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: a paradigm for genetically breeding populations of computer programs to solve problems
Genetic Programming IV: Routine Human-Competitive Machine Intelligence
Genetic Programming IV: Routine Human-Competitive Machine Intelligence
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
Evolving computer programs without subtree crossover
IEEE Transactions on Evolutionary Computation
Two fast tree-creation algorithms for genetic programming
IEEE Transactions on Evolutionary Computation
Rule Evolving System for Knee Lesion Prognosis from Medical Isokinetic Curves
IWINAC '09 Proceedings of the 3rd International Work-Conference on The Interplay Between Natural and Artificial Computation: Part II: Bioinspired Applications in Artificial and Natural Computation
A survey and taxonomy of performance improvement of canonical genetic programming
Knowledge and Information Systems
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Evolutionary construction and adaptation of intelligent systems
Expert Systems with Applications: An International Journal
Computer Methods and Programs in Biomedicine
A card game description language
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
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This paper proposes a new tree-generation algorithm for grammar-guided genetic programming that includes a parameter to control the maximum size of the trees to be generated. An important feature of this algorithm is that the initial populations generated are adequately distributed in terms of tree size and distribution within the search space. Consequently, genetic programming systems starting from the initial populations generated by the proposed method have a higher convergence speed. Two different problems have been chosen to carry out the experiments: a laboratory test involving searching for arithmetical equalities and the real-world task of breast cancer prognosis. In both problems, comparisons have been made to another five important initialization methods.