Grammar Based Genetic Programming Using Linear Representations


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In recent years, there has been a great interest in genetic programming (GP), which is used to solve many applications such as data mining, electronic engineering and pattern recognition etc.. Genetic programming paradigm as a form of adaptive learning is a functional approach to many problems that require a non-fixed representation and GP typically operates on a population of parse trees which usually represent computer programs whose nodes have single data type. In this paper GP using context-free grammars (CFGs) is described. This technique separates search space from solution space through a genotype to phenotype mapping. The genotypes and phenotypes of the individuals both act on different linear representations. A phenotype is postfix expression, a new method of representing which is described by making use of the definition and related features of a context-free grammar, i.e. a genotype is a variable length, linear valid genome determined by a simplified derivation tree (SDT) generated from a context-free grammar. A CFG is used to specify how the possible solutions are created according to experiential knowledge and to direct legal crossover (or mutation) operations without any explicit reference to the process of program generation and parsing, and automatically ensuring typing and syntax correctness. Some related definitions involving genetic operators are described. Fitness evaluation is given. This technique is applied to a symbol regression problem-the identification of nonlinear dynamic characteristics of cushioning packaging. Experimental results show this method can find good relations between variables and is better than basic GP without a grammar. Future research on it is outlined.
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Key words
genetic programming, context-free grammar, parse tree, derivation tree, linear representation
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