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
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
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Evolutionary construction and adaptation of intelligent systems
Expert Systems with Applications: An International Journal
Grammar-guided evolutionary construction of bayesian networks
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation - Volume Part I
Evolutionary industrial physical model generation
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
Evolving third-person shooter enemies to optimize player satisfaction in real-time
EvoApplications'12 Proceedings of the 2012t European conference on Applications of Evolutionary Computation
Computer Methods and Programs in Biomedicine
Bacterially inspired evolving system with an application to time series prediction
Applied Soft Computing
Hi-index | 0.00 |
This paper proposes a new grammar-guided genetic programming (GGGP) system by introducing two original genetic operators: crossover and mutation, which most influence the evolution process. The first, the so-called grammar-based crossover operator, strikes a good balance between search space exploration and exploitation capabilities and, therefore, enhances GGGP system performance. And the second is a grammar-based mutation operator, based on the crossover, which has been designed to generate individuals that match the syntactical constraints of the context-free grammar that defines the programs to be handled. The use of these operators together in the same GGGP system assures a higher convergence speed and less likelihood of getting trapped in local optima than other related approaches. These features are shown throughout the comparison of the results achieved by the proposed system with other important crossover and mutation methods in two experiments: a laboratory problem and the real-world task of breast cancer prognosis.