Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
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
Convergence Rates For The Distribution Of Program Outputs
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Fitness Distance Correlation And Problem Difficulty For Genetic Programming
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Concepts of Inductive Genetic Programming
EuroGP '98 Proceedings of the First European Workshop on Genetic Programming
Predictive models for the breeder genetic algorithm i. continuous parameter optimization
Evolutionary Computation
A Study of Fitness Distance Correlation as a Difficulty Measure in Genetic Programming
Evolutionary Computation
The impact of population size on code growth in GP: analysis and empirical validation
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Crossover, sampling, bloat and the harmful effects of size limits
EuroGP'08 Proceedings of the 11th European conference on Genetic programming
Theoretical results in genetic programming: the next ten years?
Genetic Programming and Evolvable Machines
The halting probability in von neumann architectures
EuroGP'06 Proceedings of the 9th European conference on Genetic Programming
Sub-tree swapping crossover and arity histogram distributions
EuroGP'10 Proceedings of the 13th European conference on Genetic Programming
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Point mutation has no effect on almost all linear programs. In two genetic programming (GP) computers (cyclic and bit flip) we calculate the fitness evaluations needed using steepest ascent and first ascent hill climbers and evolutionary search. We describe how the average fitness landscape scales with program length and give general bounds.