Michael Scott Brown

Dynamic-radius species-conserving genetic algorithm for the financial forecasting of dow jones index stocks

This research uses a Niche Genetic Algorithm (NGA) called Dynamic-radius Species-conserving Genetic Algorithm (DSGA) to select stocks to purchase from the Dow Jones Index. DSGA uses a set of training data to produce a set of rules. These rules are then used to predict stock prices. DSGA is an NGA that uses a clustering algorithm enhanced by a tabu list and radial variations. DSGA also uses a shared fitness algorithm to investigate different areas of the domain. This research applies the DSGA algorithm to training data which produces a set of rules. The rules are applied to a set of testing data to obtain results. The DSGA algorithm did very well in predicting stock movement.

RANGE-LIMITED UAV TRAJECTORY USING TERRAIN MASKING UNDER RADAR DETECTION RISK

Military manned and unmanned aerial vehicles (UAVs) may perform missions in contested airspace, where survival of the vehicle requires avoidance of hostile radar coverage. This research sought to determine optimum flight-path routes that make maximum utilization of UAV terrain-masking opportunities and flight range capability to avoid radar detection. The problem was formulated as one of constrained optimization in three dimensions; advantageous solutions were identified using Algorithm A*. Topographical features were exploited by the algorithm to avoid radar detection. The model included provisions for preferred altitude ranges, adjustable aircraft climb- and descent- rate envelopes, movement costs based on fractional detection probability, radar horizon masking, and simulated radar cross-section lookup tables.

A complexity measurement for de novo protein folding

Predicting how a protein folds based solely on its amino acid sequence is an ongoing challenge for the fields of Bioinformatics and Computer Science. Previous attempts to solve this problem have relied on algorithms and a specific set of benchmark proteins. However, there is currently no method for determining if the set of benchmark proteins share a similar level of complexity with proteins of similar size. As a result, a larger variety of benchmarks might be needed to evade this problem and a measure of complexity established to determine the validity of all benchmarks. We propose here the Ouroboros Complexity Measurement for the de novo folding of proteins. This measurement is easy to compute (not an NP hard problem) and allows the comparing of protein complexity.

Improved Hybrid Opponent System for Professional Military Training

Article history: Received: 28 June, 2017 Accepted: 13 September, 2017 Online: 05 October, 2017 Described herein is a general-purpose software engineering architecture for autonomous, computer controlled opponent implementation in modern maneuver warfare simulation and training. The implementation has been developed, refined, and tested in the user crucible for several years. The approach represents a hybrid application of various well-known AI techniques, including domain modeling, agent modeling, and object-oriented programming. Inspired by computer chess approaches, the methodology combines this theoretical foundation with a hybrid and scalable portfolio of additional techniques. The result remains simple enough to be maintainable, comprehensible for the code writers as well as the end-users, and robust enough to handle a wide spectrum of possible mission scenarios and circumstances without modification.

Software engineering a multi-layer and scalable autonomous forces "A.I." for professional military training

Described herein is a general-purpose software engineering architecture for autonomous, computer controlled opponent implementation in modern maneuver warfare simulation and training. The implementation has been developed, refined, and tested in the user crucible for several years. The approach represents a hybrid application of various well-known AI techniques, including domain modeling, agent modeling, and object-oriented programming. Inspired by computer chess approaches, the methodology combines this theoretical foundation with a hybrid and scalable portfolio of additional techniques. The result remains simple enough to be maintainable and comprehensible for the code writers as well as the end-users, and robust enough to handle a wide spectrum of possible mission scenarios and circumstances without modification.