G.B. Lamont

Air Force Institute of Technology, Columbus, Ohio, United States

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Publications (56)16.59 Total impact

  • [Show abstract] [Hide abstract]
    ABSTRACT: Receiver systems designed to act on multiple signals of interest at the same time must be able to differentiate between the different signals to properly form simultaneous beams toward each signal. The problem of detecting simultaneous beams is further complicated by the addition of directional interference sources. When interference sources are co-located in frequency with the signals of interest they are confused with additional signals of interest. This paper presents a signal disambiguation algorithm that is able to act in a congested electromagnetic environment and distinguish between multiple signals of interest while rejecting noise co-located in frequency with the signals of interest.
    2014 IEEE Workshop on Statistical Signal Processing, Gold Coast Australia; 06/2014
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    ABSTRACT: A framework for creating a digital representation of physical structural components is investigated. A model updating scheme used with an artificial neural network to map updating parameters to the error observed between simulated experimental data and an analytical model of a turbine-engine fan blade. The simulated experimental airfoil has as-manufactured geometric deviations from the nominal, design-intent geometry on which the analytical model is based. The manufacturing geometric deviations are reduced through principal component analysis, where the scores of the principal components are the unknown updating parameters. A range of acceptable scores is used to devise a design of computer experiments that provides training and testing data for the neural network. This training data is composed of principal component scores as inputs. The outputs are the calculated errors between the analytical and experimental predictions of modal properties and frequency-response functions. Minimizing these errors will result in an updated analytical model that has predictions closer to the simulated experimental data. This minimization process is done through the use of two multiobjective evolutionary algorithms. The goal is to determine if the updating process can identify the principal components used in simulating the experiment data.
    AIAA Journal 03/2014; 52(4). DOI:10.2514/1.J052565 · 1.17 Impact Factor
  • J. Stringer, G. Lamont, G. Akers
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    ABSTRACT: Solving complex real world Multi-Objective Optimization problems is the forte of Multi-Objective Evolutionary Algorithms (MOEA). Such algorithms have been part of many scientific and engineering endeavors. This study applies the NSGA-II and SPEA2 MOEAs to the radar phase coded waveform design problem. The MOEAs are used to generate a series of radar waveform phase codes that have excellent range resolution and Doppler resolution capabilities. The study compares the ability of NSGA-II and SPEA2 to continually evolve (phase code) solutions on the Pareto front for the problem while maintaining a diversity of solutions (phase codes). Results demonstrate that for the radar phase code problem NSGA-II provides a more diverse population of acceptable solutions and therefore a greater number of different viable phase codes when compared to the solutions provided by SPEA2.
    Radar Conference (RADAR), 2012 IEEE; 01/2012
  • J. Stringer, G. Lamont, G. Akers
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    ABSTRACT: This study applies the NSGA-II, SPEA2, and MOEA/D Multi-Objective Evolutionary Algorithms (MOEAs) to the radar phase coded waveform design problem. The MOEAs are used to generate a series of radar waveform phase codes that have excellent range resolution and Doppler resolution capabilities, while maintaining excellent autocorrelation properties. The study compares the ability of NSGA-II, SPEA2, and MOEA/D to generate a Pareto front of phase code solutions, and then improve upon the quality of the solutions while maintaining a sufficient diversity of available radar phase codes. Results demonstrate that for solving moderate to large instances of the radar phase code problem all three MOEAs generate a diverse set of Pareto optimal radar phase codes. The phase codes generated by NSGA-II have overall better autocorrelation properties than those generated by SPEA2 and MOEA/D, however, all three MOEAs produce useable phase codes.
