Introducing probabilistic adaptive mapping developmental genetic programming with redundant mappings
Genetic Programming and Evolvable Machines
Probabilistic incremental program evolution
Evolutionary Computation
Evolutionary consequences of coevolving targets
Evolutionary Computation
Semantic Aware Crossover for Genetic Programming: The Case for Real-Valued Function Regression
EuroGP '09 Proceedings of the 12th European Conference on Genetic Programming
A linear estimation-of-distribution GP system
EuroGP'08 Proceedings of the 11th European conference on Genetic programming
Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
A Bayesian Network Approach to Program Generation
IEEE Transactions on Evolutionary Computation
Program optimisation with dependency injection
EuroGP'13 Proceedings of the 16th European conference on Genetic Programming
Introducing graphical models to analyze genetic programming dynamics
Proceedings of the twelfth workshop on Foundations of genetic algorithms XII
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We present an improved estimation of distribution (EDA) genetic programming (GP) algorithm which does not rely upon a prototype tree. Instead of using a prototype tree, Operator-Free Genetic Programming learns the distribution of ancestor node chains, "n-grams", in a fit fraction of each generation's population. It then uses this information, via sampling, to create trees for the next generation. Ancestral n-grams are used because an analysis of a GP run conducted by learning depth first graphical models for each generation indicated their emergence as substructures of conditional dependence. We are able to show that our algorithm, without an operator and a prototype tree, achieves, on average, performance close to conventional tree based crossover GP on the problem we study. Our approach sets a direction for pattern-based EDA GP which off ers better tractability and improvements over GP with operators or EDAs using prototype trees.