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Title: Analysis and synthesis of broadband traveling wave antennas
Creator: Zhao, Lanwu
Date: 2004
Collection: Electronic Theses & Dissertations

Title: Achieving consistent evolution across isometrically equivalent search spaces
Creator: Patton, Arnold L.
Date: 2004
Collection: Electronic Theses & Dissertations

Title: Online innovization : towards knowledge discovery and achieving faster convergence in multi-objective optimization
Creator: Gaur, Abhinav
Date: 2020
Collection: Electronic Theses & Dissertations
Description: Ì0300nnovization'' is a task of learning common principles thatexist among some or all of the Pareto-optimal solutions in amulti-objective optimization problem. Except a few earlierstudies, most innovization related studies were performed onthe final non-dominated solutions found by an evolutionary multi-objective algorithm eithermanually or by using a machine learning method.Recent studies have shown that these principles can be learnedduring intermediate iterations of an optimization run...
Show moreÌ0300nnovization'' is a task of learning common principles thatexist among some or all of the Pareto-optimal solutions in amulti-objective optimization problem. Except a few earlierstudies, most innovization related studies were performed onthe final non-dominated solutions found by an evolutionary multi-objective algorithm eithermanually or by using a machine learning method.Recent studies have shown that these principles can be learnedduring intermediate iterations of an optimization run and simultaneously utilized in thesame optimization run to repair variables to achieve a fasterconvergence to the Pareto-optimal set. This is what we are calling as ò0300nline innovization'' as it is performed online during the run of an evolutionary multi-objective optimization algorithm. Special attention is paid to learning rules that are easier to interpret, such as short algebraic expressions, instead of complex decision trees or kernel based black box rules.We begin by showing how to learn fixed form rules that are encountered frequently in multi-objective optimization problems. We also show how can we learn free form rules, that are linear combination of non-linear terms, using a custom genetic programming algorithm. We show how can we use the concept of k0300nee' in PO set of solutions along with a custom dimensional penalty calculator to discard rules that may be overly complex, or inaccurate or just dimensionally incorrect. The results of rules learned using this custom genetic programming algorithm show that it is beneficial to let evolution learn the structure of rules while the constituent weights should be learned using some classical learning algorithm such as linear regression or linear support vector machines. When the rules are implicit functions of the problem variables, we use a computationally inexpensive way of repairing the variables by turning the problem of repairing the variable into a single variable golden section search.We show the proof of concept on test problems by learning fixed form rules among variables of the problem, which we then use during the same optimization run to repair variables. Different principleslearned during an optimization run can involve differentnumber of variables and/or variables that arecommon among a number of principles. Moreover, a preferenceorder for repairing variables may play an important role forproper convergence. Thus, when multiple principles exist, itis important to use a strategy that is most beneficial forrepairing evolving population of solutions.The above methods are applied to a mix of test problems and engineering design problems. The results are encouraging and strongly supportsthe use of innovization task in enhancing the convergence of an evolutionary multi-objective optimization algorithms. Moreover, the custom genetic program developed in this work can be a useful machine learning tool for practitioners to learn human interpretable rules in the form of algebraic expressions.
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Title: Balancing convergence and diversity in evolutionary single, multi and many objectives
Creator: Seada, Haitham
Date: 2017
Collection: Electronic Theses & Dissertations
Description: "Single objective optimization targets only one solution, that is usually the global optimum. On the other hand, the goal of multiobjective optimization is to represent the whole set of trade-off Pareto-optimal solutions to a problem. For over thirty years, researchers have been developing Evolutionary Multiobjective Optimization (EMO) algorithms for solving multiobjective optimization problems. Unfortunately, each of these algorithms were found to work well on a specific range of objective...
Show more"Single objective optimization targets only one solution, that is usually the global optimum. On the other hand, the goal of multiobjective optimization is to represent the whole set of trade-off Pareto-optimal solutions to a problem. For over thirty years, researchers have been developing Evolutionary Multiobjective Optimization (EMO) algorithms for solving multiobjective optimization problems. Unfortunately, each of these algorithms were found to work well on a specific range of objective dimensionality, i.e. number of objectives. Most researchers overlooked the idea of creating a cross-dimensional algorithm that can adapt its operation from one level of objective dimensionality to the other. One important aspect of creating such algorithm is achieving a careful balance between convergence and diversity. Researchers proposed several techniques aiming at dividing computational resources uniformly between these two goals. However, in many situations, only either of them is difficult to attain. Also for a new problem, it is difficult to tell beforehand if it will be challenging in terms of convergence, diversity or both. In this study, we propose several extensions to a state-of-the-art evolutionary many-objective optimization algorithm - NSGA-III. Our extensions collectively aim at (i) creating a unified optimization algorithm that dynamically adapts itself to single, multi- and many objectives, and (ii) enabling this algorithm to automatically focus on either convergence, diversity or both, according to the problem being considered. Our approach augments the already existing algorithm with a niching-based selection operator. It also utilizes the recently proposed Karush Kuhn Tucker Proximity Measure to identify ill-converged solutions, and finally, uses several combinations of point-to-point single objective local search procedures to remedy these solutions and enhance both convergence and diversity. Our extensions are shown to produce better results than state-of-the-art algorithms over a set of single, multi- and many-objective problems."--Pages ii-iii.
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Title: Topology optimization using genetic algorithms with superelement domain discretization
Creator: Myers, Eric Christopher
Date: 2000
Collection: Electronic Theses & Dissertations

