Explicit Building Block Multiobjective Evolutionary Computation: Methods and Applications

This dissertation presents principles, techniques, and performance of evolutionary computation optimization methods. Concentration is on concepts, design formulation, and prescription for multiobjective problem solving and explicit building block (BB) multiobjective evolutionary algorithms (MOEAs). Current state-of-the-art explicit BB MOEAs are addressed in the innovative design, execution, and testing of a new multiobjective explicit BB MOEA. 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 effectiveness and efficiency. The main result of this research is the development of a more robust algorithm where MOEA concepts are implicitly employed. Testing shows that the new MOEA can be more effective and efficient than previous state-of-the-art explicit BB MOEAs for selected test suite multiobjective optimization problems (MOPs) and U.S. Air Force applications. Other contributions include the extension of explicit BB definitions to clarify the meanings for good single and multiobjective BBs. A new visualization technique is developed for viewing genotype, phenotype, and the evolutionary process in finding Pareto front vectors while tracking the size of the BBs. The visualization technique is the result of a BB tracing mechanism integrated into the new MOEA that enables one to determine the required BB sizes and assign an approximation epistasis level for solving a particular problem. The culmination of this research is explicit BB state-of-the-art MOEA technology based on the MOEA design, BB classifier type assessment, solution evolution visualization, and insight into MOEA test metric validation and usage as applied to test suite, deception, bioinformatics, unmanned vehicle flight pattern, and digital symbol set design MOPs

Simulating urban soil carbon decomposition using local weather input from a surface model

Non peer reviewe

Exploring the Relationship between Tourism and Economic Growth in Small Island Economies: A Study of Fiji

This study examines the effect of tourism, measured by visitor arrivals) on the economic growth of Fiji, a small island economy, over the period 1975 to 2015. We use a neoclassical framework and regression analysis to examine the short-run and the long-run effects of tourism whilst accounting for structural breaks. We confirm the presence of a long-run association using the two-step procedure of Engle and Granger (1987) and the ARDL bounds test of Pesaran, Shin and Smith (2001). From the long-run results, we note that a 1% increase in visitor arrivals contribute about 0.22% to the GDP per capita. The short run elasticity is noted to be 0.19%. The study finds evidence of a unidirectional causality from economic growth to tourism, and mutually reinforcing effect between capital investment and tourism. Thus, we can expect greater impact of tourism on the economic growth through tourism related investment activities such as improvements in airports, roads, transportation, financial sector and telecommunications, and parks and beaches