Theory grounded design of genetic programming and parallel evolutionary algorithms

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  author =       "Andrea Mambrini",
  title =        "Theory grounded design of genetic programming and
                 parallel evolutionary algorithms",
  school =       "School of Computer Science, University of Birmingham",
  year =         "2015",
  address =      "UK",
  month =        apr,
  keywords =     "genetic algorithms, genetic programming, Geometric
                 Semantic genetic programming",
  URL =          "",
  URL =          "",
  size =         "162 pages",
  abstract =     "Evolutionary algorithms (EAs) have been successfully
                 applied to many problems and applications. Their
                 success comes from being general purpose, which means
                 that the same EA can be used to solve different
                 problems. Despite that, many factors can affect the
                 behaviour and the performance of an EA and it has been
                 proven that there isn't a particular EA which can solve
                 efficiently any problem. This opens to the issue of
                 understanding how different design choices can affect
                 the performance of an EA and how to efficiently design
                 and tune one. This thesis has two main objectives. On
                 the one hand we will advance the theoretical
                 understanding of evolutionary algorithms, particularly
                 focusing on Genetic Programming and Parallel
                 Evolutionary algorithms. We will do that trying to
                 understand how different design choices affect the
                 performance of the algorithms and providing rigorously
                 proven bounds of the running time for different
                 designs. This novel knowledge, built upon previous work
                 on the theoretical foundation of EAs, will then help
                 for the second objective of the thesis, which is to
                 provide theory grounded design for Parallel
                 Evolutionary Algorithms and Genetic Programming. This
                 will consist in being inspired by the analysis of the
                 algorithms to produce provably good algorithm
  notes =        "ID Code: 5928

                 Supervisor: Xin Yao",

Genetic Programming entries for Andrea Mambrini