Extending Cartesian genetic programming : multi-expression genomes and applications in image processing and classification

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@PhdThesis{Cattani:thesis,

• author = "Philip Thomas Cattani",
• title = "Extending Cartesian genetic programming : multi-expression genomes and applications in image processing and classification",
• school = "University of Kent",
• year = "2014",
• address = "UK",
• keywords = "genetic algorithms, genetic programming, Cartesian genetic programming",
• URL = "https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.655651",
• abstract = "Genetic Programming (GP) is an Evolutionary Computation technique. Genetic Programming refers to a programming strategy where an artificial population of individuals represent solutions to a problem in the form of programs, and where an iterative process of selection and reproduction is used in order to evolve increasingly better solutions. This strategy is inspired by Charles Darwin theory of evolution through the mechanism of natural selection. Genetic Programming makes use of computational procedures analogous to some of the same biological processes which occur in natural evolution, namely, crossover, mutation, selection, and reproduction. Cartesian Genetic Programming (CGP) is a form of Genetic Programming that uses directed graphs to represent programs. It is called Cartesian, because this representation uses a grid of nodes that are addressed using a Cartesian co-ordinate system. This stands in contrast to GP systems which typically use a tree-based system to represent programs. In this thesis, we will show how it is possible to enhance and extend Cartesian Genetic Programming in two ways. Firstly, we show how CGP can be made to evolve programs which make use of image manipulation functions in order to create image manipulation programs. These programs can then be applied to image classification tasks as well as other image manipulation tasks such as segmentation, the creation of image filters, and transforming an input image in to a target image. Secondly, we show how the efficiency, the time it takes to solve a problem, of a CGP program can sometimes be increased by reinterpreting the semantics of a CGP genome string. We do this by applying Multi-Expression Programming to CGP.",
• notes = "ISNI: 0000 0004 5366 4011",

}

Genetic Programming entries for Philip T Cattani

Citations