Reducing bias and inefficiency in the selection algorithm
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Efficient and Accurate Parallel Genetic Algorithms
Efficient and Accurate Parallel Genetic Algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Adaptive Selection Methods for Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Representational Issues for Context Free Grammar Induction Using Genetic Algorithms
ICGI '94 Proceedings of the Second International Colloquium on Grammatical Inference and Applications
Introduction to Genetic Algorithms
Introduction to Genetic Algorithms
A bibliographical study of grammatical inference
Pattern Recognition
Evolutionary computing as a tool for grammar development
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Schema processing under proportional selection in the presence ofrandom effects
IEEE Transactions on Evolutionary Computation
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This paper discusses a case study of grammar induction. Grammar induction is the process of learning grammar from a set of training data of the positive (S+) and negative (S-) strings. An algorithm has been designed and implemented for the induction of context free grammar (CFG). Special bit mask oriented data structures have been used to apply the crossover and mutation operations. The aim is to establish the applicability of the genetic algorithms (GAs) for different engineering problems. The paper lays a concrete foundation to formulating problems in the genetic algorithm framework. In addition, the basic principles of standard genetic algorithm, such as encoding techniques, selection techniques, operators (crossover and mutation), and the issues raised in the relevant literature have been discussed to establish the applicability of the genetic algorithm.