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Random-Key Genetic Algorithms

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Handbook of Heuristics

Abstract

A random-key genetic algorithm is an evolutionary metaheuristic for discrete and global optimization. Each solution is encoded as an array of n random keys, where a random key is a real number, randomly generated, in the continuous interval [0, 1). A decoder maps each array of random keys to a solution of the optimization problem being solved and computes its cost. The algorithm starts with a population of p arrays of random keys. At each iteration, the arrays are partitioned into two sets, a smaller set of high-valued elite solutions and the remaining nonelite solutions. All elite elements are copied, without change, to the next population. A small number of random-key arrays (the mutants) are added to the population of the next iteration. The remaining elements of the population of the next iteration are generated by combining, with the parametrized uniform crossover of Spears and DeJong (On the virtues of parameterized uniform crossover. In: Proceedings of the fourth international conference on genetic algorithms, San Mateo, pp 230–236, 1991), pairs of arrays. This chapter reviews random-key genetic algorithms and describes an effective variant called biased random-key genetic algorithms.

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References

  1. Aiex RM, Resende MGC, Ribeiro CC (2007) TTTPLOTS: a perl program to create time-to-target plots. Optim Lett 1:355–366

    Google Scholar 

  2. Andrade DV, Buriol LS, Resende MGC, Thorup M (2006) Survivable composite-link IP network design with OSPF routing. In: Proceedings of the eighth INFORMS telecommunications conference, Dallas

    Google Scholar 

  3. Andrade CE, Miyazawa FK, Resende MGC (2013) Evolutionary algorithm for the k-interconnected multi-depot multi-traveling salesmen problem. In: Proceedings of genetic and evolutionary computation conference (GECCO). ACM, Amsterdam

    Google Scholar 

  4. Andrade CE, Miyazawa FK, Resende MGC, Toso RF (2013) Biased random-key genetic algorithms for the winner determination problem in combinatorial auctions. Technical report, AT&T Labs Research, Florham Park

    Google Scholar 

  5. Andrade CE, Resende MGC, Karloff HJ, Miyazawa FK (2014) Evolutionary algorithms for overlapping correlation clustering. In: Proceedings of genetic and evolutionary computation conference (GECCO’14), Vancouver, pp 405–412

    Google Scholar 

  6. Bean JC (1994) Genetic algorithms and random keys for sequencing and optimization. ORSA J Comput 6:154–160

    Google Scholar 

  7. Breslau L, Diakonikolas I, Duffield N, Gu Y, Hajiaghayi M, Johnson DS, Karloff H, Resende MGC, Sen S (2011) Disjoint-path facility location: theory and practice. In: Proceedings of the thirteenth workshop of algorithm engineering and experiments (ALENEX11), San Francisco, pp 68–74

    Google Scholar 

  8. Buriol LS, Resende MGC, Ribeiro CC, Thorup M (2005) A hybrid genetic algorithm for the weight setting problem in OSPF/IS-IS routing. Networks 46:36–56

    Google Scholar 

  9. Buriol LS, Resende MGC, Thorup M (2007) Survivable IP network design with OSPF routing. Networks 49:51–64

    Google Scholar 

  10. Buriol LS, Hirsch MJ, Querido T, Pardalos PM, Resende MGC, Ritt M (2010) A biased random-key genetic algorithm for road congestion minimization. Optim Lett 4:619–633

    Google Scholar 

  11. Chan FTS, Tibrewal RK, Prakash A, Tiwari MK (2015) A biased random key genetic algorithm approach for inventory-based multi-item lot-sizing problem. Proc Inst Mech Eng Part B J Eng Manuf 229(1):157–171

    Google Scholar 

  12. Coco AA, Noronha TF, Santos AC (2012) A biased random-key genetic algorithm for the robust shortest path problem. In: Proceedings of global optimization workshop (GO2012), Natal, pp 53–56

    Google Scholar 

  13. Coco AA, Abreu JCA Jr, Noronha TF, Santos AC (2014) An integer linear programming formulation and heuristics for the minmax relative regret robust shortest path problem. J Glob Optim 60(2):265–287

