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
Using Experimental Design to Find Effective Parameter Settings for Heuristics
Journal of Heuristics
Using Factorial Experiments to Evaluate the Effect of Genetic Programming Parameters
Proceedings of the European Conference on Genetic Programming
Comparing Subtree Crossover with Macromutation
EP '97 Proceedings of the 6th International Conference on Evolutionary Programming VI
Optimizing Protocol Interaction Using Response Surface Methodology
IEEE Transactions on Mobile Computing
Design and Analysis of Experiments
Design and Analysis of Experiments
Mutation as a diversity enhancing mechanism in genetic programming
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Evolutionary computation techniques for intrusion detection in mobile ad hoc networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
Robustness and evolvability of recombination in linear genetic programming
EuroGP'13 Proceedings of the 16th European conference on Genetic Programming
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The role of crossover and mutation in Genetic Programming (GP) has been the subject of much debate since the emergence of the field. In this paper, we contribute new empirical evidence to this argument using a rigorous and principled experimental method applied to six problems common in the GP literature. The approach tunes the algorithm parameters to enable a fair and objective comparison of two different GP algorithms, the first using a combination of crossover and reproduction, and secondly using a combination of mutation and reproduction. We find that crossover does not significantly outperform mutation on most of the problems examined. In addition, we demonstrate that the use of a straightforward Design of Experiments methodology is effective at tuning GP algorithm parameters.