Information Processing Letters
Inferring decision trees using the minimum description length principle
Information and Computation
Introduction: paradigms for machine learning
Machine learning: paradigms and methods
A general framework for parallel distributed processing
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Dualistic geometry of the manifold of higher-order neurons
Neural Networks
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Neural networks and the bias/variance dilemma
Neural Computation
Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
Advances in genetic programming
Advances in genetic programming
Genetic programming using a minimum description length principle
Advances in genetic programming
Some studies in machine learning using the game of checkers
Computers & thought
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Simultaneous Discovery of Reusable Detectors and Subroutines Using Genetic Programming
Proceedings of the 5th International Conference on Genetic Algorithms
Competitive Environments Evolve Better Solutions for Complex Tasks
Proceedings of the 5th International Conference on Genetic Algorithms
System Identification using Structured Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
Genetic Programming for Feature Discovery and Image Discrimination
Proceedings of the 5th International Conference on Genetic Algorithms
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
Effects of Occam's Razor in Evolving Sigma-Pi Neural Nets
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
An overview of evolutionary algorithms for parameter optimization
Evolutionary Computation
Predictive models for the breeder genetic algorithm i. continuous parameter optimization
Evolutionary Computation
Size Fair and Homologous Tree Crossovers for Tree Genetic Programming
Genetic Programming and Evolvable Machines
Bayesian Methods for Efficient Genetic Programming
Genetic Programming and Evolvable Machines
Genetic Programming and Evolvable Machines
Evolutionary modeling and inference of gene network
Information Sciences—Informatics and Computer Science: An International Journal - Bioinformatics-selected papers from 4th CBGI & 6th JCIS Proceedings
Genetic Programming: A Review of Some Concerns
ICCS '01 Proceedings of the International Conference on Computational Science-Part II
Genetic Programming Bloat without Semantics
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Fighting Bloat with Nonparametric Parsimony Pressure
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Genetic Programming with Active Data Selection
SEAL'98 Selected papers from the Second Asia-Pacific Conference on Simulated Evolution and Learning on Simulated Evolution and Learning
A Schema Theory Analysis of the Evolution of Size in Genetic Programming with Linear Representations
EuroGP '01 Proceedings of the 4th European Conference on Genetic Programming
Avoiding the Bloat with Stochastic Grammar-Based Genetic Programming
Selected Papers from the 5th European Conference on Artificial Evolution
Modification point depth and genome growth in genetic programming
Evolutionary Computation
Multi-Objective Methods for Tree Size Control
Genetic Programming and Evolvable Machines
Automated Discovery of Numerical Approximation Formulae via Genetic Programming
Genetic Programming and Evolvable Machines
General schema theory for genetic programming with subtree-swapping crossover: Part II
Evolutionary Computation
Problem Difficulty and Code Growth in Genetic Programming
Genetic Programming and Evolvable Machines
Resource-limited genetic programming: the dynamic approach
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Preventing overfitting in GP with canary functions
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Variable Length Representation in Evolutionary Electronics
Evolutionary Computation
A comparison of bloat control methods for genetic programming
Evolutionary Computation
Genetic programming for epileptic pattern recognition in electroencephalographic signals
Applied Soft Computing
A numerical approach to genetic programming for system identification
Evolutionary Computation
Evolutionary induction of sparse neural trees
Evolutionary Computation
Effects of code growth and parsimony pressure on populations in genetic programming
Evolutionary Computation
Putting more genetics into genetic algorithms
Evolutionary Computation
Genetic programming for medical classification: a program simplification approach
Genetic Programming and Evolvable Machines
Evolutionary algorithm considering program size: efficient program evolution using grape
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Inference of differential equation models by genetic programming
Information Sciences: an International Journal
Efficient tree traversal to reduce code growth in tree-based genetic programming
Journal of Heuristics
Using Numerical Simplification to Control Bloat in Genetic Programming
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
Induction machine fault detection using clone selection programming
Expert Systems with Applications: An International Journal
Genetic Programming and Evolvable Machines
A Statistical Learning Perspective of Genetic Programming
EuroGP '09 Proceedings of the 12th European Conference on Genetic Programming
How online simplification affects building blocks in genetic programming
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Code growth in genetic programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Generality versus size in genetic programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
IEEE Transactions on Evolutionary Computation
A survey and taxonomy of performance improvement of canonical genetic programming
Knowledge and Information Systems
Measuring and Optimizing Behavioral Complexity for Evolutionary Reinforcement Learning
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
A simple but theoretically-motivated method to control bloat in genetic programming
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
Parsimony doesn't mean simplicity: genetic programming for inductive inference on noisy data
EuroGP'07 Proceedings of the 10th European conference on Genetic programming
A novel approach to machine discovery: genetic programming and stochastic grammars
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
Slightly beyond Turing's computability for studying genetic programming
MCU'07 Proceedings of the 5th international conference on Machines, computations, and universality
Operator equalisation and bloat free GP
EuroGP'08 Proceedings of the 11th European conference on Genetic programming
Implicitly controlling bloat in genetic programming
IEEE Transactions on Evolutionary Computation
Theoretical results in genetic programming: the next ten years?
Genetic Programming and Evolvable Machines
Variance based selection to improve test set performance in genetic programming
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Long memory time series forecasting by using genetic programming
Genetic Programming and Evolvable Machines
Molecular learning of wDNF formulae
DNA'05 Proceedings of the 11th international conference on DNA Computing
Genetic programming, validation sets, and parsimony pressure
EuroGP'06 Proceedings of the 9th European conference on Genetic Programming
A relaxed approach to simplification in genetic programming
EuroGP'10 Proceedings of the 13th European conference on Genetic Programming
Analysis of building blocks with numerical simplification in genetic programming
EuroGP'10 Proceedings of the 13th European conference on Genetic Programming
Improving the parsimony of regression models for an enhanced genetic programming process
EUROCAST'11 Proceedings of the 13th international conference on Computer Aided Systems Theory - Volume Part I
Random sampling technique for overfitting control in genetic programming
EuroGP'12 Proceedings of the 15th European conference on Genetic Programming
Operator equalisation for bloat free genetic programming and a survey of bloat control methods
Genetic Programming and Evolvable Machines
MT-CGP: mixed type cartesian genetic programming
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Compressed network complexity search
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
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
Controlling bloat through parsimonious elitist replacement and spatial structure
EuroGP'13 Proceedings of the 16th European conference on Genetic Programming
Pattern-guided genetic programming
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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Genetic programming is distinguished from other evolutionary algorithms in that it uses tree representations of variable size instead of linear strings of fixed length. The flexible representation scheme is very important because it allows the underlying structure of the data to be discovered automatically. One primary difficulty, however, is that the solutions may grow too big without any improvement of their generalization ability. In this article we investigate the fundamental relationship between the performance and complexity of the evolved structures. The essence of the parsimony problem is demonstrated empirically by analyzing error landscapes of programs evolved for neural network synthesis. We consider genetic programming as a statistical inference problem and apply the Bayesian model-comparison framework to introduce a class of fitness functions with error and complexity terms. An adaptive learning method is then presented that automatically balances the model-complexity factor to evolve parsimonious programs without losing the diversity of the population needed for achieving the desired training accuracy. The effectiveness of this approach is empirically shown on the induction of sigma-pi neural networks for solving a real-world medical diagnosis problem as well as benchmark tasks.