    Evolutionary Computation (CEC), 2012 IEEE Congress on; 01/2012
  • D.L. Hancock, G.B. Lamont
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    ABSTRACT: Trends in computing and networking, in terms of physical capability, attack surface, and attacker sophistication, call for automated, fault-tolerant response systems. Military networks present such environments with unique authorities, critical systems, and threats. Within such environments, multi agent systems may make special contributions regarding recognisance and attack scenarios. We survey three multi agent systems designed for cyber operations, with particular emphasis on our classifier for flow-based attacks, which demonstrates the effectiveness of reputation for distributing classifying agents effectively.
    Computational Intelligence in Cyber Security (CICS), 2011 IEEE Symposium on; 05/2011
  • David L. Hancock, Gary B. Lamont
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    ABSTRACT: Intrusion Detection (ID) is essential for protecting contemporary computer networks from a range of threats. Modern ID techniques must cope with increasingly sophisticated attacks as well as rapidly rising network line speeds. Signature- based ID is forced to sample sparsely, increasing the likelihood of malicious traffic entering the network without scrutiny. Con- sequently, flow-based ID is gaining attention as an effective complement. ID systems are furthermore often characterized as either network-based or host-based. The autonomous multi agent design paradigm is a scalable, attractive alternative for its potential to leverage the strengths of both architectures: the broad perspective and visibility into distributed malicious activity provided by network-based ID, and the comprehensive view of the local node provided by host-based ID. This paper therefore develops an architecture for a new multi agent, flow-based intrusion detection sysem. The architecture is designed in two iterations of increasing complexity. These innovative ID designs use a "repuation" system to permit agents to dynamically find nodes that are most effective for classifying malicious network ac- tivity. Furthermore, each system design includes the development of an innovative classifier that uses multi objective evolutionary algorithms to aid in the search for effective operational parameter values. Evaluation using an extensive agent simulation framework highlights the conditions under which the reputation system provides a significant classification benefit.
    Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2011, New Orleans, LA, USA, 5-8 June, 2011; 01/2011
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    ABSTRACT: Computer network cyber-security is a very serious concern in many commercial, industrial, and military environments. This paper proposes a new computer network security approach defined by self organized agent swarms (SOMAS) which provides a novel computer network security management framework based upon desired overall system behaviors. The SOMAS structure evolves based upon the partially observable Markov decision process (POMDP) formal model and the more complex interactive-POMDP and decentralized-POMDP models. Example swarm specific and network based behaviors are formalized and simulated. This paper illustrates through various statistical testing techniques, the significance of this proposed SOMAS architecture.
    Computational Intelligence in Cyber Security, 2009. CICS '09. IEEE Symposium on; 05/2009
  • B.R. Secrest, G.B. Lamont
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    ABSTRACT: Multitarget tracking MTT algorithms have been tuned by a variety of optimization methods using a single objective, but only recently have they been tuned with multi-objectives technique. The desire to compare single-objective MTT algorithms using numerous metrics is well documented in the literature for over a decade. We discuss an experiment to quantify the need or lack of need for Monte Carlo (MC) runs in tuning the parameters of a MTT algorithm using some of these metrics. The extreme computational requirement of running a MTT MC experiment for each individual evaluation function drives the need to determine the worth of doing so. The results of using a single run are compared to that of using a MC evaluation with multiple runs as compared to a multiobjective evolutionary algorithm approach. Additional analysis is performed on the search space demonstrating other useful information the decision maker may use to select an optimal operating point from a calculated Pareto front.
    Computational intelligence in miulti-criteria decision-making, 2009. mcdm '09. ieee symposium on; 05/2009
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    Matthew G. Judge, Gary B. Lamont
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    ABSTRACT: Computer network security has become a very serious concern of commercial, industrial, and military organizations due to the increasing number of network threats such as outsider intrusions and insider covert activities. An important security element of course is network intrusion detection which is a difficult real world problem that has been addressed through many different solution attempts. Using an artificial immune system has been shown to be one of the most promising results. By enhancing jREMISA, a multi-objective evolutionary algorithm inspired artificial immune system, with a secondary defense layer; we produce improved accuracy of intrusion classification and a flexibility in responsiveness. This responsiveness can be leveraged to provide a much more powerful and accurate system, through the use of increased processing time and dedicated hardware which has the flexibility of being located out of band.
