Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
On a relation between graph edit distance and maximum common subgraph
Pattern Recognition Letters
Foundations of genetic programming
Foundations of genetic programming
Proceedings of the European Conference on Genetic Programming
An Analysis of Koza's Computational Effort Statistic for Genetic Programming
EuroGP '02 Proceedings of the 5th European Conference on Genetic Programming
Finding needles in haystacks is harder with neutrality
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Identifying structural mechanisms in standard genetic programming
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
What makes a problem GP-hard? validating a hypothesis of structural causes
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Crossover bias in genetic programming
EuroGP'07 Proceedings of the 10th European conference on Genetic programming
Redundancy and computational efficiency in Cartesian genetic programming
IEEE Transactions on Evolutionary Computation
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
GECCO 2011 tutorial: cartesian genetic programming
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
An ecological approach to measuring locality in linear genotype to phenotype maps
EuroGP'12 Proceedings of the 15th European conference on Genetic Programming
GECCO 2012 tutorial: cartesian genetic programming
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Length bias and search limitations in cartesian genetic programming
Proceedings of the 15th annual conference on Genetic and evolutionary computation
GECCO 2013 tutorial: cartesian genetic programming
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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An evolutionary algorithm automatically discovers suitable solutions to a problem, which may lie anywhere in a large search space of candidate solutions. In the case of Genetic Programming, this means performing an efficient search of all possible computer programs represented as trees. Exploration of the search space appears to be constrained by structural mechanisms that exist in Genetic Programming as a consequence of using trees to represent solutions. As a result, programs with certain structures are more likely to be evolved, and others extremely unlikely. We investigate whether the graph representation used in Cartesian Genetic Programming causes an analogous biasing effect, imposing natural limitations on the class of solution structures that are likely to be evolved. Representation bias and structural bias are identified: the rarer "regular" structures appear to be easier to evolve than more common "irregular" ones.