Genetic programming (videotape): the movie
Genetic programming (videotape): the movie
A compiling genetic programming system that directly manipulates the machine code
Advances in genetic programming
Advanced algorithms for neural networks: a C++ sourcebook
Advanced algorithms for neural networks: a C++ sourcebook
Explicitly defined introns and destructive crossover in genetic programming
Advances in genetic programming
Evolving Turing-Complete Programs for a Register Machine with Self-modifying Code
Proceedings of the 6th International Conference on Genetic Algorithms
Complexity Compression and Evolution
Proceedings of the 6th International Conference on Genetic Algorithms
An Evaluation of EvolutionaryGeneralisation in Genetic Programming
Artificial Intelligence Review
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: FEA 2002
Multi-optimization improves genetic programming generalization ability
Proceedings of the 9th annual conference on Genetic and evolutionary computation
The Generalisation Ability of a Selection Architecture for Genetic Programming
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Using crossover based similarity measure to improve genetic programming generalization ability
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Theoretical results in genetic programming: the next ten years?
Genetic Programming and Evolvable Machines
Open issues in genetic programming
Genetic Programming and Evolvable Machines
A quantitative study of learning and generalization in genetic programming
EuroGP'11 Proceedings of the 14th European conference on Genetic programming
Improving the generalisation ability of genetic programming with semantic similarity based crossover
EuroGP'10 Proceedings of the 13th European conference on Genetic Programming
Where should we stop? an investigation on early stopping for GP learning
SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
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Compiling Genetic Programming Systems ('CPGS') are advanced evolutionary algorithms that directly evolve RISC machine code. In this paper we compare the ability of CGPS to generalize with that of other machine learning ('ML') paradigms. This study presents our results on three classification problems. Our study involved 720 complete CGPS runs of population 3000 each, over 500 billion fitness evaluations and 480 neural network runs as benchmarks. Our results were as follows: 1. When CGPS was trained on data sets that were not too sparse, CGPS performed very well, equaling the generalization capability of other ML systems quickly and consistently. 2. When CGPS was trained on very sparse data sets, CGPS produced individuals that generalized almost as well other ML systems trained on much larger data sets. 3. As between CGPS and multilayer feedforward neural networks trained on the same sparse data sets, CGPS generalized as well (and often better) than the neural network.