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This research work investigates the theoretical foundations and computational aspects of constructing optimal bespoke CDO structures. Due to the evolutionary nature of the CDO design process, stochastic search methods that
mimic the metaphor of natural biological evolution are applied. For efficient searching the optimal solution, the nondominating
sort genetic algorithm (NSGA-II) is used, which places emphasis on moving towards the true Paretooptimal
region. This is an essential part of real-world credit structuring problems. The algorithm further demonstrates
attractive constraint handling features among others, which is suitable for successfully solving the constrained portfolio
optimisation problem. Numerical analysis is conducted on a bespoke CDO collateral portfolio constructed from
constituents of the iTraxx Europe IG S5 CDS index. For comparative purposes, the default dependence structure is
modelled via Gaussian and Clayton copula assumptions. This research concludes that CDO tranche returns at all
levels of risk under the Clayton copula assumption performed better than the sub-optimal Gaussian assumption. It is
evident that our research has provided meaningful guidance to CDO traders, for seeking significant improvement of
returns over standardised CDOs tranches of similar rating.

n this age of molecular biology, The healthcare industry, politicians and the community at large are trying to find 'magic bullet' drugs and vaccines to conquer disease. Although smallpox has been eradicated and polio may soon be a scourge of the past, many pathogens replicate rapidly and mutate prodigiously, enabling them to evolve ways to circumvent our immune systems, as well as our drugs and vaccines. To fight and win the war against new emerging infections such as HIV/AIDS, TB and now SARS (severe acute respiratory syndrome), it is important to understand the temporal and spatial dynamics of the pathogens in human and, in some cases, animal reservoirs or vector populations. It is also necessary to understand the complex web of socio-economic factors pertinent to controlling the spread of disease, so that feasible, affordable and, most importantly, effective public-health policies can be devised and implemented.