'Art is the New Steel': Marketing Creative Urbanism in Twenty-First Century Hamilton, Ontario

As a major manufacturing hub in southern Ontario, Hamilton enjoyed considerable economic stability during the twentieth century. However, like most industrial-based cities, Hamilton’s role as a North American manufacturing producer has faded since the 1970’s. This has resulted in dramatic socio-economic impacts, most of which are centered on the inner city. There have been many attempts to revive the core. This includes Hamilton’s most recent urban renewal plans, based upon the principles of Richard Florida’s creative city hypothesis and Ontario’s Places to Grow Act (2005). Common throughout all of Hamilton’s urban renewal initiatives has been the role of the local press. In this thesis I conduct a discourse analysis of media based knowledge production. I show that the local press reproduces creative city discourses as local truths to substantiate and validate a revanchist political agenda. By choosing to celebrate the creative class culture, the local press fails to question its repercussions

A Scalability Study and New Algorithms for Large-Scale Many-Objective Optimization

Many real-world optimization problems contain multiple (often conflicting) goals to be optimized concurrently, commonly referred to as multi-objective problems (MOPs). Over the past few decades, a plethora of multi-objective algorithms have been proposed, often tested on MOPs possessing two or three objectives. Unfortunately, when tasked with solving MOPs with four or more objectives, referred to as many-objective problems (MaOPs), a large majority of optimizers experience significant performance degradation. The downfall of these optimizers is that simultaneously maintaining a well-spread set of solutions along with appropriate selection pressure to converge becomes difficult as the number of objectives increase. This difficulty is further compounded for large-scale MaOPs, i.e., MaOPs possessing large amounts of decision variables. In this thesis, we explore the challenges of many-objective optimization and propose three new promising algorithms designed to efficiently solve MaOPs. Experimental results demonstrate the proposed optimizers to perform very well, often outperforming state-of-the-art many-objective algorithms.