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检索条件"任意字段=Conference on Evolutionary and Bio-Inspired Computation - Theory and Applications VI"

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Initial Data Sampling in Design Optimization

Initial Data Sampling in Design Optimization

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conference on evolutionary and bio-inspired computation - theory and applications V

作者： Southall, Hugh L. O'Donnell, Terry H. US Air Force Res Lab 80 Scott Dr Hanscom AFB MA 01731 USA On Site Consultant Solid State Sci Corp Hollins NH 03049 USA Elect Syst Ctr ESC XRC Hanscom AFB MA 01731 USA

ISBN: (纸本)9780819486332

evolutionary computation (EC) techniques in design optimization such as genetic algorithms (GA) or efficient global optimization (EGO) require an initial set of data samples (design points) to start the algorithm. They are obtained by evaluating the cost function at selected sites in the input space. A two-dimensional input space can be sampled using a Latin square, a statistical sampling technique which samples a square grid such that there is a single sample in any given row and column. The Latin hypercube is a generalization to any number of dimensions. However, a standard random Latin hypercube can result in initial data sets which may be highly correlated and may not have good space-filling properties. There are techniques which address these issues. We describe and use one technique in this paper.

关键词： Efficient global optimization (EGO) computational electromagnetic (CEM) evolutionary computation (EC) antenna design optimization design and analysis of computer experiments (DACE) Latin Hypercube Design (LHD) orthogonal-maximin LHD (OMLHD) kriging deterministic function approximation

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Image Sets for Satellite Image Processing Systems

Image Sets for Satellite Image Processing Systems

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conference on evolutionary and bio-inspired computation - theory and applications V

作者： Peterson, Michael R. Horner, Toby Temple, Asael Univ Hawaii Dept Comp Sci & Engn Hilo HI 96720 USA

ISBN: (纸本)9780819486332

The development of novel image processing algorithms requires a diverse and relevant set of training images to ensure the general applicability of such algorithms for their required tasks. Images must be appropriately chosen for the algorithm's intended applications. Image processing algorithms often employ the discrete wavelet transform (DWT) algorithm to provide efficient compression and near-perfect reconstruction of image data. Defense applications often require the transmission of images and video across noisy or low-bandwidth channels. Unfortunately, the DWT algorithm's performance deteriorates in the presence of noise. evolutionary algorithms are often able to train image filters that outperform DWT filters in noisy environments. Here, we present and evaluate two image sets suitable for the training of such filters for satellite and unmanned aerial vehicle imagery applications. We demonstrate the use of the first image set as a training platform for evolutionary algorithms that optimize discrete wavelet transform (DWT)-based image transform filters for satellite image compression. We evaluate the suitability of each image as a training image during optimization. Each image is ranked according to its suitability as a training image and its difficulty as a test image. The second image set provides a test-bed for holdout validation of trained image filters. These images are used to independently verify that trained filters will provide strong performance on unseen satellite images. Collectively, these image sets are suitable for the development of image processing algorithms for satellite and reconnaissance imagery applications.

关键词： image processing wavelets evolutionary computation unmanned aerial vehicles

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Using a multi-agent Evidential Reasoning Network as the objective function for an evolutionary Algorithm

Using a multi-agent Evidential Reasoning Network as the obje...

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conference on evolutionary and bio-inspired computation - theory and applications

作者： Woodley, Robert Lindahl, Eric Barker, Joseph 21st Century Syst Inc 199 East 4th StSuite B Fort Leonard Wood MO 65473 USA 21st Century Syst Inc Omaha NE 68106 USA

ISBN: (纸本)9780819466853

A culturally diverse group of people are now participating in military multinational coalition operations (e.g., combined air operations center, training exercises such as Red Flag at Nellis AFB, NATO AWACS), as well as in extreme environments. Human biases and routines, capabilities, and limitations strongly influence overall system performance;whether during operations or simulations using models of humans. Many missions and environments challenge human capabilities (e.g., combat stress, waiting, fatigue from long duty hours or tour of duty). This paper presents a team selection algorithm based on an evolutionary algorithm. The main difference between this and the standard EA is that a new form of objective function is used that incorporates the beliefs and uncertainties of the data. Preliminary results show that this selection algorithm will be very beneficial for very large data sets with multiple constraints and uncertainties. This algorithm will be utilized in a military unit selection tool.

