Our goal for this book is to examine the contemporary therapy of rheumatoid arthritis (RA) from the increasingly important perspective of impact upon quality of life, costs and long-term health outcomes. For too long the focus has been on short term, symptomatic, and surrogate indicator outcomes. Yet RA is a life-long disor der with the majority of impact on an individual patient many years following onset. Further, even in the short-term, researchers and rheumatologists have tended to emphasize measurements of disease activity such as joint counts, ESR and physi cian's opinion as to the amount of disease activity present. It is only relatively recently that measures of structural damage, quality of life and impact on broad domains of health have been given increasing emphasis. Also, the significance of early treatment of RA in order to optimise long-term outcomes has a relatively short history [1]. We have been focussed on the disease processes as surrogates for long term outcomes. Until the short-term process measures are validated as surrogates of long-term effects we should also turn our attention to outcomes of disease and the impact of our management on those outcomes [2). Inour view, this book is especially timely. We are at the dawn of a revolution in the management of RA and other complex immunological inflammatory disorders because their molecular, genetic and environmental mechanisms are being unrav elled. Inthe process, we are revealing a substantial number of novel and significant targets for pharmacotherapy.

This dissertation presents principles, techniques, and performance of evolutionary computation optimization methods. Evolutionary computation concepts examined are algorithm convergence, population diversity and sizing, genotype and phenotype partitioning, archiving, BB concepts, parallel evolutionary algorithm (EA) models, robustness, visualization of evolutionary process, and performance in terms of e ectiveness and e ciency. Additional contributions include the extension of explicit BB de nitions to clarify the meanings for good single and multiobjective BBs and a new visualization technique is developed for viewing genotype, phenotype, and the evolutionary process in nding Pareto front vectors. The culmination of this research is explicit BB state-of-the-art MOEA technology based on the MOEA design, BB classi er type assessment, solution evolution visualization, and insight into MOEA test metric validation and usage as applied to the following: test suite, deception, bioinformatics, unmanned vehicle -ight pattern, and digital symbol set design MOPs.

Interest in discovering a methodology for solving the Protein Structure Prediction (PSP) problem extends into many fields of study including biochemistry, medicine, biology, and numerous engineering and science disciplines. Experimental approaches, such as, x-ray crystallographic studies or solution Nuclear Magnetic Resonance (NMR) Spectroscopy, to mathematical modelling, such as minimum energy models are used to solve this problem. Recently, Evolutionary Algorithm studies at the Air Force Institute of Technology (AFIT) include the following: Simple GA, messy GA (mga), fast messy GA (fmGA), and Linkage Learning GA (LLGA), as approaches for potential protein energy minimization. Prepackaged software like GENOCOP, GENESIS, and mGA are in use to facilitate experimentation of these techniques. In addition to this software, a parallelized version of the fmGA, the so called parallel fast messy GA (pfmGA), is found to be good at finding semi-optimal answers in a reasonable time. The aim of this work is to apply a (Multiobjective MO) approach to solving this problem using a modified fast messy GA. By dividing the CHARMm energy model into separate objectives, it should be possible to find structural configurations of a protein that yield lower energy values and ultimately more correct conformations.