The Role of Representation on the Multidimensional Knapsack Problem by means of Fitness Landscape Analysis



We investigate five different encodings for the Multidimensional Knapsack Problem, using fitness landscape analysis techniques, in order to better understand the influence of genetic representations when solving a combinatorial optimization problem. Fitness distance correlation and autocorrelation measures are employed to analyze the encodings. The effect of heuristics, as well as repair and local optimization is also examined. The investigation helps to understand how the adopted representations influence the search performance of an evolutionary algorithm.


Genetic Algorithms


IEEE Congress on Evolutionary Computation, July 2006

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Year 2015 : 3 citations

 An Analysis of the Fitness Landscape of Travelling Salesman Problem
MH Tayarani-N, A Prügel-Bennett - Evolutionary computation, 2015 - MIT Press

 A differential evolution algorithm with variable neighborhood search for multidimensional knapsack problem
MF Tasgetiren, QK Pan, D Kizilay… - … (CEC), 2015 IEEE …, 2015 -

 Análise da aprendizagem de ligações em otimização evolutiva
JP Martins -

Year 2011 : 1 citations

 Yanghui Wu; McCall, J.; Corne, D.; , "Fitness landscape analysis of Bayesian network structure learning," Evolutionary Computation (CEC), 2011 IEEE Congress on , vol., no., pp.981-988, 5-8 June 2011
doi: 10.1109/CEC.2011.5949724

Year 2009 : 2 citations

 P. Rohlfshagen, X. Yao (2009). The Dynamic Knapsack Problem Revisited: A New Benchmark Problem for Dynamic Combinatorial Optimisation. Proceedings of the Evoworkshops 2009, Lecture Notes on Computer Science 53484, pp. 745-754, Spinger-Verlag.

 E Özcan, C Ba?aran. A case study of memetic algorithms for constraint optimization. Soft Computing, 2009, Springer.