The k-coloring fitness landscape
Journal of Combinatorial Optimization
Greedy algorithms for a class of knapsack problems with binary weights
Computers and Operations Research
Review: Measuring instance difficulty for combinatorial optimization problems
Computers and Operations Research
EvoCOP'13 Proceedings of the 13th European conference on Evolutionary Computation in Combinatorial Optimization
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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Fitness landscape analysis techniques are used to better understand the influence of genetic representations and associated variation operators when solving a combinatorial optimization problem. Five representations are investigated for the multidimensional knapsack problem. Common mutation operators, such as bit-flip mutation, are employed to generate fitness landscapes. Measures such as fitness distance correlation and autocorrelation are applied to examine the landscapes associated with the tested genetic encodings. Furthermore, additional experiments are made to observe the effects of adding heuristics and local optimization to the representations. Encodings with a strong heuristic bias are more efficient, and the addition of local optimization techniques further enhances their performance.