Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Explicitly defined introns and destructive crossover in genetic programming
Advances in genetic programming
Genetic Programming and Evolvable Machines
Lexicographic Parsimony Pressure
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Evolving Behaviors for Cooperating Agents
ISMIS '00 Proceedings of the 12th International Symposium on Foundations of Intelligent Systems
Explicit Control of Diversity and Effective Variation Distance in Linear Genetic Programming
EuroGP '02 Proceedings of the 5th European Conference on Genetic Programming
A learning system based on genetic adaptive algorithms
A learning system based on genetic adaptive algorithms
Balancing accuracy and parsimony in genetic programming
Evolutionary Computation
Collective adaptation: The exchange of coding segments
Evolutionary Computation
Putting more genetics into genetic algorithms
Evolutionary Computation
Code growth in genetic programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Multi-Objective Methods for Tree Size Control
Genetic Programming and Evolvable Machines
Problem Difficulty and Code Growth in Genetic Programming
Genetic Programming and Evolvable Machines
Molecular programming: evolving genetic programs in a test tube
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
A comparison of bloat control methods for genetic programming
Evolutionary Computation
Neutral offspring controlling operators in genetic programming
Pattern Recognition
Autonomous and cooperative robotic behavior based on fuzzy logic and genetic programming
Integrated Computer-Aided Engineering
An analysis of multi-sampled issue and no-replacement tournament selection
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Learning General Solutions through Multiple Evaluations during Development
ICES '08 Proceedings of the 8th international conference on Evolvable Systems: From Biology to Hardware
Efficient tree traversal to reduce code growth in tree-based genetic programming
Journal of Heuristics
Genetic Programming and Evolvable Machines
Evolutionary and embryogenic approaches to autonomic systems
Proceedings of the 3rd International Conference on Performance Evaluation Methodologies and Tools
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
A survey and taxonomy of performance improvement of canonical genetic programming
Knowledge and Information Systems
A survey of evolutionary and embryogenic approaches to autonomic networking
Computer Networks: The International Journal of Computer and Telecommunications Networking
Implicitly controlling bloat in genetic programming
IEEE Transactions on Evolutionary Computation
Measuring bloat, overfitting and functional complexity in genetic programming
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Long memory time series forecasting by using genetic programming
Genetic Programming and Evolvable Machines
Guiding genetic program based data mining using fuzzy rules
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Operator equalisation for bloat free genetic programming and a survey of bloat control methods
Genetic Programming and Evolvable Machines
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Many forms of parsimony pressure are parametric, that is final fitness is a parametric model of the actual size and raw fitness values. The problem with parametric techniques is that they are hard to tune to prevent size from dominating fitness late in the evolutionary run, or to compensate for problem-dependent nonlinearities in the raw fitness function. In this paper we briefly discuss existing bloat-control techniques, then introduce two new kinds of non-parametric parsimony pressure, Direct and Proportional Tournament. As their names suggest, these techniques are based on simple modifications of tournament selection to consider both size and fitness, but not together as a combined parametric equation. We compare the techniques against, and in combination with, the most popular genetic programming bloat-control technique, Koza-style depth limiting, and show that they are effective in limiting size while still maintaining good best-fitness-of-run results.