The evolution of evolvability in genetic programming
Advances in genetic programming
Neutrality in fitness landscapes
Applied Mathematics and Computation
Foundations of genetic programming
Foundations of genetic programming
Neutrality and the Evolvability of Boolean Function Landscape
EuroGP '01 Proceedings of the 4th European Conference on Genetic Programming
Finding Needles in Haystacks Is Not Hard with Neutrality
EuroGP '02 Proceedings of the 5th European Conference on Genetic Programming
Synthetic Neutrality for Artificial Evolution
AE '99 Selected Papers from the 4th European Conference on Artificial Evolution
Finding needles in haystacks is harder with neutrality
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Neutrality: a necessity for self-adaptation
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Fitness distance correlation in structural mutation genetic programming
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
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
A comprehensive view of fitness landscapes with neutrality and fitness clouds
EuroGP'07 Proceedings of the 10th European conference on Genetic programming
Data mining of genetic programming run logs
EuroGP'07 Proceedings of the 10th European conference on Genetic programming
A study of the neutrality of Boolean function landscapes in genetic programming
Theoretical Computer Science
Genetic Programming and Evolvable Machines
A survey of techniques for characterising fitness landscapes and some possible ways forward
Information Sciences: an International Journal
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Neutrality of some boolean parity fitness landscapes is investigated in this paper. Compared with some well known contributions on the same issue, we define some new measures that help characterizing neutral landscapes, we use a new sampling methodology, which captures some features that are disregarded by uniform random sampling, and we introduce new genetic operators to define the neighborhood of tree structures. We compare the fitness landscape induced by two different sets of functional operators (SNand and SXorNot). The different characteristics of the neutral networks seem to justify the different difficulties of these landscapes for genetic programming.