Genetic Algorithms, Operators, and DNA Fragment Assembly
Machine Learning - Special issue on applications in molecular biology
Autocorrelation coefficient for the graph bipartitioning problem
Theoretical Computer Science
On the classification of NP-complete problems in terms of their correlation coefficient
Proceedings of the 5th Twente workshop on on Graphs and combinatorial optimization
On the landscape ruggedness of the quadratic assignment problem
Theoretical Computer Science
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
The Traveling Salesman Problem: A Computational Study (Princeton Series in Applied Mathematics)
The Traveling Salesman Problem: A Computational Study (Princeton Series in Applied Mathematics)
Understanding elementary landscapes
Proceedings of the 10th annual conference on Genetic and evolutionary computation
A polynomial time computation of the exact correlation structure of k-satisfiability landscapes
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
A methodology to find the elementary landscape decomposition of combinatorial optimization problems
Evolutionary Computation
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Local optima networks, landscape autocorrelation and heuristic search performance
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part II
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The Quadratic Assignment Problem (QAP) is a well-known NP-hard combinatorial optimization problem that is at the core of many real-world optimization problems. We prove that QAP can be written as the sum of three elementary landscapes when the swap neighborhood is used. We present a closed formula for each of the three elementary components and we compute bounds for the autocorrelation coefficient.