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
Evolutive Introns: A Non-Costly Method of Using Introns in GP
Genetic Programming and Evolvable Machines
Grammar-Guided Genetic Programming and Automatically Defined Functions
SBIA '02 Proceedings of the 16th Brazilian Symposium on Artificial Intelligence: Advances in Artificial Intelligence
EuroGP '98 Proceedings of the First European Workshop on Genetic Programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Connection Science - Evolutionary Learning and Optimisation
A survey and taxonomy of performance improvement of canonical genetic programming
Knowledge and Information Systems
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Decimation and automatically defined functions are intended to improve the fitness of the generated programs and to increase the rate of convergence to the solution. Each method has an associated computational cost, the cost for automatically defined functions being considerably higher than for decimation. This paper compares the performance improvements in genetic programming provided by automatically defined functions with that of decimation on four common benchmark problems – the Santa Fe ant, the lawnmower, even 3-bit parity and a symbolic regression problem. The results indicate that decimation provides improvement in performance that justifies the additional computation but the added computational effort required for automatically defined functions is not justified by any performance improvements.