Title: Hybrid structural and behavioral diversity techniques for effective genetic programming
Creator: Burks, Armand Rashad
Date: 2017
Collection: Electronic Theses & Dissertations
Description: "Sustaining the diversity of evolving populations is a fundamental issue in genetic programming. We describe a novel measure of structural diversity for tree-based genetic programming, and we demonstrate its utility compared to other diversity techniques. We demonstrate our technique on the real-world application of tuberculosis screening from X-ray images. We then introduce a new paradigm of genetic programming that involves simultaneously maintaining structural and behavioral diversity in...
Show more"Sustaining the diversity of evolving populations is a fundamental issue in genetic programming. We describe a novel measure of structural diversity for tree-based genetic programming, and we demonstrate its utility compared to other diversity techniques. We demonstrate our technique on the real-world application of tuberculosis screening from X-ray images. We then introduce a new paradigm of genetic programming that involves simultaneously maintaining structural and behavioral diversity in order to further improve the efficiency of genetic programming. Our results show that simultaneously promoting structural and behavioral diversity improves genetic programming by leveraging the benefits of both aspects of diversity while overcoming the shortcomings of either technique in isolation. The hybridization increases the behavioral diversity of our structural diversity technique, and increases the structural diversity of the behavioral diversity techniques. This increased diversity leads to performance gains compared to either technique in isolation. We found that in many cases, our structural diversity technique provides significant performance improvement compared to other state-of-the-art techniques. Our results from the experiments comparing the hybrid techniques indicate that the largest performance gain was typically attributed to our structural diversity technique. The incorporation of the behavioral diversity techniques provide additional improvement in many cases."--Page ii.
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Title: On prediction and detection of epileptic seizures by means of genetic programming artificial features
Creator: Firpi, Hiram A.
Date: 2005
Collection: Electronic Theses & Dissertations

Title: An adaptive representation for a genetic algorithm in solving flexible job-shop scheduling and rescheduling problems
Creator: Unachak, Prakarn
Date: 2010
Collection: Electronic Theses & Dissertations

Title: A self tuning electromagnetic shutter
Creator: Ouedraogo, Raoul Ouatagom
Date: 2008
Collection: Electronic Theses & Dissertations

Title: Use of injection island genetic algorithms in the optimization of composite flywheels
Creator: Eby, David James
Date: 1997
Collection: Electronic Theses & Dissertations

Title: Development of dynamic real-time integration of transit signal priority in coordinated traffic signal control system using genetic algorithms and artificial neural networks
Creator: Ghanim, Mohammad Shareef
Date: 2008
Collection: Electronic Theses & Dissertations

Title: Nesting of irregular shapes using a parallel genetic algorithm and feature matching
Creator: Uday, Anand
Date: 2001
Collection: Electronic Theses & Dissertations

Title: Genetic algorithm optimized feature extraction and selection for ECG pattern classification
Creator: Huang, Zhijian
Date: 2002
Collection: Electronic Theses & Dissertations

Title: A comparison of cohort GA with canonical serial and island-model distributed GA's
Creator: Pei, Huafeng
Date: 2000
Collection: Electronic Theses & Dissertations

Title: Sustainable evolutionary algorithms and scalable evolutionary synthesis of dynamic systems
Creator: Hu, Jianjun
Date: 2004
Collection: Electronic Theses & Dissertations

Title: Molecular conformation of clusters by genetic algorithm using spatial operators and unlabeled distance data
Creator: Cherba, David M.
Date: 2005
Collection: Electronic Theses & Dissertations

Title: Evolving artificial neural networks with generative encodings inspired by developmental biology
Creator: Clune, Jeff
Date: 2010
Collection: Electronic Theses & Dissertations

Title: Modeling genetic algorithm dynamics for OneMax and deceptive functions
Creator: Buyukbozkirli, Bulent
Date: 2004
Collection: Electronic Theses & Dissertations

Title: Natural niching : applying ecological principles to evolutionary computation
Creator: Goings, Sherri
Date: 2010
Collection: Electronic Theses & Dissertations

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