    Google Scholar 

  14. Duarte A, Martí R, Resende MGC, Silva RMA (2014) Improved heuristics for the regenerator location problem. Int Trans Oper Res 21:541–558

    Google Scholar 

  15. Ericsson M, Resende MGC, Pardalos PM (2002) A genetic algorithm for the weight setting problem in OSPF routing. J Comb Optim 6:299–333

    Google Scholar 

  16. Festa P (2013) A biased random-key genetic algorithm for data clustering. Math Biosci 245:76–85

    Google Scholar 

  17. Festa P, Gonçalves JF, Resende MGC, Silva RMA (2010) Automatic tuning of GRASP with path-relinking heuristics with a biased random-key genetic algorithm. In: Festa P (ed) Experimental algorithms. Lecture notes in computer science, vol 6049. Springer, Berlin/Heidelberg, pp 338–349

    Google Scholar 

  18. Fontes DBMM, Gonçalves JF (2007) Heuristic solutions for general concave minimum cost network flow problems. Networks 50:67–76

    Google Scholar 

  19. Fontes DBMM, Gonçalves JF (2013) A multi-population hybrid biased random key genetic algorithm for hop-constrained trees in nonlinear cost flow networks. Optim Lett 7(6): 1303–1324

    Google Scholar 

  20. Gonçalves JF (2007) A hybrid genetic algorithm-heuristic for a two-dimensional orthogonal packing problem. Eur J Oper Res 183:1212–1229

    Google Scholar 

  21. Gonçalves JF, Almeida J (2002) A hybrid genetic algorithm for assembly line balancing. J Heuristics 8:629–642

    Google Scholar 

  22. Gonçalves JF, Beirão NC (1999) Um algoritmo genético baseado em chaves aleatórias para sequenciamento de operações. Revista Associação Portuguesa de Desenvolvimento e Investigação Operacional 19:123–137

    Google Scholar 

  23. Gonçalves JF, Resende MGC (2004) An evolutionary algorithm for manufacturing cell formation. Comput Ind Eng 47:247–273

    Google Scholar 

  24. Gonçalves JF, Resende MGC (2011) Biased random-key genetic algorithms for combinatorial optimization. J Heuristics 17:487–525

    Google Scholar 

  25. Gonçalves JF, Resende MGC (2011) A parallel multi-population genetic algorithm for a constrained two-dimensional orthogonal packing problem. J Comb Optim 22:180–201

    Google Scholar 

  26. Gonçalves JF, Resende MGC (2012) A parallel multi-population biased random-key genetic algorithm for a container loading problem. Comput Oper Res 29:179–190

    Google Scholar 

  27. Gonçalves JF, Resende MGC (2013) A biased random-key genetic algorithm for a 2D and 3D bin packing problem. Int J Prod Econ 145:500–510

    Google Scholar 

  28. Gonçalves JF, Resende MGC (2014) An extended Akers graphical minimization method with a biased random-key genetic algorithm for job-shop scheduling. Int Tran Oper Res 21:215–246

    Google Scholar 

  29. Gonçalves JF, Resende MGC (2015) A biased random-key genetic algorithm for the unequal area facility layout problem. Eur J Oper Res 246(1):86–107

    Google Scholar 

  30. Gonçalves JF, Mendes JJM, Resende MGC (2005) A hybrid genetic algorithm for the job shop scheduling problem. Eur J Oper Res 167:77–95

    Google Scholar 

  31. Gonçalves JF, Mendes JJM, Resende MGC (2008) A genetic algorithm for the resource constrained multi-project scheduling problem. Eur J Oper Res 189:1171–1190

    Google Scholar 

  32. Gonçalves JF, Resende MGC, Mendes JJM (2011) A biased random-key genetic algorithm with forward-backward improvement for the resource constrained project scheduling problem. J Heuristics 17:467–486

    Google Scholar 

  33. Gonçalves JF, Resende MGC, Costa MD (2016) A biased random-key genetic algorithm for the minimization of open stacks problem. Int Trans Oper Res 23(1–2):25–46