关键词： evolutionary algorithm multi-agent subjective logic Evidential Reasoning Network

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Genetic programming techniques for thin-wire antennas

Genetic programming techniques for thin-wire antennas

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conference on evolutionary and bio-inspired computation - theory and applications

作者： O'Donnell, Terry H. Air Force Res Lab Hanscom AFB MA 01730 USA

ISBN: (纸本)9780819466853

Simple genetic algorithm optimizations often utilize fixed-length chromosomes containing a predefined set of parameters to be optimized. While such algorithms have successfully created electrically small narrow-band and large wide-band military antennas, they require the antenna designer to have a fairly concrete antenna representation prior to exercising the genetic algorithm. In this research we investigate the use of genetic programming (GP) techniques to "program" the design of simple thin-wire antennas. Genetic programming techniques offer the potential to create random, multi-arm, multi-dimension antennas from variable length, tree-like chromosomes. We present a new genetic programming paradigm for creating multi-branched, thin-wire, genetic antennas and describe how GP commands are represented and decoded into physical antenna structures. We present preliminary results obtained from this algorithm showing evolutions along Pareto fronts representing antenna electrical size, VSWR, and antenna quality factor (Q).

关键词： genetic programming Genetic algorithm evolutionary optimization antenna design antenna optimization GP GA genetic design

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Modeling decision uncertainties in total situation awareness using cloud computation theory

Modeling decision uncertainties in total situation awareness...

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conference on evolutionary and bio-inspired computation - theory and applications V

作者： Zein-Sabatto, Saleh Khoshnaw, Abdulqadir Shetty, Sachin Malkani, Mohan Mitra, Atindra Tennessee State Univ Coll Engn Technol & Comp Sci Nashville TN 37203 USA US Air Force Res Lab EOS Div Sensors Directorate Wright Patterson AFB OH 45433 USA

ISBN: (纸本)9780819486332

Uncertainty plays decisive role in the confidence of the decisions made about events. For example, in situation awareness, decision-making is faced with two types of uncertainties;information uncertainty and data uncertainty. Data uncertainty exists due to noise in sensor measurements and is classified as randomness. Information uncertainty is due to ambiguity of using (words) to describe events. This uncertainty is known as fuzziness. Typically, these two types of uncertainties are handled separately using two different theories. Randomness is modeled by probability theory, while fuzzy-logic is used to address fuzziness. In this paper we used the Cloud computation theory to treat data randomness and information fuzziness in one single model. First, we described the Cloud theory then used the theory to generate one and two-dimensional Cloud models. Second, we used the Cloud models to capture and process data randomness and fuzziness in information relative to decision-making in situation awareness. Finally, we applied the models to generate security decisions for security monitoring of sensitive area. Testing results are reported at the end of the paper.

关键词： Situation awareness Layered sensing systems Cloud theory Uncertainties

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Differential profiling of volatile organic compound biomarker signatures utilizing a logical statistical filter-set and novel hybrid evolutionary classifiers

Differential profiling of volatile organic compound biomarke...

