C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Genetic Programming and Autoconstructive Evolution with the Push Programming Language
Genetic Programming and Evolvable Machines
Generating effective symmetry-breaking predicates for search problems
Discrete Applied Mathematics
Balancing accuracy and parsimony in genetic programming
Evolutionary Computation
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Semantic building blocks in genetic programming
EuroGP'08 Proceedings of the 11th European conference on Genetic programming
Visual Learning by Evolutionary and Coevolutionary Feature Synthesis
IEEE Transactions on Evolutionary Computation
Paper: Modeling by shortest data description
Automatica (Journal of IFAC)
On relationships between semantic diversity, complexity and modularity of programming tasks
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Genetic programming: where meaning emerges from program code
Genetic Programming and Evolvable Machines
| Hi-index | 0.00 |
Online progress in search and optimization is often hindered by neutrality in the fitness landscape, when many genotypes map to the same fitness value. We propose a method for imposing a gradient on the fitness function of a metaheuristic (in this case, Genetic Programming) via a metric (Minimum Description Length) induced from patterns detected in the trajectory of program execution. These patterns are induced via a decision tree classifier. We apply this method to a range of integer and boolean-valued problems, significantly outperforming the standard approach. The method is conceptually straightforward and applicable to virtually any metaheuristic that can be appropriately instrumented.