Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
Genetic programming and emergent intelligence
Advances in genetic programming
Inductive functional programming using incremental program transformation
Artificial Intelligence
Niching methods for genetic algorithms
Niching methods for genetic algorithms
Explicitly defined introns and destructive crossover in genetic programming
Advances in genetic programming
Data structures and genetic programming
Advances in genetic programming
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
The evolution of size and shape
Advances in genetic programming
An updated survey of GA-based multiobjective optimization techniques
ACM Computing Surveys (CSUR)
Foundations of genetic programming
Foundations of genetic programming
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Genetic Programming and Evolvable Machines
Some Considerations on the Reason for Bloat
Genetic Programming and Evolvable Machines
An Analysis of the Causes of Code Growth in Genetic Programming
Genetic Programming and Evolvable Machines
Combining convergence and diversity in evolutionary multiobjective optimization
Evolutionary Computation
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
A Representation for the Adaptive Generation of Simple Sequential Programs
Proceedings of the 1st International Conference on Genetic Algorithms
Compaction of Symbolic Layout Using Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
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
Accurate Replication in Genetic Programming
Proceedings of the 6th International Conference on Genetic Algorithms
Complexity Compression and Evolution
Proceedings of the 6th International Conference on Genetic Algorithms
Fighting Bloat with Nonparametric Parsimony Pressure
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Seeding Genetic Programming Populations
Proceedings of the European Conference on Genetic Programming
EuroGP '01 Proceedings of the 4th European Conference on Genetic Programming
Fitness Causes Bloat: Mutation
EuroGP '98 Proceedings of the First European Workshop on Genetic Programming
A learning system based on genetic adaptive algorithms
A learning system based on genetic adaptive algorithms
Code growth in genetic programming
Code growth in genetic programming
An overview of evolutionary algorithms in multiobjective optimization
Evolutionary Computation
Balancing accuracy and parsimony in genetic programming
Evolutionary Computation
Effects of code growth and parsimony pressure on populations in genetic programming
Evolutionary Computation
Generality versus size in genetic programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
Multi-objective diversity maintenance
Proceedings of the 8th annual conference on Genetic and evolutionary computation
A comparison of bloat control methods for genetic programming
Evolutionary Computation
Proceedings of the 9th annual conference on Genetic and evolutionary computation
ACM SIGEVOlution
Evolving encapsulated programs as shared grammars
Genetic Programming and Evolvable Machines
Efficient tree traversal to reduce code growth in tree-based genetic programming
Journal of Heuristics
Dynamic population variation in genetic programming
Information Sciences: an International Journal
Using Numerical Simplification to Control Bloat in Genetic Programming
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Incorporating expert knowledge in evolutionary search: a study of seeding methods
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Solving iterated functions using genetic programming
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
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
Multi-Objective Genetic Programming for Classification with Unbalanced Data
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
The influence of mutation on population dynamics in multiobjective genetic programming
Genetic Programming and Evolvable Machines
Predicting solution rank to improve performance
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Abandoning objectives: Evolution through the search for novelty alone
Evolutionary Computation
Long memory time series forecasting by using genetic programming
Genetic Programming and Evolvable Machines
Characterizing diversity in genetic programming
EuroGP'06 Proceedings of the 9th European conference on Genetic Programming
Dynamic size populations in distributed genetic programming
EuroGP'05 Proceedings of the 8th European conference on Genetic Programming
The tree-string problem: an artificial domain for structure and content search
EuroGP'05 Proceedings of the 8th European conference on Genetic Programming
Operator equalisation for bloat free genetic programming and a survey of bloat control methods
Genetic Programming and Evolvable Machines
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Variable length methods for evolutionary computation can lead to a progressive and mainly unnecessary growth of individuals, known as bloat. First, we propose to measure performance in genetic programming as a function of the number of nodes, rather than trees, that have been evaluated. Evolutionary Multi-Objective Optimization (EMOO) constitutes a principled way to optimize both size and fitness and may provide parameterless size control. Reportedly, its use can also lead to minimization of size at the expense of fitness. We replicate this problem, and an empirical analysis suggests that multi-objective size control particularly requires diversity maintenance. Experiments support this explanation.The multi-objective approach is compared to genetic programming without size control on the 11-multiplexer, 6-parity, and a symbolic regression problem. On all three test problems, the method greatly reduces bloat and significantly improves fitness as a function of computational expense. Using the FOCUS algorithm, multi-objective size control is combined with active pursuit of diversity, and hypothesized minimum-size solutions to 3-, 4- and 5-parity are found. The solutions thus found are furthermore easily interpretable. When combined with diversity maintenance, EMOO can provide an adequate and parameterless approach to size control in variable length evolution.