Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
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 II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
Genetic programming and emergent intelligence
Advances in genetic programming
Genetic Programming and Evolvable Machines
Accurate Replication in Genetic Programming
Proceedings of the 6th International Conference on Genetic Algorithms
Genetic Programming Bloat without Semantics
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
A Survey of Intron Research in Genetics
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Smooth Uniform Crossover with Smooth Point Mutation in Genetic Programming: A Preliminary Study
Proceedings of the Second European Workshop on Genetic Programming
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
Fitness Causes Bloat: Mutation
EuroGP '98 Proceedings of the First European Workshop on Genetic Programming
Evolving computer programs without subtree crossover
IEEE Transactions on Evolutionary Computation
Functional genetic programming and exhaustive program search with combinator expressions
International Journal of Knowledge-based and Intelligent Engineering Systems - Genetic Programming An Emerging Engineering Tool
Genetic Programming and Evolvable Machines
Dynamic maximum tree depth: a simple technique for avoiding bloat in tree-based GP
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Introducing a Perl genetic programming system - and can meta-evolution solve the bloat problem?
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
Bloat control operators and diversity in genetic programming: A comparative study
Evolutionary Computation
Self-replication, evolvability and asynchronicity in stochastic worlds
SAGA'05 Proceedings of the Third international conference on StochasticAlgorithms: foundations and applications
Operator equalisation for bloat free genetic programming and a survey of bloat control methods
Genetic Programming and Evolvable Machines
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The traditional genetic programming crossover and mutation operators have the property that they tend to affect smaller and smaller fractions of a solution tree as the tree grows larger. It is generally thought that this property contributes to the 'code bloat' problem, in which evolving solution trees rapidly become unmanageably large, and researchers have investigated alternate operators designed to avoid this effect. We introduce one such operator, called uniform subtree mutation (USM), and investigate its performance--alone and in combination with traditional crossover--on six standard problems. We measure its behavior usingb oth computational effort and size effort, a variation that takes tree size into account. Our tests show that genetic programming using pure USM reduces evolved tree sizes dramatically, compared to crossover, but does impact solution quality somewhat. In some cases, however, a combination of USM and crossover yielded both smaller trees and superior performance, as measured both by size effort and traditional metrics.