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conference on evolutionary and bio-inspired computation - theory and applications vi

作者： Grigsby, Claude C. Zmuda, Michael A. Boone, Derek W. Highlander, Tyler C. Kramer, Ryan M. Rizki, Mateen M. USAF Human Biosignatures Branch Human Performance Wing 711 Res Lab 2510 5th StArea BBld 840 Wright Patterson AFB OH 45433 USA

ISBN: (纸本)9780819490803

A growing body of discoveries in molecular signatures has revealed that volatile organic compounds (VOCs), the small molecules associated with an individual's odor and breath, can be monitored to reveal the identity and presence of a unique individual, as well their overall physiological status. Given the analysis requirements for differential VOC profiling via gas chromatography/mass spectrometry, our group has developed a novel informatics platform, Metabolite Differentiation and Discovery Lab (MeDDL). In its current version, MeDDL is a comprehensive tool for time-series spectral registration and alignment, visualization, comparative analysis, and machine learning to facilitate the efficient analysis of multiple, large-scale biomarker discovery studies. The MeDDL toolset can therefore identify a large differential subset of registered peaks, where their corresponding intensities can be used as features for classification. This initial screening of peaks yields results sets that are typically too large for incorporation into a portable, electronic nose based system in addition to including VOCs that are not amenable to classification;consequently, it is also important to identify an optimal subset of these peaks to increase classification accuracy and to decrease the cost of the final system. MeDDL's learning tools include a classifier similar to a K-nearest neighbor classifier used in conjunction with a genetic algorithm (GA) that simultaneously optimizes the classifier and subset of features. The GA uses ROC curves to produce classifiers having maximal area under their ROC curve. Experimental results on over a dozen recognition problems show many examples of classifiers and feature sets that produce perfect ROC curves.

关键词： machine learning receiver operating characteristic K-nearest neighbor genetic algorithm biomarker differential profiling gas chromatography mass spectrometry volatile organic compound

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Evolving Matched Filter Transform Pairs for Satellite Image Processing

Evolving Matched Filter Transform Pairs for Satellite Image ...

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conference on evolutionary and bio-inspired computation - theory and applications V

作者： Peterson, Michael R. Horner, Toby Moore, Frank Univ Hawaii Dept Comp Sci & Engn Hilo HI 96720 USA Univ Alaska Anchorage Dept Math & Comp Sci Anchorage AK 99508 USA

ISBN: (纸本)9780819486332

Wavelets provide an attractive method for efficient image compression. For transmission across noisy or bandwidth limited channels, a signal may be subjected to quantization in which the signal is transcribed onto a reduced alphabet in order to save bandwidth. Unfortunately, the performance of the discrete wavelet transform (DWT) degrades at increasing levels of quantization. In recent years, evolutionary algorithms (EAs) have been employed to optimize wavelet-inspired transform filters to improve compression performance in the presence of quantization. Wavelet filters consist of a pair of real-valued coefficient sets;one set represents the compression filter while the other set defines the image reconstruction filter. The reconstruction filter is defined as the biorthogonal inverse of the compression filter. Previous research focused upon two approaches to filter optimization. In one approach, the original wavelet filter is used for image compression while the reconstruction filter is evolved by an EA. In the second approach, both the compression and reconstruction filters are evolved. In both cases, the filters are not biorthogonally related to one another. We propose a novel approach to filter evolution. The EA optimizes a compression filter. Rather than using a wavelet filter or evolving a second filter for reconstruction, the reconstruction filter is computed as the biorthogonal inverse of the evolved compression filter. The resulting filter pair retains some of the mathematical properties of wavelets. This paper compares this new approach to existing filter optimization approaches to determine its suitability for the optimization of image filters appropriate for defense applications of image processing.

关键词： image processing wavelets evolutionary computation unmanned aerial vehicles

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Fitness landscape analysis of evolved image transforms for defense applications

Fitness landscape analysis of evolved image transforms for d...

