Ruggedness and neutrality—the NKp family of fitness landscapes
ALIFE Proceedings of the sixth international conference on Artificial life
Finite-time Analysis of the Multiarmed Bandit Problem
Machine Learning
Fitness landscapes and evolvability
Evolutionary Computation
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
The Advantages of Landscape Neutrality in Digital Circuit Evolution
ICES '00 Proceedings of the Third International Conference on Evolvable Systems: From Biology to Hardware
Genotype-Phenotype-Mapping and Neutral Variation - A Case Study in Genetic Programming
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Neutrality and the Evolvability of Boolean Function Landscape
EuroGP '01 Proceedings of the 4th European Conference on Genetic Programming
An adaptive pursuit strategy for allocating operator probabilities
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Fitness landscape of the cellular automata majority problem: View from the "Olympus"
Theoretical Computer Science
Adaptive operator selection with dynamic multi-armed bandits
Proceedings of the 10th annual conference on Genetic and evolutionary computation
When gravity fails: local search topology
Journal of Artificial Intelligence Research
Toward comparison-based adaptive operator selection
Proceedings of the 12th annual conference on Genetic and evolutionary computation
NILS: a neutrality-based iterated local search and its application to flowshop scheduling
EvoCOP'11 Proceedings of the 11th European conference on Evolutionary computation in combinatorial optimization
On the neutrality of flowshop scheduling fitness landscapes
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
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VEGAS (Varying Evolvability-Guided Adaptive Search) is a new methodology proposed to deal with the neutrality property that frequently appears on combinatorial optimization problems. Its main feature is to consider the whole evaluated solutions of a neutral network rather than the last accepted solution. Moreover, VEGAS is designed to escape from plateaus based on the evolvability of solutions, and on a multi-armed bandit by selecting the more promising solution from the neutral network. Experiments are conducted on NK-landscapes with neutrality. Results show the importance of considering the whole identified solutions from the neutral network and of guiding the search explicitly. The impact of the level of neutrality and of the exploration-exploitation trade-off are deeply analyzed.