Randomization tests
Simulated annealing: theory and applications
Simulated annealing: theory and applications
A genetic algorithm for flowshop sequencing
Computers and Operations Research - Special issue on genetic algorithms
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms applied to the continuous flow shop problem
Computers and Industrial Engineering
The String-to-String Correction Problem
Journal of the ACM (JACM)
Algorithms for the Longest Common Subsequence Problem
Journal of the ACM (JACM)
Flowshop scheduling with limited temporary storage
Journal of the ACM (JACM)
Swarm intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
AllelesLociand the Traveling Salesman Problem
Proceedings of the 1st International Conference on Genetic Algorithms
Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
Stochastic Local Search: Foundations & Applications
Stochastic Local Search: Foundations & Applications
Distance measures based on the edit distance for permutation-type representations
Journal of Heuristics
A review of metrics on permutations for search landscape analysis
Computers and Operations Research
A discrete particle swarm optimization algorithm for the no-wait flowshop scheduling problem
Computers and Operations Research
Applying adaptive algorithms to epistatic domains
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 1
Hybrid evolutionary algorithm for flowtime minimisation in no-wait flowshop scheduling
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
Fitness landscape analysis and memetic algorithms for the quadratic assignment problem
IEEE Transactions on Evolutionary Computation
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The fitness landscape of the no-wait (continuous) flow-shop scheduling problem is investigated by examining the ruggedness of the landscape and the correlation between the quality of a solution and its distance to an optimal solution. The results confirm the presence of a big valley structure as known from other combinatorial optimization problems. The suitability of the landscape for search with evolutionary computation and local search methods is discussed. The observations are validated by experiments with two evolutionary algorithms.