An efficient evolutionary algorithm for solving incrementally structured problems

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Jason Ansel and Maciej Pacula and 
                 Saman Amarasinghe and Una-May O'Reilly",
  title =        "An efficient evolutionary algorithm for solving
                 incrementally structured problems",
  booktitle =    "GECCO '11: Proceedings of the 13th annual conference
                 on Genetic and evolutionary computation",
  year =         "2011",
  editor =       "Natalio Krasnogor and Pier Luca Lanzi and 
                 Andries Engelbrecht and David Pelta and Carlos Gershenson and 
                 Giovanni Squillero and Alex Freitas and 
                 Marylyn Ritchie and Mike Preuss and Christian Gagne and 
                 Yew Soon Ong and Guenther Raidl and Marcus Gallager and 
                 Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and 
                 Nikolaus Hansen and Silja Meyer-Nieberg and 
                 Jim Smith and Gus Eiben and Ester Bernado-Mansilla and 
                 Will Browne and Lee Spector and Tina Yu and Jeff Clune and 
                 Greg Hornby and Man-Leung Wong and Pierre Collet and 
                 Steve Gustafson and Jean-Paul Watson and 
                 Moshe Sipper and Simon Poulding and Gabriela Ochoa and 
                 Marc Schoenauer and Carsten Witt and Anne Auger",
  isbn13 =       "978-1-4503-0557-0",
  pages =        "1699--1706",
  keywords =     "genetic algorithms, genetic programming, SBSE, Real
                 world applications",
  month =        "12-16 " # jul,
  organisation = "SIGEVO",
  address =      "Dublin, Ireland",
  DOI =          "doi:10.1145/2001576.2001805",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Many real world problems have a structure where small
                 problem instances are embedded within large problem
                 instances, or where solution quality for large problem
                 instances is loosely correlated to that of small
                 problem instances. This structure can be exploited
                 because smaller problem instances typically have
                 smaller search spaces and are cheaper to evaluate. We
                 present an evolutionary algorithm, INCREA, which is
                 designed to incrementally solve a large, noisy,
                 computationally expensive problem by deriving its
                 initial population through recursively running itself
                 on problem instances of smaller sizes. The INCREA
                 algorithm also expands and shrinks its population each
                 generation and cuts off work that doesn't appear to
                 promise a fruitful result. For further efficiency, it
                 addresses noisy solution quality efficiently by
                 focusing on resolving it for small, potentially
                 reusable solutions which have a much lower cost of
                 evaluation. We compare INCREA to a general purpose
                 evolutionary algorithm and find that in most cases
                 INCREA arrives at the same solution in significantly
                 less time.",
  notes =        "Research compiler petabricks. GPEA. Aim: autotuning
                 for computer when program when is actually installed on
                 that computer.

                 Looks at recursive sort and which chooses one of 4
                 types of sort (Insertion sort, quick sort, radix sort
                 and a dummy) to use at each level of recursion. Noisy
                 fitness evaluation (run for real, not simulation). uses
                 T-test (trying to be too fair?). Examples: sort, matrix
                 multiply (matmult) and eig (symmetric eigen

                 Also known as \cite{2001805} GECCO-2011 A joint meeting
                 of the twentieth international conference on genetic
                 algorithms (ICGA-2011) and the sixteenth annual genetic
                 programming conference (GP-2011)",

Genetic Programming entries for Jason Ansel Maciej Pacula Saman Amarasinghe Una-May O'Reilly