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
Genetic Programming III: Darwinian Invention & Problem Solving
Genetic Programming III: Darwinian Invention & Problem Solving
Genetic Programming and Evolvable Machines
Adaption of Operator Probabilities in Genetic Programming
EuroGP '01 Proceedings of the 4th European Conference on Genetic Programming
Genetic Programming IV: Routine Human-Competitive Machine Intelligence
Genetic Programming IV: Routine Human-Competitive Machine Intelligence
Towards identifying populations that increase the likelihood of success in genetic programming
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Parameter sweeps for exploring GP parameters
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
Genetic programming: parametric analysis of structure altering mutation techniques
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
A survey of mutation techniques in genetic programming
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Genetic programming: optimal population sizes for varying complexity problems
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
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Genetic Programming (GP) algorithms benefit from careful consideration of parameter values, especially for complex problems. We submit that determining the optimal parameter value is not as important as finding a window of reasonable parameter values. We test seven problems to determine if windows of reasonable parameter values for mutation rates and population size exist. The results show narrowing, expanding and static windows of effective mutation rates dependent upon the problem type. The results for varying population sizes show that less complex problems use more resources per solution with increasing population size. Conversely as the problem difficulty increases we see either no significant change in solution effort as population size increases, indicating constant efficiency or in some cases we discover decreasing solution effort with larger population sizes. This suggests that in general as the instances of a problem increase in difficulty increasing the population size will either have no effect on efficiency or, for some problems, will lead to relatively small increases in efficiency.