Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Sequential circuit test generation in a genetic algorithm framework
DAC '94 Proceedings of the 31st annual Design Automation Conference
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
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
Avoiding the Bloat with Stochastic Grammar-Based Genetic Programming
Selected Papers from the 5th European Conference on Artificial Evolution
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
Learning probabilistic networks
The Knowledge Engineering Review
IEEE Transactions on Evolutionary Computation
Scalable estimation-of-distribution program evolution
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Binary encoding for prototype tree of probabilistic model building GP
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
IEEE Transactions on Evolutionary Computation
EA'05 Proceedings of the 7th international conference on Artificial Evolution
Hi-index | 0.00 |
In this work a new approach, named Bayesian Automatic Programming (BAP), to inducing programs is presented. BAP integrates the power of grammar evolution and probabilistic models to evolve programs. We explore the use of BAP in two domains: a regression problem and the artificial ant problem. Its results are compared with traditional Genetic Programming (GP). The experimental results found encourage further investigation, especially to explore BAP in other domains and to improve the proposed approach to incorporating new mechanisms.