Evolving Distributed Algorithms with Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@Article{Weise:2011:ieeeTEC,

• author = "Thomas Weise and Ke Tang",
• title = "Evolving Distributed Algorithms with Genetic Programming",
• journal = "IEEE Transactions on Evolutionary Computation",
• year = "2012",
• volume = "16",
• number = "2",
• pages = "242--265",
• month = apr,
• keywords = "genetic algorithms, genetic programming, Algorithm design and analysis, Approximation algorithms, Computational modelling, Distributed algorithms, Optimisation, Protocols, Critical section, GCD, LGP, SGP, distributed algorithms, election, distributed greatest common divisor DGCD, fraglets, mutual exclusion, rule-based genetic programming, SBSE",
• ISSN = "1089-778X",
• DOI = "doi:10.1109/TEVC.2011.2112666",
• size = "24 pages",
• abstract = "In this paper, we evaluate the applicability of genetic programming (GP) for the evolution of distributed algorithms. We carry out a large-scale experimental study in which we tackle three well-known problems from distributed computing with six different program representations. For this purpose, we first define a simulation environment in which phenomena such as asynchronous computation at changing speed and messages taking over each other, i.e., out-of-order message delivery, occur with high probability. Second, we define extensions and adaptations of established GP approaches (such as tree-based and linear GP) in order to make them suitable for representing distributed algorithms. Third, we introduce novel rule-based GP methods designed especially with the characteristic difficulties of evolving algorithms (such as epistasis) in mind. Based on our extensive experimental study of these approaches, we conclude that GP is indeed a viable method for evolving non-trivial, deterministic, non-approximative distributed algorithms. Furthermore, one of the two rule-based approaches is shown to exhibit superior performance in most of the tasks and thus can be considered as an interesting idea also for other problem domains.",
• notes = "Indexed memory, scope, MOGP, Pareto (size v fitness not just for anti bloat reasons), GCD, Election (distributed smallest), mutual exclusion-distributed locking, LCS, rule-based genetic programming RBGP, eRBGP, Turing complete, tree GP, linear GP LGP, ADF, for loop, while loop, jmp, call, fraglets \cite{conf/wac/YamamotoT05}, interrupt service routine, various network connections. Statements like evolved 'algorithm is not correct' section VI-B which seem at odds with rest of text. Population 512, 700+ generation, homologous multipoint crossover, subtree crossover, 4 types of mutation. comparison with random walk. GP run 8 minutes. marginal fairness CSMA. Also known as \cite{6026925}",

}

Genetic Programming entries for Thomas Weise Ke Tang

Citations