The evolution of evolvability in genetic programming
Advances in genetic programming
Measuring the evolvability landscape to study neutrality
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Fitness landscape of the cellular automata majority problem: View from the "Olympus"
Theoretical Computer Science
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
The road to VEGAS: guiding the search over neutral networks
Proceedings of the 13th 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
Fitness Landscape Analysis and Metaheuristics Efficiency
Journal of Mathematical Modelling and Algorithms
Fitness landscapes and graphs: multimodularity, ruggedness and neutrality
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
ParadisEO-MO: from fitness landscape analysis to efficient local search algorithms
Journal of Heuristics
| Hi-index | 0.00 |
Solving efficiently complex problems using metaheuristics, and in particular local search algorithms, requires incorporating knowledge about the problem to solve. In this paper, the permutation flowshop problem is studied. It is well known that in such problems, several solutions may have the same fitness value. As this neutrality property is an important issue, it should be taken into account during the design of search methods. Then, in the context of the permutation flowshop, a deep landscape analysis focused on the neutrality property is driven and propositions on the way to use this neutrality in order to guide the search efficiently are given.