Guided genetic algorithm for the multidimensional knapsack problem

Acan A, Tekol Y (2003) Chromosome reuse in genetic algorithms. In: Genetic and evolutionary computation conference, Springer, pp 695–705

Akçay Y, Li H, Xu SH (2007) Greedy algorithm for the general multidimensional knapsack problem. Ann Oper Res 150(1):17

Bader-El-Den M, Gaber M (2012) Garf: towards self-optimised random forests. In: International conference on neural information processing, Springer, pp 506–515

Bader-El-Den M, Poli R (2007) Generating sat local-search heuristics using a gp hyper-heuristic framework. In: International conference on artificial evolution (Evolution Artificielle), Springer, pp 37–49

Bader-El-Den M, Poli R, Fatima S (2009) Evolving timetabling heuristics using a grammar-based genetic programming hyper-heuristic framework. Memet Comput 1(3):205–219

Balas E, Zemel E (1980) An algorithm for large zero-one knapsack problems. Oper Res 28(5):1130–1154

Baroni MDV, Varejão FM (2015) A shuffled complex evolution algorithm for the multidimensional knapsack problem. In: Iberoamerican congress on pattern recognition, Springer, pp 768–775

Castro F, Gelbukh A, González M (2013) Advances in Soft Computing and Its Applications. In: 12th Mexican international conference, MICAI 2013, Mexico City, Mexico, November 24-30, 2013, proceedings. No. pt. 2 in lecture notes in computer science, Springer Berlin Heidelberg, https://books.google.com.om/books?id=WgC6BQAAQBAJ

Chen SH, Chang PC, Cheng T, Zhang Q (2012) A self-guided genetic algorithm for permutation flowshop scheduling problems. Comput Oper Res 39(7):1450–1457

Chu PC, Beasley JE (1998) A genetic algorithm for the multidimensional knapsack problem. J Heuristics 4(1):63–86

Dantzig GB (1957) Discrete-variable extremum problems. Oper Res 5(2):266–288

Dobson G (1982) Worst-case analysis of greedy heuristics for integer programming with nonnegative data. Math Oper Res 7(4):515–531

Drake JH, Özcan E, Burke EK (2015) Modified choice function heuristic selection for the multidimensional knapsack problem. In: Genetic and evolutionary computing, Springer, pp 225–234

Fatima S, Bader-El-Den M (2010) Co-evolutionary hyper-heuristic method for auction based scheduling. In: Evolutionary computation (CEC), 2010 IEEE congress on, IEEE, pp 1–8

Gabaldon E, Lerida JL, Guirado F, Planes J (2014) Slowdown-guided genetic algorithm for job scheduling in federated environments. In: International conference on nature of computation and communication, Springer, pp 181–190

Hill RR, Cho YK, Moore JT (2012) Problem reduction heuristic for the 0–1 multidimensional knapsack problem. Comput Oper Res 39(1):19–26

Huston S, Puchinger J, Stuckey P (2008) The core concept for 0/1 integer programming. In: Proceedings of the fourteenth symposium on Computing: the Australasian theory-Volume 77, Australian Computer Society, Inc., pp 39–47

Kellerer H, Pferschy U, Pisinger D (2004) Introduction to NP-completeness of knapsack problems. Springer, Berlin, Heidelberg, pp 483–493

Magazine M, Oguz O (1984) A heuristic algorithm for the multidimensional zero-one knapsack problem. Eur J Oper Res 16(3):319–326

Perry T, Bader-El-Den M, Cooper S (2015) Imbalanced classification using genetically optimized cost sensitive classifiers. In: Evolutionary computation (CEC), 2015 IEEE congress on, IEEE, pp 680–687

Pirkul H (1987) A heuristic solution procedure for the multiconstraint zero? one knapsack problem. Naval Res Logist 34(2):161–172

Poli R, Koza J (2014) Genetic programming. In: Search methodologies, Springer, pp 143–185

Puchinger J, Raidl GR, Pferschy U (2006) The core concept for the multidimensional knapsack problem. Springer, Berlin

Rasheed K (1999) Guided crossover: a new operator for genetic algorithm based optimization. In: Evolutionary computation, 1999. CEC 99. Proceedings of the 1999 congress on, IEEE, vol 2, pp 1535–1541

Rezoug A, Boughaci D (2016) A self-adaptive harmony search combined with a stochastic local search for the 0–1 multidimensional knapsack problem. Int J Bio Inspired Comput 8(4):234–239

Rezoug A, Boughaci D, Bader-El-Den M (2015) Memetic algorithm for solving the 0-1 multidimensional knapsack problem. In: Portuguese conference on artificial intelligence, Springer, pp 298–304

Senju S, Toyoda Y (1968) An approach to linear programming with 0-1 variables. Management Science pp B196–B207

Snášel V, Dvorskỳ J, Ochodková E, Krömer P, Platoš J, Abraham A (2010) Evolving quasigroups by genetic algorithms. In: Proceedings of DATESO, Citeseer, pp 108–117

Sudholt D (2015) Parallel evolutionary algorithms. In: Springer handbook of computational intelligence, Springer, pp 929–959

Tang KS, Man KF, Kwong S, He Q (1996) Genetic algorithms and their applications. IEEE Signal Process Mag 13(6):22–37

Vázquez-Barreiros B, Mucientes M, Lama M (2014) A genetic algorithm for process discovery guided by completeness, precision and simplicity. In: International conference on business process management, Springer, pp 118–133

Volgenant A, Zoon J (1990) An improved heuristic for multidimensional 0–1 knapsack problems. J Oper Res Soc 41(10):963–970

Yang S, Jat SN (2011) Genetic algorithms with guided and local search strategies for university course timetabling. IEEE Trans Syst Man Cybern Part C (Appl Rev) 41(1):93–106

Zhang Q, Sun J, Tsang E (2005) An evolutionary algorithm with guided mutation for the maximum clique problem. IEEE Trans Evolut Comput 9(2):192–200