Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
Empirical methods for artificial intelligence
Empirical methods for artificial intelligence
On using syntactic constraints with genetic programming
Advances in genetic programming
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
Genetic Programming and Domain Knowledge: Beyond the Limitations of Grammar-Guided Machine Discovery
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
An analysis of genetic programming
An analysis of genetic programming
Evolving encapsulated programs as shared grammars
Genetic Programming and Evolvable Machines
Modelling Medical Time Series Using Grammar-Guided Genetic Programming
ICDM '08 Proceedings of the 8th industrial conference on Advances in Data Mining: Medical Applications, E-Commerce, Marketing, and Theoretical Aspects
Grammar based crossover operator in genetic programming
IWINAC'05 Proceedings of the First international work-conference on the Interplay Between Natural and Artificial Computation conference on Artificial Intelligence and Knowledge Engineering Applications: a bioinspired approach - Volume Part II
Comparison of the effectiveness of decimation and automatically defined functions
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
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Genetic Programming (GP) is a powerful software induction technique that has been recently applied for solving a wide variety of problems. Attempts to extend GP have focussed on applying type restrictions to the language to control genetic operators and to ensure that only valid programs are created. In this sense, the use of context free grammar (CFG) was proposed. This work studies the use of a CFG to define the structure of the initial population and direct crossover and mutation operators. Chameleon, a Grammar-Guided Genetic Programming system (GGGP) is also presented. On a suite of experiments composed of even-parity problems, the performance of Chameleon is compared to traditional GP. Furthermore, the automatic discovery of sub-functions, one of the most important research areas in GP, is also explored.We describe how to use ADFs with GGGP and, using Chameleon, we demonstrate that GGGP has similar results to Koza's Automatically Defined Functions (ADF) approach.