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This research emphasizes explicit building block (BB) based MOEA performance with detailed symbolic representations. An explicit BB-based MOEA for solving constrained and real-world multiple objective problems (MOPs) is developed, the multiobjective messy genetic algorithm II (MOMGA-II) in order to validate symbolic BB concepts. This algorithm provides insight into solving difficult NP-complete MOPs that are generally not realized through the use of implicit BB-based MOEA approaches. Specific constrained integer problem examples include advanced logistics and modified knapsack problems. A primary focus is on generic repair mechanisms for generating feasible solutions per generation. The insight provided is necessary to increase the effectiveness and efficiency over all possible MOEA approaches.

The use of large scale distributed systems for multiple perhaps heterogeneous applications is becoming more commonplace. The organizations that are utilizing these resources must ensure that the applications are executed in a timely manner without unnecessary wasting the resources available on the distributed system. Characteristics of two distributed computing applications are presented; large scale discrete event simulation and a real-time digital signal processing activity. A stochastic processor allocation algorithm is developed for assigning processes to processors in an effective and efficient manner based upon application characteristics. In particular, a multiobjective evolutionary algorithm (MOEA) is created in order to examine Pareto results for such diverse processor allocation. The results indicate that the focus of the two distinct applications and the associated respective optimal regions have distinct differences.

The multiobjective quadratic assignment problem is an NP-complete problem with a multitude of real-world applications. The specific application addressed is the minimization of communication flows in a heterogenous mix of unmanned aerial vehicles. Developed is a multiobjective approach to solving the general mQAP for this UAV application. The combinatoric nature of this problem calls for a stochastic search algorithm; moreover, the multiobjective fast messy genetic algorithm (MOMGA-II) [Jesse Zydallis (2003)] is used for experimentation. Results indicate that much of the Pareto optimal points are found.

Research in steganalysis is motivated by the concern that communications associated with illicit activity could be hidden in seemingly innocent electronic transactions. By developing defensive tools before steganographic communication grows, computer security professionals may be better prepared for the threat. This research investigated an artificial immune system approach to novel steganography detection for digital images. The results show promising detection and false positive rates.

Combining horizontal flow treatment wells (HFTWs) with in situ biodegradation is an innovative approach with the potential to remediate perchlorate-contaminated groundwater. A model has been developed that combines the groundwater flow induced by HFTWs with biodegradation processes that result from using the HFTWs to mix electron donor into perchlorate-contaminated groundwater. The model can be used to select engineering design parameters that optimize performance under given site conditions. In particular, one desires to design a system that 1) maximizes perchlorate destruction, 2) minimizes treatment expense, and 3) attains regulatory limits on downgradient contaminant concentrations. Unfortunately, for a relatively complex technology like in situ bioremediation, system optimization is not straightforward. In this study, a general multi-objective parallel evolutionary algorithm call GENMOP is developed and used to stochastically determine design parameter values (flow rate, well spacing, concentration of injected electron donor, and injection schedule) in order to maximize perchlorate destruction while minimizing cost. Results indicate that the relationship between perchlorate mass removal and operating cost is positively correlated and nonlinear. For equivalent operating times and costs, the solutions show that the technology achieves higher perchlorate mass removals for a site having both higher hydraulic conductivity as well as higher initial perchlorate concentrations.