Genetic Algorithms Plus Data Structures Equals Evolution Programs
Genetic Algorithms Plus Data Structures Equals Evolution Programs
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Parallel Evolutionary Optimisation with Constraint Propagation
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Evolutionary Parsing for a Probabilistic Context Free Grammar
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
A hybrid evolutionary approach for solving constrained optimization problems over finite domains
IEEE Transactions on Evolutionary Computation
Stochastic Parsing and Evolutionary Algorithms
Applied Artificial Intelligence
GRAEL: an agent-based evolutionary computing approach for natural language grammar development
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Studying the advantages of a messy evolutionary algorithm for natural language tagging
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Evolutionary computing as a tool for grammar development
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
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This paper presents a parallel evolutionary program for natural language parsing. The implementation follows an island model, in which, after a number of generations, demes exchange some individuals in a round-robin manner. The population is composed of potential parsings for a sentence, and the fitness function evaluates the appropriateness of the parsing according to a given stochastic grammar. Both the fitness function and the genetic operators, which require that the result of their application still corresponds to the words in the input sentence, are expensive enough to make the evolutionary program appropriate for a coarse grain parallel model and its distributed implementation. The system has been implemented in a parallel machine using the PVM (Parallel Virtual Machine) software. The paper describes the study of the parameters in the parallel evolutionary program, such as the number of individuals to be exchanged between demes, and the number of generations between exchanges. Different parameters of the algorithm, such as population size, and crossover and mutation rates, have also been tested.