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 royal tree problem, a benchmark for single and multiple population genetic programming
Advances in genetic programming
Foundations of genetic programming
Foundations of genetic programming
What Makes a Problem GP-Hard? Analysis of a Tunably Difficult Problem in Genetic Programming
Genetic Programming and Evolvable Machines
A Study of Fitness Distance Correlation as a Difficulty Measure in Genetic Programming
Evolutionary Computation
Constant creation in grammatical evolution
International Journal of Innovative Computing and Applications
GEVA: grammatical evolution in Java
ACM SIGEVOlution
Analysis of a digit concatenation approach to constant creation
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
On the limiting distribution of program sizes in tree-based genetic programming
EuroGP'07 Proceedings of the 10th European conference on Genetic programming
Crossover, sampling, bloat and the harmful effects of size limits
EuroGP'08 Proceedings of the 11th European conference on Genetic programming
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This paper studies the difference between Persistent Random Constants (PRC) and Digit Concatenation as methods for generating constants. It has been shown that certain problems have different fitness landscapes depending on how they are represented, independent of changes to the combinatorial search space, thus changing problem difficulty. In this case we show that the method for generating the constants can also influence how hard the problem is for Genetic Programming.