2 resultados para Benchmarks

em Brock University, Canada


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Population-based metaheuristics, such as particle swarm optimization (PSO), have been employed to solve many real-world optimization problems. Although it is of- ten sufficient to find a single solution to these problems, there does exist those cases where identifying multiple, diverse solutions can be beneficial or even required. Some of these problems are further complicated by a change in their objective function over time. This type of optimization is referred to as dynamic, multi-modal optimization. Algorithms which exploit multiple optima in a search space are identified as niching algorithms. Although numerous dynamic, niching algorithms have been developed, their performance is often measured solely on their ability to find a single, global optimum. Furthermore, the comparisons often use synthetic benchmarks whose landscape characteristics are generally limited and unknown. This thesis provides a landscape analysis of the dynamic benchmark functions commonly developed for multi-modal optimization. The benchmark analysis results reveal that the mechanisms responsible for dynamism in the current dynamic bench- marks do not significantly affect landscape features, thus suggesting a lack of representation for problems whose landscape features vary over time. This analysis is used in a comparison of current niching algorithms to identify the effects that specific landscape features have on niching performance. Two performance metrics are proposed to measure both the scalability and accuracy of the niching algorithms. The algorithm comparison results demonstrate the algorithms best suited for a variety of dynamic environments. This comparison also examines each of the algorithms in terms of their niching behaviours and analyzing the range and trade-off between scalability and accuracy when tuning the algorithms respective parameters. These results contribute to the understanding of current niching techniques as well as the problem features that ultimately dictate their success.

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This study investigates instructors’ perceptions of reading instruction and difficulties among Language Instruction for Newcomers to Canada (LINC) Level 1-3 learners. Statistics Canada reports that 60% of immigrants possess inadequate literacy skills. Newcomers are placed in classes using the Canadian Language Benchmarks but large, mixed-level classes create little opportunity for individualized instruction, leading some clients to demonstrate little change in their reading benchmarks. Data were collected (via demographic questionnaires, semi-structured interviews, teaching plans, and field study notes) to create a case study of five LINC instructors’ perceptions of why some clients do not progress through the LINC reading levels as expected and how their previous experiences relate to those within the LINC program. Qualitative analyses of the data revealed three primary themes: client/instructor background and classroom needs, reading, strategies, methods and challenges, and assessment expectations and progress, each containing a number of subthemes. A comparison between the themes and literature demonstrated six areas for discussion: (a) some clients, specifically refugees, require more time to progress to higher benchmarks; (b) clients’ level of prior education can be indicative of their literacy skills; (c) clients with literacy needs should be separated and placed into literacy-specific classes; (d) evidence-based approaches to reading instruction were not always evident in participants’ responses, demonstrating a lack of knowledge about these approaches; (e) first language literacy influences second language reading acquisition through a transfer of skills; and (f) collaboration in the classroom supports learning by extending clients’ capabilities. These points form the basis of recommendations about how reading instruction might be improved for such clients.