A closed asynchronous dynamic model of cellular learning automata and its application to peer-to-peer networks

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  author =       "Ali Mohammad Saghiri and Mohammad Reza Meybodi",
  title =        "A closed asynchronous dynamic model of cellular
                 learning automata and its application to peer-to-peer
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2017",
  volume =       "18",
  number =       "3",
  pages =        "313-–349",
  month =        sep,
  keywords =     "genetic algorithms, genetic programming, Cellular
                 learning automata, Dynamic cellular learning automata,
                 Peer-to-peer networks, Landmark clustering algorithm",
  ISSN =         "1389-2576",
  DOI =          "doi:10.1007/s10710-017-9299-7",
  abstract =     "Cellular Learning Automata (CLAs) are hybrid models
                 obtained from combination of Cellular Automata (CAs)
                 and Learning Automata (LAs). These models can be either
                 open or closed. In closed CLAs, the states of
                 neighboring cells of each cell called local environment
                 affect on the action selection process of the LA of
                 that cell whereas in open CLAs, each cell, in addition
                 to its local environment has an exclusive environment
                 which is observed by the cell only and the global
                 environment which can be observed by all the cells in
                 CLA. In dynamic models of CLAs, one of their aspects
                 such as structure, local rule or neighborhood radius
                 may change during the evolution of the CLA. CLAs can
                 also be classified as synchronous CLAs or asynchronous
                 CLAs. In a synchronous CLA, all LAs in different cells
                 are activated synchronously whereas in an asynchronous
                 CLA, the LAs in different cells are activated
                 asynchronously. In this paper, a new closed
                 asynchronous dynamic model of CLA whose structure and
                 the number of LAs in each cell may vary with time has
                 been introduced. To show the potential of the proposed
                 model, a landmark clustering algorithm for solving
                 topology mismatch problem in unstructured peer-to-peer
                 networks has been proposed. To evaluate the proposed
                 algorithm, computer simulations have been conducted and
                 then the results are compared with the results obtained
                 for two existing algorithms for solving topology
                 mismatch problem. It has been shown that the proposed
                 algorithm is superior to the existing algorithms with
                 respect to communication delay and average round-trip
                 time between peers within clusters.",

Genetic Programming entries for Ali Mohammad Saghiri Mohammad Reza Meybodi