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 26 for “"Genetic Programming (GP)"”.
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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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Machine Learning Prediction of Shear Capacity of Steel Fiber Reinforced Concrete
… based on artificial neural network (ANN) and genetic programming (GP) to predict the shear strength of SFRC beams with great accuracy. Different statistical metrics were employed to assess the reliability of the proposed models. The suggested models have been benchmarked against various …
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An Investigation Into a Hybrid Genetic Programming and Ant Colony Optimization Method for Credit Scoring
… and investigates a new hybrid technique based on Genetic Programming (GP) and Ant Colony Optimization (ACO) techniques for inducing data classification rules. The proposed hybrid approach aims to improve on the accuracy of data classification rules produced by the original GP technique, which uses …
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Real-Time Automatic Object Classification and Tracking using Genetic Programming and NVIDIA R CUDA TM
Genetic Programming (GP) is a widely used methodology for solving various computational problems. GP's problem solving ability is usually hindered by its long execution times. In this thesis, GP is applied toward real-time computer vision. In particular, object classification and tracking using a …
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Neural Network Guided Evolution of L-system Plants
… generates graphic shapes using several rules. Genetic programming (GP) is an evolutionary algorithm that evolves expressions. A convolutional neural network(CNN) is a type of neural network which is useful for image recognition and classification. The goal of this thesis will be to generate …
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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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FlexGP 2.0 : multiple levels of parallelism in distributed machine learning via genetic programming
This thesis presents FlexGP 2.0, a distributed cloud-backed machine learning system. FlexGP 2.0 features multiple levels of parallelism which provide a significant improvement in accuracy v.s. elapsed time. The amount of computational resources in FlexGP 2.0 can be scaled along several dimensions …
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Behavioural synthesis of analogue integrated circuits
… Design Automation (EDA) in recent decades. Genetic Algorithms (GAs) are biologically inspired search algorithms which have previously shown some promise in this field. Their ability to form the basis of a practically useful synthesis system is investigated. A GA-based experimental synthesis …
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A Novel Hybrid Focused Crawling Algorithm to Build Domain-Specific Collections
… novel hybrid focused crawling framework based on Genetic Programming (GP) and meta-search. We showed that our novel hybrid framework can be applied to traditional focused crawlers to accurately find more relevant Web documents for the use of digital libraries and domain-specific search engines. …
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Machine Learning Based Prediction of Reinforced Concrete Members’ Shear Friction Capacity
… data. The second model,which is based on genetic programming (GP), for generating an analytical equation for estimating RC members' interface shear friction capacity. To achieve optimal accuracy, the hyper parameters of each model have been appropriately tuned. Several statistical metrics …
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Deep Learning Concepts for Evolutionary Art
… guide the automatic evolution of images. We use genetic programming (GP) to evolve procedural textures. We incorporate a pre-trained deep CNN model into the fitness. We are not performing any training, but rather, we pass a target image through the pre-trained deep CNN and use its the high-level …
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Intelligent Fusion of Evidence from Multiple Sources for Text Classification
… based on an inductive learning method -- Genetic Programming (GP) -- to fuse evidence from multiple sources. We show that good classification is possible with documents which are noisy or which have small amounts of text (e.g., short metadata records) -- if multiple sources of evidence are …
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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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Uncovering Efficient Learning and Initialisation Algorithms for Neural Networks Using Evolutionary Algorithms and Theoretical Analyses
… algorithms. One of the tools used to do this is Genetic Programming (GP): a form of program evolution. Very little research had been done on the use of GP to induce learning rules for ANNs. This thesis started from where others left and also developed a rigorous methodology for fairly comparing …
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Automatic generation of sound synthesis techniques
… done using evolutionary methods; specifically, Genetic Programming (GP). A custom language for representing and manipulating SSTs as topology graphs and expression trees is proposed, as well as the mapping rules between both representations. Fitness functions that use analytical and perceptual …
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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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Learning Strategies for Evolved Co-operating Multi-Agent Teams in Pursuit Domain
This study investigates how genetic programming (GP) can be effectively used in a multi-agent system to allow agents to learn to communicate. Using the predator-prey scenario and a co-operative learning strategy, communication protocols are compared as multiple predator agents learn the meaning of …
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Gene expression programming for Efficient Time-series Financial Forecasting
… extension to this technique is known as the genetic programming (GP) and gene expression programming (GEP) to explore and investigate the outcome of the GEP criteria on the stock market price prediction. The research presented in this thesis aims at the modelling and prediction of …
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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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Computation Approaches for Continuous Reinforcement Learning Problems
… of a specific branch of EC, that of Genetic Programming (GP). The evolving population in the GP case is comprised from individuals, which are immediately translated to mathematical functions, which can serve as a control law. The major contribution of this thesis is the proposed …
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