Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
A Knowledge-Intensive Genetic Algorithm for Supervised Learning
Machine Learning - Special issue on genetic algorithms
Machine discovery in chemistry: new results
Artificial Intelligence
On using syntactic constraints with genetic programming
Advances in genetic programming
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Evolving Structured Programs with Hierarchical Instructions and Skip Nodes
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Extending Population-Based Incremental Learning to Continuous Search Spaces
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Grammatical Evolution: Evolving Programs for an Arbitrary Language
EuroGP '98 Proceedings of the First European Workshop on Genetic Programming
Introns in Nature and in Simulated Structure Evolution
Biocomputing and emergent computation: Proceedings of BCEC97
Balancing accuracy and parsimony in genetic programming
Evolutionary Computation
Strongly typed genetic programming
Evolutionary Computation
Grammar-based Genetic Programming: a survey
Genetic Programming and Evolvable Machines
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The application of Genetic Programming (GP) to the discovery of empirical laws most often suffers from two limitations. The first one is the size of the search space; the second one is the growth of non-coding segments, the introns, which exhausts the memory resources as GP evolution proceeds. These limitations are addressed by combining Genetic Programming and Stochastic Grammars. On one hand, grammars are used to represent prior knowledge; for instance, context-free grammars can be used to enforce the discovery of dimensionally consistent laws, thereby significantly restricting GP search space. On the other hand, in the spirit of distribution estimation algorithms, the grammar is enriched with derivation probabilities. By exploiting such probabilities, GP avoids the intron phenomenon. The approach is illustrated on a real-world like problem, the identification of behavioral laws in Mechanics.