Elements of information theory
Elements of information theory
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic programming and emergent intelligence
Advances in genetic programming
Scalable learning in genetic programming using automatic function definition
Advances in genetic programming
Genetic programming using a minimum description length principle
Advances in genetic programming
Competitively evolving decision trees against fixed training cases for natural language processing
Advances in genetic programming
Advances in genetic programming
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Explicitly defined introns and destructive crossover in genetic programming
Advances in genetic programming
Simultaneous evolution of programs and their control structures
Advances in genetic programming
Discovery of subroutines in genetic programming
Advances in genetic programming
Evolutionary identification of macro-mechanical models
Advances in genetic programming
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Size Fair and Homologous Tree Crossovers for Tree Genetic Programming
Genetic Programming and Evolvable Machines
Genetic Programming of Minimal Neural Nets Using Occam's Razor
Proceedings of the 5th International Conference on Genetic Algorithms
Generality and Difficulty in Genetic Programming: Evolving a Sort
Proceedings of the 5th International Conference on Genetic Algorithms
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
Evolving Compact Solutions in Genetic Programming: A Case Study
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Fitness Causes Bloat: Mutation
EuroGP '98 Proceedings of the First European Workshop on Genetic Programming
Balancing accuracy and parsimony in genetic programming
Evolutionary Computation
Empirical studies of the genetic algorithm with noncoding segments
Evolutionary Computation
Evolutionary induction of sparse neural trees
Evolutionary Computation
Effects of code growth and parsimony pressure on populations in genetic programming
Evolutionary Computation
Code growth in genetic programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Investigating the generality of automatically defined functions
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Bayesian Evolutionary Optimization Using Helmholtz Machines
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Adaptive Genetic Programming Applied to New and Existing Simple Regression Problems
EuroGP '01 Proceedings of the 4th European Conference on Genetic Programming
Modification point depth and genome growth in genetic programming
Evolutionary Computation
A unified Bayesian framework for evolutionary learning and optimization
Advances in evolutionary computing
Operator equalisation for bloat free genetic programming and a survey of bloat control methods
Genetic Programming and Evolvable Machines
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A Bayesian framework for genetic programming (GP) is presented. This is motivated by the observation that genetic programming iteratively searches populations of fitter programs and thus the information gained in the previous generation can be used in the next generation. The Bayesian GP makes use of Bayes theorem to estimate the posterior distribution of programs from their prior distribution and likelihood for the fitness data observed. Offspring programs are then generated by sampling from the posterior distribution by genetic variation operators. We present two GP algorithms derived from the Bayesian GP framework. One is the genetic programming with the adaptive Occam's razor (AOR) designed to evolve parsimonious programs. The other is the genetic programming with incremental data inheritance (IDI) designed to accelerate evolution by active selection of fitness cases. A multiagent learning task is used to demonstrate the effectiveness of the presented methods. In a series of experiments, AOR reduced solution complexity by 20% and IDI doubled evolution speed, both without loss of solution accuracy.