Sorting by reversals is difficult
RECOMB '97 Proceedings of the first annual international conference on Computational molecular biology
A fast algorithm for computing longest common subsequences
Communications of the ACM
Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
Fitness Distance Correlation and Ridge Functions
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
A Study of Fitness Distance Correlation as a Difficulty Measure in Genetic Programming
Evolutionary Computation
A 1.375-Approximation Algorithm for Sorting by Transpositions
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A review of metrics on permutations for search landscape analysis
Computers and Operations Research
Fitness landscape analysis and memetic algorithms for the quadratic assignment problem
IEEE Transactions on Evolutionary Computation
| Hi-index | 0.00 |
The fitness distance correlation (FDC) as a measure for problem difficulty was first introduced by Forrest and Jones. It was applied to many binary coded problems. This method is now applied to permutation based problems. As demanded by Schiavinotto and Stützle, the distance in a search space is calculated by regarding the steps of the (neighborhood) operator. In this paper the five most common operators for permutations are analyzed on symmetric and asymmetric TSP instances. In addition a local quality measure, the point quality (PQ) is proposed as a supplement to the global FDC. With this local measure more characteristics and differences can be investigated. Some experimental results are illustrating these concepts.