Browsing by Subject "genetic algorithms"

Now showing 1 - 3 of 3
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    Characterizing software components using evolutionary testing and path-guided analysis
    (2013-12-16) McNeany, Scott Edward; Hill, James H. (James Haswell); Raje, Rajeev; Al Hasan, Mohammad; Fang, Shiaofen
    Evolutionary testing (ET) techniques (e.g., mutation, crossover, and natural selection) have been applied successfully to many areas of software engineering, such as error/fault identification, data mining, and software cost estimation. Previous research has also applied ET techniques to performance testing. Its application to performance testing, however, only goes as far as finding the best and worst case, execution times. Although such performance testing is beneficial, it provides little insight into performance characteristics of complex functions with multiple branches. This thesis therefore provides two contributions towards performance testing of software systems. First, this thesis demonstrates how ET and genetic algorithms (GAs), which are search heuristic mechanisms for solving optimization problems using mutation, crossover, and natural selection, can be combined with a constraint solver to target specific paths in the software. Secondly, this thesis demonstrates how such an approach can identify local minima and maxima execution times, which can provide a more detailed characterization of software performance. The results from applying our approach to example software applications show that it is able to characterize different execution paths in relatively short amounts of time. This thesis also examines a modified exhaustive approach which can be plugged in when the constraint solver cannot properly provide the information needed to target specific paths.
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    Interactive Watershed Optimization in the Presence of Spatially-varying and Uncertain Stakeholder Preferences
    (IEEE, 2020-09) Babbar Sebens, Meghna; Cannady Shultz, Kenneth R.; Mukhopadhyay, Snehasis; Computer and Information Science, School of Science
    Watershed planning over a geographic area is a difficult task primarily due to the presence of large number of stakeholders and decision makers whose intrinsic conflicting and/or subjective preferences often lead to uncertainty in perceived fitness of planning decisions. Deciding which watershed strategy should be implemented at what location requires a participatory approach to design and decision making, if adoption of landscape decisions is critical to success. Analytical participatory design (APD) approaches aim to enable farmers, environmentalists, government agencies, and other stakeholders to visualize the landscape, explore and design competitive scenarios of implementing certain management practices on the landscape. Since these approaches improve decision makers' awareness of opportunities and constraints in the co-existing physical and human systems, it is hypothesized that they can be used to generate acceptable decisions that are robust to uncertainties in stakeholder preferences. An APD method based on Interactive optimization is described in this paper and tested for design of wetlands in a study watershed site (Eagle Creek Watershed) in the state of Indiana. The method is then used to test research hypothesis by involving multiple virtual stakeholders as surrogates to diverse human users and their preferences. The results indicate that, while, as expected, the interactive optimization approach results in lower values of the financial and environmental objective criteria (which are being traded off against users' diverse subjective personal criteria), it also results in a relatively high degree of user consensus, indicating high likelihood of adoption of the generated solutions by the stakeholders.
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    Music Recombination Using a Genetic Algorithm
    (ICMA, 2018) Majumder, Sanjay; Smith, Benjamin D.; Music and Arts Technology, School of Engineering and Technology
    This paper presents a new system, based on genetic algorithms, to compose music pieces automatically based on analysis of the exemplar MIDI files. The aim of this project is to create a new music piece which is based on the information in the source pieces. This system extracts musical features from two MIDI files and automatically generates a new music piece using a genetic algorithm. The user specifies the length of the piece to create, and the weighting of musical features from each of the MIDI files to guide the generation. This system will provide the composer a new music piece based on two selected music pieces.