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SPBMODEL, a legacy southern pine beetle (SPB) simulation model, was translated into a new JavaTM model called Javahog. The Javahog output was verified to be essentially identical to SPBMODEL output by means of standard and paired t-tests. Javahog was placed online and is currently accessible via a servlet. Genetic algorithms (GAs) were applied to the Javahog model. GAs are a type of optimization heuristic that operate as an analog to evolution. GAs "evolve" a very good solution to a complex problem. In this case, GAs were intended to evolve a very good version of SPBMODEL. GAs were applied in part to improve upon the SPBMODEL design, and in part to demonstrate that GAs are effective tools for recalibrating legacy simulation models. Beyond simply recalibrating model parameters, the GA was used to select optimal functional forms for the development rates of each SPB life stage. The GA evolved a model that performed better than SPBMODEL at predicting observed field data, according to a balanced fitness function and according to sums of squared errors. However, from a visual comparison of the output of both models versus observed field data, neither model achieved satisfactory performance. / Master of Science

Yes / Identification of fuzzy rules is an important issue in designing of a fuzzy neural network (FNN). However, there is no systematic design procedure at present. In this paper we present a genetic algorithm (GA) based learning algorithm to make use of the known membership function to identify the fuzzy rules form a large set of all possible rules. The proposed learning algorithm initially considers all possible rules then uses the training data and the fitness function to perform ruleselection. The proposed GA based learning algorithm has been tested with two different sets of training data. The results obtained from the experiments are promising and demonstrate that the proposed GA based learning algorithm can provide a reliable mechanism for fuzzy rule selection.

Genetic algorithm and artificial life techniques are applied to the development of challenging and interesting opponents in a combat-based computer game. Computer simulations are carried out against an idealized human player to gather data on the effectiveness of the computer generated opponents.

"Micro aerial vehicles (MAV) are small remotely piloted or autonomous aircraft. Wingspans of MAVs can be as small as six inches to allow MAV’s to avoid detection during reconnaissance missions. Improving the aerodynamic efficiency of MAV’s by increasing the lift to drag ratio could lead to increased MAV range and endurance or future decreases in aircraft size. In this project, biologically inspired flight is used as a framework to improve MAV performance since MAV’s operate in a similar flight regime to birds. A novel wind tunnel apparatus was constructed that allows the planform shape of a MAV wing to be easily altered. The scale-model wing mimics a bird wing by using variable feather lengths to vary the wing planform shape. Genetic algorithms that use natural selection as an optimization process were applied to establish successive populations of candidate wing shapes. These wing shapes were tested in the wind tunnel where wings with higher fitness values were allowed to ‘breed’ and create a next generation of wings. After numerous generations were tested an acceptably strong solution was found that yielded a lift to drag ratio of 3.28. This planform was a non conventional planform that further emphasized the ability of a genetic algorithm to find a novel solution to a complex problem. Performance of the best planform was compared to previously published data for conventional MAV planform shapes. Results of this comparison show that while the highest lift to drag ratio found from the genetic algorithm is lower than published data, inabilities of the test wing to accurately represent a flat plate Zimmerman planform and limitations of the test setup can account for these discrepancies."

This dissertation presents a study of linearization techniques that have been applied to power amplifiers in the cellular communication industry. The objective of this work is to understand the limitations of power amplifiers, specifically the limitations introduced by the use of spectrally efficient modulation schemes. The digitization of communication systems has favored the use of new techniques and technologies capable of increasing the efficiency of costly power amplifiers. The work explores traditional and digital linearization systems; an algorithm based on the principles of natural recombination is proposed to directly address the limitations of previous embodiments. Previous techniques, although effective, have significant implementation costs that increase exponentially with the increasing signal bandwidths. The proposed software-hardware architecture significantly reduces implementation costs and the overall complexity of the design without sacrificing performance. To fulfill the requirements of this study, multiple systems are implemented through simulation and closed-loop hardware. Both simulation and hardware embodiments meet the expected performance metrics, providing validation of the proposed algorithm. The application of the algorithm to memory power amplifier linearization is a new approach to adaptive digital pre-distortion using narrowband feedback. The work will show performance improvements on an amplifier with memory effects suggesting that this technique can be employed as a lower-cost solution to meet requirements when compared to typical system implementations.