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conference on evolutionary and bio-inspired computation - theory and applications II

作者： Peterson, Michael R. Lamont, Gary B. Wright State Univ Dept Comp Sci & Engn Dayton OH 45435 USA USAF Inst Technol Dept Elect & Comp Engn Wright Patterson AFB OH USA

ISBN: (纸本)9780819471550

In recent years, there has been increased interest in the use of evolutionary algorithms (EAs) in the design of robust image transforms for use in defense and security applications. An EA replaces the defining filter coefficients of a discrete wavelet transform (DWT) to provide improved image quality within bandwidth-limited image processing applications, such as the transmission of surveillance data by swarms of unmanned aerial vehicles (UAVs) over shared communication channels. The evolvability of image transform filters depends upon the properties of the underlying fitness landscape traversed by the evolutionary algorithm. The landscape topography determines the ease with which all optimization algorithm may identify highly-fit filters. The properties of a fitness landscape depend upon a chosen evaluation function defined over the space of possible solutions. Evaluation functions appropriate for image filter evolution include mean squared error (MSE), the universal image quality index (UQI), peak signal-to-noise ratio (PSNR), and average absolute pixel error (AAPE). We conduct a theoretical comparison of these image quality measures using random walks through fitness landscapes defined over each evaluation function. This analysis allows us to compare the relative evolvability of the various potential image quality measures by examining fitness topology for each measure in terms of ruggedness and deceptiveness. A theoretical understanding of the topology of fitness landscapes aids in the design of evolutionary algorithms capable of identifying near-optimal image transforms suitable for deployment in defense and security applications of image processing.

关键词： wavelets image processing evolutionary computation fitness landscape analysis

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Categorification of the Layered Sensing Construct

Categorification of the Layered Sensing Construct

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conference on evolutionary and bio-inspired computation - theory and applications V

作者： Sturtz, Kirk Culbertson, Jared Oxley, Mark E. Rogers, Steven K. Universal Math Dayton OH 45417 USA US Air Force Inst Tech Dept Math & Stat Wright Patterson AFB OH 45433 USA US Air Force Res Lab Sensors & Informat Directorates Wright Patterson AFB OH 45433 USA

ISBN: (纸本)9780819486332

We propose a mathematical formulation for a layered sensing architecture based on the theory of categories that will allow us to abstractly define agents and their interactions in such a way that we can treat human and machine (or systems of these) agents homogeneously. One particular advantage is that this general formulation will allow the development of multi-resolution analyses of a given situation that is independent of the particular models used to represent a given agent or system of agents. In this paper, we define the model and prove basic facts that will be fundamental in future work. Central to our approach is the integration of uncertainty into our model. Such a framework is necessitated by our desire to define (among other things) measures of alignment and efficacy for systems of heterogeneous agents operating in a diverse and complex environment.

关键词： agent model category theory probabilistic Giry monad layered sensing

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evolutionary optimization of cooperative heterogeneous teams

Evolutionary optimization of cooperative heterogeneous teams

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conference on evolutionary and bio-inspired computation - theory and applications

作者： Soule, Terence Heckendorn, Robert B. Univ Idaho Dept Comp Sci Moscow ID 83843 USA

ISBN: (纸本)9780819466853

There is considerable interest in developing teams of autonomous, unmanned vehicles that can function in hostile environments without endangering human lives. However, heterogeneous teams, teams of units with specialized roles and/or specialized capabilities, have received relatively little attention. Specialized roles and capabilities can significantly increase team effectiveness and efficiency. Unfortunately, developing effective cooperation mechanisms is much more difficult in heterogeneous teams. Units with specialized roles or capabilities require specialized software that take into account the role and capabilities of both itself and its neighbors. evolutionary algorithms, algorithms modeled on the principles of natural selection, have a proven track record in generating successful teams for a wide variety of problem domains. Using classification problems as a prototype, we have shown that typical evolutionary algorithms either generate highly effective teams members that cooperate poorly or poorly performing individuals that cooperate well. To overcome these weaknesses we have developed a novel class of evolutionary algorithms. In this paper we apply these algorithms to the problem of controlling simulated, heterogeneous teams of "scouts" and "investigators". Our test problem requires producing a map of an area and to further investigate "areas of interest". We compare several evolutionary algorithms for their ability to generate individually effective members and high levels of cooperation.

关键词： evolutionary algorithms genetic programming cooperation autonomous vehicles planning heterogeneous teams

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