    Proceedings of SPIE - The International Society for Optical Engineering 05/2009; DOI:10.1117/12.822217 · 0.20 Impact Factor
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    ABSTRACT: A wide variety of signal and image processing applications, including the US Federal Bureau of Investigation's fingerprint compression standard [3] and the JPEG-2000 image compression standard [26], utilize wavelets. This paper describes new research that demonstrates how a genetic algorithm (GA) may be used to evolve transforms that outperform wavelets for satellite image compression and reconstruction under conditions subject to quantization error. The new approach builds upon prior work by simultaneously evolving real-valued coefficients representing matched forward and inverse transform pairs at each of three levels of a multi-resolution analysis (MRA) transform. The training data for this investigation consists of actual satellite photographs of strategic urban areas. Test results show that a dramatic reduction in the error present in reconstructed satellite images may be achieved without sacrificing the compression capabilities of the forward transform. The transforms evolved during this research outperform previous start-of-the-art solutions, which optimized coefficients for the reconstruction transform only. These transforms also outperform wavelets, reducing error by more than 0.76 dB at a quantization level of 64. In addition, transforms trained using representative satellite images do not perform quite as well when subsequently tested against images from other classes (such as fingerprints or portraits). This result suggests that the GA developed for this research is automatically learning to exploit specific attributes common to the class of images represented in the training population.© (2008) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
  • M.R. Peterson, G.B. Lamont
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    ABSTRACT: Government, commercial, scientific, and defense applications in image processing often require transmission of large amounts of data across bandwidth-limited channels. Applications require robust transforms simultaneously minimizing bandwidth requirements and image resolution loss. Image processing algorithms take advantage of quantization to provide substantial lossy compression ratios at the expense of resolution. Recent research demonstrates that genetic algorithms evolve filters outperforming standard discrete wavelet transforms in conditions subject to high quantization error. While evolved filters improve overall image quality, wavelet filters typically provide a superior high frequency response, demonstrating improved reconstruction near the edges of objects within an image. This paper presents an algorithm to generate transform filters that optimize edge reconstruction, improving object edge resolution by an average of 17%. Edges within satellite images are isolated, and image transforms are evolved to optimize both the edge and non-edge portions of reconnaissance photographs. Such filters provide an increased object resolution over standard wavelets and traditionally evolved filters for varied applications of image processing.
    Aerospace Conference, 2008 IEEE; 04/2008
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    Adam J. Pohl, Gary B. Lamont
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    ABSTRACT: This investigation develops an innovative algorithm for mul- tiple autonomous unmanned aerial vehicle (UAV) mission routing. The concept of a UAV Swarm Routing Problem (SRP) as a new combinatorics problem, is developed as a variant of the Vehicle Routing Problem with Time Windows (VRPTW). Solutions of SRP problem model result in route assignments per vehicle that successfully track to all targets, on time, within distance constraints. A complexity analysis and multi-objective formulation of the VRPTW indicates the necessity of a stochastic solution approach leading to a multi-objective evolutionary algorithm. A full problem definition of the SRP as well as a multi-objective formu- lation parallels that of the VRPTW method. Benchmark problems for the VRPTW are modified in order to create SRP benchmarks. The solutions show the SRP solutions are comparable or better than the same VRPTW solutions, while also representing a more realistic UAV swarm routing solution.
