Ruggedness and neutrality—the NKp family of fitness landscapes
ALIFE Proceedings of the sixth international conference on Artificial life
How neutral networks influence evolvability
Complexity
Through the Labyrinth Evolution Finds a Way: A Silicon Ridge
ICES '96 Proceedings of the First International Conference on Evolvable Systems: From Biology to Hardware
Fitness Distance Correlation and Ridge Functions
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Finding Needles in Haystacks Is Not Hard with Neutrality
EuroGP '02 Proceedings of the 5th European Conference on Genetic Programming
Neutrality and ruggedness in robot landscapes
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
The effects of constant neutrality on performance and problem hardness in GP
EuroGP'08 Proceedings of the 11th European conference on Genetic programming
The effect of bloat on the efficiency of incremental evolution of simulated snake-like robot
EuroGP'12 Proceedings of the 15th European conference on Genetic Programming
Proceedings of the 14th annual conference on Genetic and evolutionary computation
A survey of techniques for characterising fitness landscapes and some possible ways forward
Information Sciences: an International Journal
GP-induced and explicit bloating of the seeds in incremental GP improves evolutionary success
Genetic Programming and Evolvable Machines
Hi-index | 0.01 |
When a considerable number of mutations have no effects on fitness values, the fitness landscape is said neutral. In order to study the interplay between neutrality, which exists in many real-world applications, and performances of metaheuristics, it is useful to design landscapes which make it possible to tune precisely neutral degree distribution. Even though many neutral landscape models have already been designed, none of them are general enough to create landscapes with specific neutral degree distributions. We propose three steps to design such landscapes: first using an algorithm we construct a landscape whose distribution roughly fits the target one, then we use a simulated annealing heuristic to bring closer the two distributions and finally we affect fitness values to each neutral network. Then using this new family of fitness landscapes we are able to highlight the interplay between deceptiveness and neutrality.