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.
Subject
Genetic Algorithms
Conference
IEEE Congress on Evolutionary Computation, July 2006
Cited by
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 - ieeexplore.ieee.org
Análise da aprendizagem de ligações em otimização evolutiva
JP Martins - teses.usp.br
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.
