Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
The evolution of evolvability in genetic programming
Advances in genetic programming
Genetic programming and emergent intelligence
Advances in genetic programming
Explicitly defined introns and destructive crossover in genetic programming
Advances in genetic programming
Type inheritance in strongly typed genetic programming
Advances in genetic programming
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
An Introduction to Discrete Mathematics and Its Applications
An Introduction to Discrete Mathematics and Its Applications
Genetic Programming for Feature Discovery and Image Discrimination
Proceedings of the 5th International Conference on Genetic Algorithms
Shall We Repair? Genetic AlgorithmsCombinatorial Optimizationand Feasibility Constraints
Proceedings of the 5th International Conference on Genetic Algorithms
An analysis of genetic programming
An analysis of genetic programming
Empirical studies of the genetic algorithm with noncoding segments
Evolutionary Computation
Strongly typed genetic programming
Evolutionary Computation
SAC '97 Proceedings of the 1997 ACM symposium on Applied computing
Introducing Start Expression Genes to the Linkage Learning Genetic Algorithm
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
A comparison of the fixed and floating building block representation in the genetic algorithm
Evolutionary Computation
Collective adaptation: The exchange of coding segments
Evolutionary Computation
Putting more genetics into genetic algorithms
Evolutionary Computation
Entailment for specification refinement
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Multi Niche parallel GP with a junk-code migration model
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
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Research into the utility of non-eoding segments, or introns, in genetic-based encodings has shown that they expedite the evolution of solutions in domains by protecting building blocks against destructive crossover. We consider a genetic programming system where non-coding segments can be removed, and the resultant chromosomes returned into the population. This parsimonious repair leads to premature convergence, since as we remove the naturally occurring non-coding segments, we strip away their protective backup feature. We then duplicate the coding segments in the repaired chromosomes, and place the modified chromosomes into the population. The duplication method significantly improves the learning rate in the domain we have considered. We also show that this method can be applied to other domains.