Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Hierarchical learning with procedural abstraction mechanisms
Hierarchical learning with procedural abstraction mechanisms
The evolution of size and shape
Advances in genetic programming
Scaling of program fitness spaces
Evolutionary Computation
Smooth Uniform Crossover with Smooth Point Mutation in Genetic Programming: A Preliminary Study
Proceedings of the Second European Workshop on Genetic Programming
An algorithmic chemistry for genetic programming
EuroGP'05 Proceedings of the 8th European conference on Genetic Programming
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We investigate the distribution of performance of the Boolean functions of 3 Boolean inputs (particularly that of the parity functions), the always-on-6 and even-6 parity functions. We use enumeration, uniform Monte-Carlo random sampling and sampling random full trees. As expected XOR dramatically changes the fitness distributions. In all cases once some minimum size threshold has been exceeded, the distribution of performance is approximately independent of program size. However the distribution of the performance of full trees is different from that of asymmetric trees and varies with tree depth.