Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Size Fair and Homologous Tree Crossovers for Tree Genetic Programming
Genetic Programming and Evolvable Machines
Grammar-Guided Genetic Programming and Automatically Defined Functions
SBIA '02 Proceedings of the 16th Brazilian Symposium on Artificial Intelligence: Advances in Artificial Intelligence
Size Control Via Size Fair Genetic Operators In The PushGP Genetic Programming System
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Genetic programming: a paradigm for genetically breeding populations of computer programs to solve problems
Attribute grammar encoding of the structure and behaviour of artificial neural networks
Attribute grammar encoding of the structure and behaviour of artificial neural networks
Dynamic maximum tree depth: a simple technique for avoiding bloat in tree-based GP
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Initialization method for grammar-guided genetic programming
Knowledge-Based Systems
Grammar-Guided Neural Architecture Evolution
IWINAC '07 Proceedings of the 2nd international work-conference on The Interplay Between Natural and Artificial Computation, Part I: Bio-inspired Modeling of Cognitive Tasks
A survey and taxonomy of performance improvement of canonical genetic programming
Knowledge and Information Systems
Construction of Hoare Triples under Generalized Model with Semantically Valid Genetic Operations
ISICA '09 Proceedings of the 4th International Symposium on Advances in Computation and Intelligence
Hi-index | 0.00 |
This paper introduces a new crossover operator for the genetic programming (GP)paradigm, the grammar-based crossover (GBX). This operator works with any grammar-guided genetic programming system. GBX has three important features: it prevents the growth of tree-based GP individuals (a phenomenon known as code bloat), it provides a satisfactory trade-off between the search space exploration and the exploitation capabilities by preserving the context in which subtrees appear in the parent trees and, finally, it takes advantage of the main feature of ambiguous grammars, namely, that there is more than one derivation tree for some sentences (solutions). These features give GBX a high convergence speed and low probability of getting trapped in local optima, as shown throughout the comparison of the results achieved by GBX with other relevant crossover operators in two experiments: a laboratory problem and a real-world task: breast cancer prognosis.