Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 172 for “"Evolutionary Algorithms"”.
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Evolutionary algorithms for scheduling operations
… are becoming increasingly popular, specifically Evolutionary Algorithms (EAs). However, there are three key limitations of previous studies researching application of EAs for the solution of the scheduling problems. First of all, there is no justification for the selection of a particular genetic …
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The art of parameterless evolutionary algorithms
"Evolutionary Algorithms (EAs) can be powerful problem solving tools when other methods fail. EAs maintain a collection of solutions and go through many iterations of recombining and randomly modifying current solutions, deleting some of the discovered solutions and giving the better solutions a …
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Continuous dynamic optimisation using evolutionary algorithms
Evolutionary dynamic optimisation (EDO), or the study of applying evolutionary algorithms to dynamic optimisation problems (DOPs) is the focus of this thesis. Based on two comprehensive literature reviews on existing academic EDO research and real-world DOPs, this thesis for the first time …
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Mining Aircraft Telemetry Data With Evolutionary Algorithms
… (GKA) and Expectation-</p> <p>Maximization (EM) algorithms.</p> <p>The results obtained from the subpath discovery and data mining suggest a pilot</p> <p>flying a GA aircraft near to an uncontrolled airport will perform different maneuvers than</p> <p>a pilot flying a GA aircraft far from an …
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Decomposition Evolutionary Algorithms for Noisy Multiobjective Optimization
… on computer experiments with meta-heuristic algorithms. Most of these meta-heuristics implement some sort of stochastic search method, amongst which the 'Evolutionary Algorithm' is garnering much attention. It possesses several characteristics that make it a desirable method for confronting …
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Multi-objective evolutionary algorithms for data clustering
… of different Multi-Objective clustering algorithms. In the context of clustering, a robust measure for evaluating clustering solutions is an important component of an algorithm. These Cluster Quality Measures (CQMs) should rely solely on the structure of the clustering solution. A robust …
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Multi-objective evolutionary algorithms for product design
… Chemistry and Machine Learning (ML) with Evolutionary MultiObjective Optimisation (EMOO) techniques to streamline compound design. This approach automates the design process by leveraging ML to accurately predict compound properties and using EMOO to select compounds that meet various …
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Evolutionary algorithms for compiler-enabled program autotuning
… INCREA, based on a novel bottom-up evolutionary algorithm, optimizes programs offline at compile time. SiblingRivalry improves on INCREA by optimizing online during a program's execution, dynamically adapting to changes in hardware and the operating system. Continuous adaptation is …
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Evolutionary Algorithms for Resource Allocation in Smart Grid
… proper energy management strategies and applying evolutionary algorithms in smart grid resource allocation and management. With the massive literature review and analysis, the research mainly focuses on the linkage management of microgrids and electric vehicles, and three specific management …
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Network Theoretic Analyses and Enhancements of Evolutionary Algorithms
<p>Information in evolutionary algorithms is available at multiple levels; however most analyses focus on the individual level. This dissertation extracts useful information from networks and communities formed by examining interrelationships between individuals in the populations as they change …
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Development of Construction Projects Scheduling with Evolutionary Algorithms
Evolutionary Algorithms (EAs) as appropriate tools to optimize multi-objective problems have been applied to optimize construction projects in the last two decades. However, studies on improving the convergence ratio and processing time in the most applied algorithms such as Genetic Algorithm (GA), …
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Design of power system stabilizers using evolutionary algorithms
Over the past decades, the issue of low frequency oscillations has been of major concern to power system engineers. These oscillations range from 0.1 to 3Hz and tend to be poorly damped especially in systems equipped with high gain fast acting AVRs and highly interconnected networks. If these …
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Multi-Objective evolutionary algorithms for vehicle routing problems
… techniques have been considered, among which, evolutionary methods have proved to be suitable for solving the problem. Despite this method being able to provide a set of solutions that represent the trade-offs between multiple objectives, very few studies have concentrated on the optimisation …
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Evolutionary algorithms for optimising reinforcement learning policy approximation
… traditional parameter optimization methods, an evolutionary approach similar to that taken by Dufourq and Bassett (2017) for image classification tasks was used to find the optimal model architecture when training an agent that learns to play Atari Pong. The approach found models that were able …
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Design problem optimization with multi-objective evolutionary algorithms
… and comparing two advanced multi-objective evolutionary algorithms—Multi-Objective Covariance Matrix Adaptation Evolution Strategy (MOCMA-ES) and the Non-Dominated Sorting Genetic Algorithm II (NSGA-II)—to optimize sunshades across five key objectives: thermal comfort, energy consumption, …
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Learning image enhancement and object localization using evolutionary algorithms
… learning techniques, we can develop e ective algorithms which are image and task oriented. Learning image processing comprises two main phases, namely: training and testing phases. During training phase, the algorithm has the capability of discovering and adjusting an optimum transformation …
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Real time evolutionary algorithms in robotic neural control systems.
This thesis describes the use of a Real-Time Evolutionary Algorithm (RTEA) to optimise an Artificial Neural Network (ANN) on-line (in this context on-line means while it is in use). Traditionally, Evolutionary Algorithms (Genetic Algorithms, Evolutionary Strategies and Evolutionary Programming) …
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Analog circuit optimization using evolutionary algorithms and convex optimization
In this thesis, we analyze state-of-art techniques for analog circuit sizing and compare them on various metrics. We ascertain that a methodology which improves the accuracy of sizing without increasing the run time or the designer effort is a contribution. We argue that the accuracy of geometric …
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Walsh analysis, epistasis, and optimization problem difficulty for evolutionary algorithms
… Epistasis is related to problem difficulty for evolutionary algorithms. Walsh analysis can be used to quantify epistasis. In this dissertation Walsh analysis is developed in detail starting with the definitions for the Walsh transform. The epistasis of a polynomial is shown to be bounded by the …
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