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 85 for “"Genetic programming"”.
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Genetic programming and protocol configuration
… must identify the correct elements and ordering. Genetic programming is a machine learning technique method for generating computer programs automatically. It has been applied to many areas including electronic circuit design, image classification and machine code creation. However, it has never …
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Extensions to behavioral genetic programming
In this work I introduce genetic programming [5] as a general technique to produce programs with arbitrary behavior. I discuss genetic programming and its application the task of symbolic regression. I introduce behavioral genetic programming [6] as an extension to genetic programming and explore …
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Genetic Programming for Low-Resource Systems
… describe such a situation as an instance of programming the unprogrammable, and Genetic Programming is one solution method used for such problems. Genetic Programming, inspired by nature's ability to solve problems involving complex interactions and strong pressures on resource consumption, …
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Object oriented approach to genetic programming
… is the important issue in the research on genetic algorithms. In this thesis, we developed our own representation scheme, called "Object-Oriented Trucking" approach. Under the "Object-Oriented Trucking" approach each organism is encoded by a fixed-length string. Each entry in the string …
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Inverse Illumination Design with Genetic Programming
… interacting factors. This research applies genetic programming towards problems in illumination design. The Radiance system is used for performing accurate illumination simulations. Radiance accounts for a number of important environmental factors, which we exploit during fitness evaluation. …
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Characterizing function inlining with genetic programming
… itself. Using a form of machine learning called genetic programming, this thesis examines which factors are important in determining which function calls to inline to maximize performance. A number of different heuristics are generated for inlining decisions in the Trimaran compiler, which …
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DATA MINING AND IMAGE CLASSIFICATION USING GENETIC PROGRAMMING
<p>Genetic programming (GP), a capable machine learning and search method, motivated by Darwinian-evolution, is an evolutionary learning algorithm which automatically evolves computer programs in the form of trees to solve problems. This thesis studies the application of GP for data mining and …
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Genetic programming for the RoboCup Rescue Simulation System
… provides the first known attempt of applying Genetic Programming (GP) to the development of behaviours necessary to perform well in the RCRSS. Specifically, this thesis studies the suitability of GP to evolve the operational behaviours required of each type of rescue agent in the RCRSS. The …
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Applications of genetic programming to parallel system optimization
Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.
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Synthesis of Local Thermo-Physical Models Using Genetic Programming
… based on approximation of theoretical models. Genetic Programming (GP) system can extract knowledge from the given data in the form of symbolic expressions. This research describes a fully data driven automatic self-evolving algorithm that builds appropriate approximating formulae for local …
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Genetic programming applied to RFI mitigation in radio astronomy
Genetic Programming is a type of machine learning that employs a stochastic search of a solutions space, genetic operators, a fitness function, and multiple generations of evolved programs to resolve a user-defined task, such as the classification of data. At the time of this research, the …
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Using genetic programming to learn predictive models from spatio-temporal data
… by a novel technique called Spatio-Temporal Genetic Programming (STGP). STGP has been compared against the following methods: an Inductive Logic Programming system (Progol), Stochastic Logic Programs, Neural Networks, Bayesian Networks and C4.5, on learning the rules of card games, and …
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Investigating genetic programming with novelty and domain knowledge for program synthesis
… solve coding problems with the support of both programming and problem specfic knowledge. We attempt to transfer insights from such human expertise to genetic programming (GP) for solving automatic program synthesis. We draw upon manual and non-GP Artificial Intelligence methods to extract …
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Evolving circuits on a field programmable analog array using genetic programming
… describes the design and implementation of the Genetic Programming Intrinsic Circuit (GPIC) design system. Inspired by a number of recent advances in the field of Evolvable Hardware, the intended purpose of GPIC is to automate the design of analog circuits with minimal domain knowledge, …
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A Genetic Programming Approach to Solving Optimization Problems on Agent-Based Models
… models (ABM). The approach utilizes concepts in genetic programming (GP) and is demonstrated here using an optimization problem on the Sugarscape ABM, a prototype ABM that includes spatial heterogeneity, accumulation of agent resources, and agents with different attributes. The optimization …
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FlexGP 2.0 : multiple levels of parallelism in distributed machine learning via genetic programming
… support large, complex data. FlexGP 2.0's core genetic programming (GP) learner includes multithreaded C++ model evaluation and a multi-objective optimization algorithm which is extensible to pursue any number of objectives simultaneously in parallel. FlexGP 2.0 parallelizes the entire learner …
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