A fuzzy Prolog database system
A fuzzy Prolog database system
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
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
Advances in genetic programming
Advances in genetic programming
Inductive functional programming using incremental program transformation
Artificial Intelligence
Evolving recursive functions for the even-parity problem using genetic programming
Advances in genetic programming
A Methodology for LISP Program Construction from Examples
Journal of the ACM (JACM)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Inducing Logic Programs With Genetic Algorithms: The Genetic Logic Programming System
IEEE Expert: Intelligent Systems and Their Applications
Learning Logical Definitions from Relations
Machine Learning
Inductive Learning in Deductive Databases
IEEE Transactions on Knowledge and Data Engineering
TAI '95 Proceedings of the Seventh International Conference on Tools with Artificial Intelligence
The Push3 execution stack and the evolution of control
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Evolutionary program induction directed by logic grammars
Evolutionary Computation
Data Mining on DNA Sequences of Hepatitis B Virus
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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One of the most important and challenging areas of research in evolutionary algorithms is the investigation of ways to successfully apply evolutionary algorithms to larger and more complicated problems. In this paper, we apply GGP (Generic Genetic Programming) to evolve general recursive functions for the even-n-parity problem from noisy training examples. GGP is very flexible and programs in various programming languages can be acquired. Moreover, it is powerful enough to handle context-sensitive information and domain-dependent knowledge. A number of experiments have been performed to determine the impact of noise in training examples on the speed of learning.