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
Data structures and genetic programming
Advances in genetic programming
The evolution of size and shape
Advances in genetic programming
Evolutive Introns: A Non-Costly Method of Using Introns in GP
Genetic Programming and Evolvable Machines
Exons and Code Growth in Genetic Programming
EuroGP '02 Proceedings of the 5th European Conference on Genetic Programming
Code growth in genetic programming
Code growth in genetic programming
Code growth, explicitly defined introns, and alternative selection schemes
Evolutionary Computation
Code growth in genetic programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Behavioural Diversity and Filtering in GP Navigation Problems
EuroGP '09 Proceedings of the 12th European Conference on Genetic Programming
A survey and taxonomy of performance improvement of canonical genetic programming
Knowledge and Information Systems
Promoting phenotypic diversity in genetic programming
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II
Phenotypic diversity in initial genetic programming populations
EuroGP'10 Proceedings of the 13th European conference on Genetic Programming
Single node genetic programming on problems with side effects
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
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In genetic programming, there is a tendency for individuals in a population to accumulate fragments of code - often called introns - which are redundant in the fitness evaluation of those individuals. Crossover at the sites of certain classes of intron cannot produce a different fitness in the offspring, but the cost of identifying such sites may be high. We have therefore focused our attention on one particular class of non-contributory node that can be easily identified without sophisticated analysis. Experimentation shows that, for certain problem types, the presence of such dormant nodes can be extensive. We have therefore devised a technique that can use this information to reduce the number of fitness evaluations performed, leading to substantial savings in execution time without affecting the results obtained.