Genetic Programming for Low-Resource Systems

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@PhdThesis{White:thesis,
  author =       "David R. White",
  title =        "Genetic Programming for Low-Resource Systems",
  school =       "Department of Computer Science, University of York",
  year =         "2009",
  address =      "UK",
  month =        dec,
  keywords =     "genetic algorithms, genetic programming, SBSE,
                 Low-Resource Systems, Embedded Systems, Evolutionary
                 Computation",
  URL =          "http://etheses.whiterose.ac.uk/757/",
  URL =          "http://www-users.cs.york.ac.uk/~drw/papers/thesis/drwthesis.pdf",
  URL =          "http://ethos.bl.uk/OrderDetails.do?did=15&uin=uk.bl.ethos.516366",
  size =         "185 pages",
  abstract =     "Embedded systems dominate the computing landscape.
                 This dominance is increasing with the advent of
                 ubiquitous computing whereby lightweight, low-resource
                 systems are being deployed on a vast scale. These
                 systems present new engineering challenges: high-volume
                 production places a stronger emphasis on absolute cost,
                 resources available to executing software are highly
                 constrained, and physical manufacturing capabilities
                 approach hard limits. Add to this the sensitive nature
                 of many of these systems, such as smart cards used for
                 financial transactions, and the development of these
                 systems becomes a formidable engineering challenge.

                 For the software engineer, the incentive to produce
                 efficient and resource-aware software for these
                 platforms is great, yet existing tools do not support
                 them well in this task. It is difficult to assess the
                 impact of decisions made at the source code level in
                 terms of how they change a system's resource
                 consumption. Existing tool chains, together with the
                 very complex interactions of software and their host
                 processors, can produce unforeseen implications at
                 run-time of even small changes.

                 We could 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, is a clear candidate
                 for attacking the challenges presented in these
                 systems. Genetic Programming facilitates the creation
                 and manipulation of source code in a way that grants us
                 fine control over its measurable characteristics.

                 In this thesis, I investigate the potential of Genetic
                 Programming as a tool in controlling the non-functional
                 properties of software, as a new method of designing
                 code for low-resource systems. I demonstrate the
                 feasibility of this approach, and investigate some of
                 the ways Genetic Programming could be used by a
                 practitioner. In doing so, I also identify key
                 components that any application of Genetic Programming
                 to such a domain will require.

                 I review current low-resource system optimisation,
                 Genetic Programming and methods for simultaneously
                 handling multiple requirements. I present a series of
                 empirical investigations designed to provide evidence
                 for and against a set of hypotheses regarding the
                 success of Genetic Programming in solving problems
                 within the low-resource systems domain. These
                 experiments include the creation of new software, the
                 improvement of existing software and the fine-grained
                 control of resource usage in general.

                 To conclude, I review the progress made, reassess my
                 hypotheses, and outline how these new methods can be
                 carried forward to a wide range of applications.",
  notes =        "uk.bl.ethos.516366",
}

Genetic Programming entries for David Robert White

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