Grammatical Evolution Enhancing Simulated Annealing for the Load Balancing Problem in Cloud Computing

Created by W.Langdon from gp-bibliography.bib Revision:1.4202

  author =       "Nasser R. Sabar and Andy Song",
  title =        "Grammatical Evolution Enhancing Simulated Annealing
                 for the Load Balancing Problem in Cloud Computing",
  booktitle =    "GECCO '16: Proceedings of the 2016 Annual Conference
                 on Genetic and Evolutionary Computation",
  year =         "2016",
  editor =       "Tobias Friedrich",
  pages =        "997--1003",
  keywords =     "genetic algorithms, genetic programming, grammatical
  month =        "20-24 " # jul,
  organisation = "SIGEVO",
  address =      "Denver, USA",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  isbn13 =       "978-1-4503-4206-3",
  DOI =          "doi:10.1145/2908812.2908861",
  abstract =     "Load balancing (LB) is crucial in the field of cloud
                 computing. LB is to find the optimum allocation of
                 services onto a set of machines so the machine usage
                 can be maximised. This paper proposes a new method for
                 LB, simulated annealing (SA) enhanced by grammatical
                 evolution (GE). SA is a well-known stochastic
                 optimisation algorithm that has good performance on a
                 range of problems including loading balancing. However
                 the success of SA often relies on a key parameter known
                 as the cooling schedule and the type of the
                 neighbourhood structure. Both the parameter and the
                 structure of SA are problem specific. They need to be
                 manually adjusted to fit the problem in hand. In
                 addition different stages of the search process may
                 have different optimal parameter values. To address
                 these issues, a grammar evolution approach is
                 introduced to adaptively evolve the cooling schedule
                 parameter and neighbourhood structures. The proposed
                 method can adjust SA parameter and structure based on
                 the landscape of the current search state so high
                 quality solutions can be found more quickly. The
                 effectiveness of the proposed GE method is demonstrated
                 on the Google machine reassignment problem, which is a
                 typical LB problem, proposed for the ROADEF/EURO 2012
                 challenge. Experimental results show that our GE
                 enhanced SA is highly competitive compared to
                 state-of-the-art algorithms.",
  notes =        "RMIT University Australia

                 GECCO-2016 A Recombination of the 25th International
                 Conference on Genetic Algorithms (ICGA-2016) and the
                 21st Annual Genetic Programming Conference (GP-2016)",

Genetic Programming entries for Nasser R Sabar Andy Song