Postcolonial entanglements of Power, Race, Class and Privilege in Child and Youth Care Work Within a Brazilian Favela

This research assesses the various aspects of Child and Youth Care (CYC) work and how relationships between child or youth and care provider are limited and constricted within greater political, social and historical contexts. Specifically, this research takes place internationally in Rio de Janeiro, Brazil within a favela (slum) and unveils the entangled and complex relationship that I, not only as an ethnographer, but also as a CYC worker had with the many young people that I encountered. It will address a variety of theories that demonstrate the potentials of reproducing oppressive relationships, and argue that it is imperative for CYC workers to critically reflect on the greater contexts in which their work is situated in order to gain forces with those young people whom they are attempting to serve.

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.