Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Efficient and Accurate Parallel Genetic Algorithms
Efficient and Accurate Parallel Genetic Algorithms
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Analyzing synchronous and asynchronous parallel distributed genetic algorithms
Future Generation Computer Systems - Special issue on bio-impaired solutions to parallel processing problems
Building Better Test Functions
Proceedings of the 6th International Conference on Genetic Algorithms
Explicit Parallelism of Genetic Algorithms through Population Structures
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Efficient evolution of neural networks through complexification
Efficient evolution of neural networks through complexification
The influence of migration sizes and intervals on island models
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Dynamic optimization of migration topology in internet-based distributed genetic algorithms
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Towards an empirical measure of evolvability
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence (Studies in Computational Intelligence)
Using gene expression programming to construct sentence ranking functions for text summarization
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
A multipopulation cultural algorithm using fuzzy clustering
Applied Soft Computing
Is the island model fault tolerant?
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
Generating plants with gene expression programming
AFRIGRAPH '07 Proceedings of the 5th international conference on Computer graphics, virtual reality, visualisation and interaction in Africa
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 03
AdaGEP - An Adaptive Gene Expression Programming Algorithm
SYNASC '07 Proceedings of the Ninth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing
Adaptive Gene Expression Programming Algorithm Based on Cloud Model
BMEI '08 Proceedings of the 2008 International Conference on BioMedical Engineering and Informatics - Volume 01
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Parallel Multi-objective Gene Expression Programming Based on Area Penalty
ICCSIT '08 Proceedings of the 2008 International Conference on Computer Science and Information Technology
ICNC '08 Proceedings of the 2008 Fourth International Conference on Natural Computation - Volume 01
Multiobjective classification with moGEP: an application in the network traffic domain
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
A niching gene expression programming algorithm based on parallel model
APPT'07 Proceedings of the 7th international conference on Advanced parallel processing technologies
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
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Gene Expression Programming (GEP) is a genetic algorithm that evolves linear chromosomes encoding nonlinear (tree-like) structures. In the original GEP algorithm, the genome size is problem specific and is determined through trial and error. In this work, a method for adaptive control of the genome size is presented. The approach introduces mutation, transposition, and recombination operators that enable a population of heterogeneously structured chromosomes, something the original GEP algorithm does not support. This permits crossbreeding between normally incompatible individuals, speciation within a population, increases the evolvability of the representations, and enhances parallel GEP. To test our approach, an assortment of problems were used, including symbolic regression, classification, and parameter optimization. Our experimental results show that our approach provides a solution for the problem of self-adaptive control of the genome size of GEP's representation.