Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
Dynamic Training Subset Selection for Supervised Learning in Genetic Programming
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Probability and Information: An Integrated Approach
Probability and Information: An Integrated Approach
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
Theoretical results in genetic programming: the next ten years?
Genetic Programming and Evolvable Machines
HMXT-GP: an information-theoretic approach to genetic programming that maintains diversity
Proceedings of the 2011 ACM Symposium on Applied Computing
Population clustering in genetic programming
EuroGP'06 Proceedings of the 9th European conference on Genetic Programming
Bottom-Up tree evaluation in tree-based genetic programming
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part I
Parallel linear genetic programming for multi-class classification
Genetic Programming and Evolvable Machines
| Hi-index | 0.00 |
Fitness evaluation is often a time consuming activity in genetic programming applications and it is thus of interest to find criteria that can help in reducing the time without compromising the quality of the results. We use well-known results in statistics and information theory to limit the number of fitness cases that are needed for reliable function reconstruction in genetic programming. By using two numerical examples, we show that the results agree with our theoretical predictions. Since our approach is problem-independent, it can be used together with techniques for choosing an efficient set of fitness cases.