in this strategy. Students' prior knowledge also influenced their interaction with the question-generation strategy. Time for process versus covering content was a dominant instructional challenge. This study provides first hand information for teachers when considering how students' cultural backgrounds may affect their reactions to explicit strategy instruction.

A school-based cognitive behavior modification intervention strategy for learned helpless mentally retarded students

This study assessed the usefulness of a cognitive behavior modification (CBM) intervention package with mentally retarded students in overcoming learned helplessness and improving learning strategies. It also examined the feasibility of instructing teachers in the use of such a training program for a classroom setting. A modified single subject design across individuals was employed using two groups of three subjects. Three students from each of two segregated schools for the mentally retarded were selected using a teacher questionnaire and pupil checklist of the most learned helpless students enrolled there. Three additional learned helplessness assessments were conducted on each subject before and after the intervention in order to evaluate the usefulness of the program in alleviating learned helplessness. A classroom environment was created with the three students from each school engaged in three twenty minute work sessions a week with the experimenter and a tutor experimenter (TE) as instructors. Baseline measurements were established on seven targeted behaviors for each subject: task-relevant speech, task-irrelevant speech, speech denoting a positive evaluation of performance, speech denoting a negative evaluation of performance, proportion of time on task, non-verbal positive evaluation of performance and non-verbal negative evaluation of performance. The intervention package combined a variety of CBM techniques such as Meichenbaum's (1977) Stop, Look and Listen approach, role rehearsal and feedback. During the intervention each subject met with his TE twice a week for an individual half-hour session and one joint twenty minute session with all three students, the experimentor and one TE. Five weeks after the end of this experiment one follow up probe was conducted. All baseline, post-intervention and probe sessions were videotaped. The seven targeted behaviors were coded and comparisons of baseline, post intervention, and probe testing were presented in graph form. Results showed a reduction in learned helplessness in all subjects. Improvement was noted in each of the seven targeted behaviors for each of the six subjects. This study indicated that mentally retarded children can be taught to reduce learned helplessness with the aid of a CBM intervention package. It also showed that CBM is a viable approach in helping mentally retarded students acquire more effective learning strategies. Because the TEs (Tutor experimenters) had no trouble learning and implementing this program, it was considered feasible for teachers to use similar methods in the classroom.

Explicit versus implicit spelling strategy instruction and its effects on grade one children's invented spellings

The intent of this study was to investigate .the effectiveness of teaching thirty-five Grade One children a variety of effective spelling strategies in comparison to tradit~onal spelling instruction. Strategy instruction included training in phonology, imagery and analogy. In addition, the type of instruction pro~ided (implicit versus explicit) was also examined. Children were seen in small groups of four or five, for four, twenty-five minute sessions. All children were tested prior and immediately following the training sessions, as well as at 14-day follow-ups. Pretest and posttest measures included a dictated spelling test (based on words used in training), a developmental spelling test and a sample of each child's writing. In addition, children completed a metacognitive spelling test as a measure of their strategy awareness. Performance scores on the pretest and posttest measures were compared to determine if any differences existed between the three spelling instruction groups using the Dunn-Bonferroni and Dunnett procedures. Findings revealed that explicit strategy instruction was the most effective spelling program for improving Grade One children's invented spellings. Children who received this instruction were able to spell targeted words more accurately, even after a 14-day follow-up, and were able to recall more effective spelling strategies than children who received either implicit strategy instruction or traditional strategy instruction.

Improving grade eight students' spelling performance with a triad strategy approach

Thesis (M.Ed.)-- Brock University, 1996.

Preadjunct questions as a learning strategy for older adults

This study investigated the effectiveness of comprehension level preadjunct questions as a learning strategy for older adults in a classroom setting. Fifty-five adults from 55 to 70 years of age were randomly assigned to two groups, the preadjunct question group and a no-question control group. They viewed a video on high blood pressure and completed a recall posttest immediately after viewing the video and again seven days tater. Results demonstrated that there was no significant difference between groups. However, the no-question control group obtained a higher mean score on both the immediate and delayed recall tests than did the preadjunct question group. Nevertheless, significant differences in posttest scores were found related to educational levels and prior knowledge about high blood pressure. Results obtained were explained in terms of resource theory of cognitive aging.

Spelling performance of third grade students : a comparison of whole-language-plus-strategy instruction, strategy instruction only, and the whole language approach

This study investigated three methods for teaching children how to spell. Third grade students were divided into three conditions for a one-week training period consisting of 15- to 20-minute lessons. One of the two experimental conditions used a whole language approach along with explicit strategy instruction. The second condition used strategy instruction within a traditional setting. The control used strictly a whole language approach to le~ing hO\\l to spell. The spelling perfonnance of all three conditions improved after the one-week training period. However, students in the strategy instruction groups did significantly better on the study "'7ords than the whole language only group. The students in whole-Ianguage-plusstrateg)! instruction outperformed both other groups. Significantly better spelling perfonnance was observed even at the nine-week posttest. This study frrst supported the hypothesis that children can make significantly greater improven1ents in their spelling when explicitly taught how to use spelling strategies. Secondl)', this study indicated that whole language provided a relevant context for the study words, clearly giving the students in the whole-Ianguage-plus-strategy condition an additional advantage.

The effectiveness of explicit strategy instruction on the top level structure of a text

Thesis (M.Ed.)-- Brock University, 1995.

The effects of teaching a mathematics problem solving strategy on the problem solving ability of grade nine students

Forty grade 9 students were selected from a small rural board in southern Ontario. The students were in two classes and were treated as two groups. The treatment group received instruction in the Logical Numerical Problem Solving Strategy every day for 37 minutes over a 6 week period. The control group received instruction in problem solving without this strategy over the same time period. Then the control group received the treat~ent and the treatment group received the instruction without the strategy. Quite a large variance was found in the problem solving ability of students in grade 9. It was also found that the growth of the problem solving ability achievement of students could be measured using growth strands based upon the results of the pilot study. The analysis of the results of the study using t-tests and a MANOVA demonstrated that the teaching of the strategy did not significaritly (at p s 0.05) increase the problem solving achievement of the students. However, there was an encouraging trend seen in the data.