    Proceedings of the 2008 Winter Simulation Conference, Global Gateway to Discovery, WSC 2008, InterContinental Hotel, Miami, Florida, USA, December 7-10, 2008; 01/2008
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    ABSTRACT: A wide variety of signal and image processing applications, including the US Federal Bureau of Investigation's fingerprint compression standard [3] and the JPEG-2000 image compression standard [26], utilize wavelets. This paper describes new research that demonstrates how a genetic algorithm (GA) may be used to evolve transforms that outperform wavelets for satellite image compression and reconstruction under conditions subject to quantization error. The new approach builds upon prior work by simultaneously evolving real-valued coefficients representing matched forward and inverse transform pairs at each of three levels of a multi-resolution analysis (MRA) transform. The training data for this investigation consists of actual satellite photographs of strategic urban areas. Test results show that a dramatic reduction in the error present in reconstructed satellite images may be achieved without sacrificing the compression capabilities of the forward transform. The transforms evolved during this research outperform previous start-of-the-art solutions, which optimized coefficients for the reconstruction transform only. These transforms also outperform wavelets, reducing error by more than 0.76 dB at a quantization level of 64. In addition, transforms trained using representative satellite images do not perform quite as well when subsequently tested against images from other classes (such as fingerprints or portraits). This result suggests that the GA developed for this research is automatically learning to exploit specific attributes common to the class of images represented in the training population.
  • D. J. Nowak, G. B. Lamont
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    ABSTRACT: Interest in Self-Organized (SO) unmanned aerial vehicles (UAVs) systems is increasing because of their flexibility, versatility and economics. Many countries and industries are developing autonomous and swarming UAVs for reconnaissance, surveillance, intelligence gathering, and target engagement and neutralization. The processes for effectively developing these systems are still in their infancy. Currently, little effort is focused on building simple agent rules with low-level SO systems communication in order to facilitate emergent behaviors. Note that only with the use of effective control structures can the full potential of these systems realized. Presented is an innovative new paradigm for developing SO-based autonomous vehicles. Using a formal design model, the Interactive Partially Observable Markov Decision Process, a full understanding of this SO domain is possible. With this design model and a focused effort on the minimization of computational and informational complexity, emergent entangled control hierarchies allow the SO rules to operate efficiently and effectively. This work extends the formal model decomposition technique, and in doing so ties in the information theoretic optimization to develop emergent structures. Preliminary computational results reflect limited success.
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    Gary B Lamont
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    ABSTRACT: The purpose of this paper is to discuss the design and implementation of comprehensive mission planning systems for swarms of autonomous aerial vehicles (UAV). Such a system could integrate several problem domains including path planning, vehicle routing, and swarm behavior as based upon a hierarchical architecture. The example developed system consists of a parallel multi-objective evolutionary algorithm-based terrain-following parallel path planner, a multi-objective evolutionary algorithm (MOEA) for the UAV swarm router, and a parallel simulation. Generic objectives include minimizing cost, time, and risk generally associated with a three dimensional vehicle routing problem (VRP). The concept of the Swarm Routing Problem (SRP) as a new combinatorics problem for use in modeling UAV swarm routing is presented as a variant of the Vehicle Routing Problem with Time Windows (VRPTW). Various multi-objective VRPTW routing benchmarks result in very good Pareto-based performance with the MOEA which is also reflected in the results of the new SRP benchmarks. The culmination of this effort is the development of an extensible developmental path planning model integrated with swarm routing behavior and tested with a parallel UAV simulation. Discussions of this system's capabilities are presented along with recommendations for generic development of UAV swarm mission planning.
