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 433 for “"genetic algorithms."”.
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Genetic algorithms using Galib
GAlib is a C++ library of genetic algorithm objects that was recently developed at the Massachusetts Institute of Technology. This thesis is to demonstrate its functionality and versatility for implementing haploid tripartite genetic algorithms; We first built a test bed in which GAlib could be …
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Genetic algorithms with implicit memory
This thesis investigates the workings of genetic algorithms in dynamic optimisation problems where fitness landscapes materialise that are identical to, or resemble in some way, landscapes previously encountered. The objective is to appraise the performances of the various approaches offered by the …
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Solver Tuning with Genetic Algorithms
… accessible technology. Two types of tuning algorithms are discussed in this thesis: single instance tuning algorithms and instance-based tuning algorithms. A standard genetic based algorithm and a sexual genetic based algorithm are proposed and implemented to deal with the single instance …
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Niching methods for genetic algorithms
Niching methods extend genetic algorithms to domains that require the location and maintenance of multiple solutions. Such domains include classification and machine learning, multimodal function optimization, multiobjective function optimization, and simulation of complex and adaptive systems.
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Repetitive project scheduling using genetic algorithms
… with an evolutionary optimization technique. The Genetic Algorithm is used to search for the best schedule by varying the crew size and work continuity requirements of the project activities. The schedules which have lower work continuity and do not meet deadlines are penalized.
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Weighted Graph Compression using Genetic Algorithms
… the approaches to compress weighted graphs via genetic algorithms and analyse the compression from an epidemic point of view. It is seen that edge weights provide vital information for graph compression. Not only this, but having meaningful edge weights is important as different weights can lead …
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Noise, Sampling, and Efficient Genetic Algorithms
As genetic algorithms (GA) move into industry, a thorough understanding of how GAs are affected by noise is becoming increasingly important. Noise affects a GA's population sizing requirements, performance characteristics, and computational requirements. This research develops quantitative models …
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Genetic algorithms for uncapacitated network design
Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1995.
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Setting language parameters using genetic algorithms
Thesis (B.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1992.
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Helical Antenna Optimization Using Genetic Algorithms
The genetic algorithm (GA) is used to design helical antennas that provide a significantly larger bandwidth than conventional helices with the same size. Over the bandwidth of operation, the GA-optimized helix offers considerably smaller axial-ratio and slightly higher gain than the conventional …
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Creative Design Using Collaborative Interactive Genetic Algorithms
… design based on collaborative interactive genetic algorithms. My model enhances creativity in the conceptual design phase by allowing designers to guide genetic algorithms in order to breed new design ideas quickly, and by supporting team collaboration through the sharing of solutions among …
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Genetic Algorithms, Orthogonal Arrays and Diploid Chromosomes
… the principles of Darwinian natural selection, genetic algorithms are a well established search and optimisation technique. They can be said to 'evolve' solutions to complex problems. However, genetic algorithms can be slow to use, as they require a lot of computer resources to function. Given …
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The truckin' project : experimenting with genetic algorithms
… fitter population. In this thesis the basics of genetic algorithms are explained and then the Truckin' project is described in detail. Finally the results and current status of the project are outlined.
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Representation and parameterisation issues in genetic algorithms
… in a number of areas including representation, genetic operators, their parameter rates and real world multi-dimensional applications. A series of experiments were conducted, comparing the performance of the Multi-GA to a traditional GA on a number of recognised and increasingly complex test …
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Active Control of Structures Using Genetic Algorithms
… developed three major design aspects: a genetic algorithm-based control parameter optimization based on the real design criteria, a state space reconstruction technique for estimating the full state of a structure using accelerations measured from sensors installed on the structure, and …
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Designing Efficient and Accurate Parallel Genetic Algorithms
Parallel implementations of genetic algorithms (GAs) are common, and, in most cases, they succeed to reduce the time required to find acceptable solutions. However, the effect of the parameters of parallel GAs on the quality of their search and on their efficiency are not well understood. This …
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Optimization of composite structures by genetic algorithms
… may benefit by knowing many of those designs. Genetic algorithms are well suited for laminate design because they can handle the combinatorial nature of the problem and they permit the designer to obtain many near-optimal designs. However, their computational cost is high for most structural …
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Fuzzy Logic, Genetic Algorithms and Next-State Models
… from JAM process. The conclusion is that the Genetic Algorithm works for selecting optimal Fuzzy Logic network but the Fuzzy Logic modeling method may not be an appropriate approach to create the next state models.
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Optimization of Active Rendezvous Trajectories by Genetic Algorithms
… One of the methods to solve such problems is the Genetic Algorithm (GA) method. In this work, a GA has been developed using Matlab®. It treats possible solutions to the studied problems as individuals and eventually converges to an optimal or near optimal solution. Genetic Algorithms have been …
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