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    ABSTRACT: Evolutionary algorithms have been applied to a variety of network flow problems with acceptable results. In this research, a multiobjective evolutionary algorithm (MOEA) is used to solve a variation of the multicommodity capacitated network design problem (MCNDP). This variation represents a hybrid communication network as found in network centric models with multiple objectives including costs, delays, robustness, vulnerability, and reliability. Nodes in such centric systems can have multiple and varying link capacities, rates and information (commodity) quantities to be delivered and received. Each commodity can have an independent prioritized bandwidth requirement as well. Insight to the MCNDP problem domain and Pareto structure is developed. The nondominated sorting genetic algorithm (NSGA-II) is modified and extended to solve such a MCNDP. Since the MCNDP is highly constrained, a novel initialization procedure and mutation method are also integrated into this MOEA. Empirical results and analysis indicate that effective solutions are generated very efficiently
    Computational Intelligence in Security and Defense Applications, 2007. CISDA 2007. IEEE Symposium on; 05/2007
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    ABSTRACT: The purpose of this research is to design and implement a comprehensive mission planning system for swarms of autonomous aerial vehicles (UAV). The system integrates several problem domains including path planning, vehicle routing, and swarm behavior as based upon a hierarchical architecture. The developed system consists of a parallel, multi-objective evolutionary algorithm-based terrain-following parallel path planner and an evolutionary algorithm-based vehicle router. Objectives include minimizing cost and risk generally associated with a three dimensional vehicle routing problem (VRP). The culmination of this effort is the development of an extensible developmental path planning model integrated with swarm behavior and tested with a parallel UAV simulation. Discussions on the system's capabilities are presented along with recommendations for further development.
    Computational Intelligence in Multicriteria Decision Making, IEEE Symposium on; 05/2007
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    ABSTRACT: The content of the book provides a general overview of the field now called evolutionary multiobjective optimization, which refers to the use of the evolutionary algorithms of any sort to solve multiobjective optimization problems. It covers also other metaheuristics that have been used to solve multiobjective optimization problems. This book should be of interest to the many disciplines that have to deal with multiobjective optimization. Each chapter is complemented by discussion questions and several ideas meant to trigger novel research paths. Chapter 1 presents the basic terminology and nomenclature for use throughout the rest of the book. Chapter 2 provides an overview of the different multi-objective evolutionary (MOEAs) currently available. Chapter 3 discusses both coevolutionary MOEAs and hybridizations of MOEAs with local search procedures. A variety of MOEA implementations within each of these two types of approaches are presented summarized, categorized and analyzed. Chapter 4 presents a detailed developement of MOP test suites ranging from numerical functions to discrete NP-Complete problems and real-world applications. MOEA performance comparisons are presented in Chapter 5. Chapter 6 summarizes the MOEA theoretical results found in the literature. Chapter 7 attempts to group and classify the wide variety of applications found in the literature. Chapter 8 classifies and analyzes the existing research on parallel MOEAs. Chapter 9 describes the most representative research regarding the incorporation of preferences articulation into MOEAs. Chapter 10 discusses multiobjective extensions of other metaheuristics used for optimization. The first edition was published in 2002 (see Zbl 1130.90002).
    2nd 01/2007; Springer, New York.
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    M.A. Russell, G.B. Lamont, K. Melendez
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    ABSTRACT: Unmanned aerial vehicle (UAV) research is an increasingly important pillar of national security and military interest. A high fidelity discrete event simulation is prerequisite to any systems implementation. The synchronous parallel environment for emulation and discrete event simulation (SPEEDES) is a versatile and powerful tool that can be used for realization of this objective. A suite of five expert measures the efficiency a parallel UAV swarming SPEEDES application. Results indicate that the conservative time management produces more than twice the speed up as optimistic time management.
    Simulation Conference, 2005 Proceedings of the Winter; 01/2006
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    ABSTRACT: We present a new multiobjective evolutionary algorithm (MOEA), called fast Pareto genetic algorithm (FastPGA). FastPGA uses a new fitness assignment and ranking strategy for the simultaneous optimization of multiple objectives where each solution evaluation is computationally- and/or financially-expensive. This is often the case when there are time or resource constraints involved in finding a solution. A population regulation operator is introduced to dynamically adapt the population size as needed up to a user-specified maximum population size. Computational results for a number of well-known test problems indicate that FastPGA is a promising approach. FastPGA outperforms the improved nondominated sorting genetic algorithm (NSGA-II) within a relatively small number of solution evaluations.
    Evolutionary Multi-Criterion Optimization, 4th International Conference, EMO 2007, Matsushima, Japan, March 5-8, 2007, Proceedings; 01/2006