    Google Scholar 

  34. Gonçalves JF, Resende MGC, Toso RF (2014) An experimental comparison of biased and unbiased random-key genetic algorithms. Pesquisa Operacional 34:143–164

    Google Scholar 

  35. Goulart N, de Souza SR, Dias LGS, Noronha TF (2011) Biased random-key genetic algorithm for fiber installation in optical network optimization. In: IEEE congress on evolutionary computation (CEC 2011). IEEE, New Orleans, pp 2267–2271

    Google Scholar 

  36. Grasas A, Lourenço HR, Pessoa LS, Resende MGC, Caballé I, Barba N (2014) On the improvement of blood sample collection at clinical laboratories. BMC Health Serv Res 14:Article 12

    Google Scholar 

  37. Lalla-Ruiz E, González-Velarde JL, Melián-Batista B, Moreno-Vega JM (2014) Biased random key genetic algorithm for the tactical berth allocation problem. Appl Soft Comput 22:60–76

    Google Scholar 

  38. Marques I, Captivo ME, Vaz Pato M (2014) Scheduling elective surgeries in a portuguese hospital using a genetic heuristic. Oper Res Health Care 3:59–72

    Google Scholar 

  39. Mendes JJM, Gonçalves JF, Resende MGC (2009) A random key based genetic algorithm for the resource constrained project scheduling problem. Comput Oper Res 36:92–109

    Google Scholar 

  40. Morán-Mirabal LF, González-Velarde JL, Resende MGC (2013) Automatic tuning of GRASP with evolutionary path-relinking. In: Proceedings of hybrid metaheuristics 2013 (HM 2013). Lecture notes in computer science, vol 7919. Springer, Ischia, pp 62–77

    Google Scholar 

  41. Morán-Mirabal LF, González-Velarde JL, Resende MGC, Silva RMA (2013) Randomized heuristics for handover minimization in mobility networks. J Heuristics 19:845–880

    Google Scholar 

  42. Morán-Mirabal LF, González-Velarde JL, Resende MGC (2014) Randomized heuristics for the family traveling salesperson problem. Int Trans Oper Res 21:41–57

    Google Scholar 

  43. Moreira MCO, Ritt M, Costa AM, Chaves AA (2012) Simple heuristics for the assembly line worker assignment and balancing problem. J Heuristics 18:505–524

    Google Scholar 

  44. Noronha TF, Resende MGC, Ribeiro CC (2011) A biased random-key genetic algorithm for routing and wavelength assignment. J Glob Optim 50:503–518

    Google Scholar 

  45. OpenMP (2013) http://openmp.org/wp/. Last visted on 11 May 2013

  46. Pedrola O, Careglio D, Klinkowski M, Velasco L, Bergman K, Solé-Pareta J (2013) Metaheuristic hybridizations for the regenerator placement and dimensioning problem in sub-wavelength switching optical networks. Eur J Oper Res 224:614–624

    Google Scholar 

  47. Pedrola O, Ruiz M, Velasco L, Careglio D, González de Dios O, Comellas J (2013) A GRASP with path-relinking heuristic for the survivable IP/MPLS-over-WSON multi-layer network optimization problem. Comput Oper Res 40:3174–3187

    Google Scholar 

  48. Reis R, Ritt M, Buriol LS,, Resende MGC (2011) A biased random-key genetic algorithm for OSPF and DEFT routing to minimize network congestion. Int Trans Oper Res 18:401–423

    Google Scholar 

  49. Resende MGC (2012) Biased random-key genetic algorithms with applications in telecommunications. TOP 20:120–153

    Google Scholar 

  50. Resende MGC, Ribeiro CC (2011) Restart strategies for GRASP with path-relinking heuristics. Optim Lett 5:467–478

    Google Scholar 

  51. Resende MGC, Toso RF, Gonçalves JF, Silva RMA (2012) A biased random-key genetic algorithm for the Steiner triple covering problem. Optim Lett 6:605–619

    Google Scholar 

  52. Roque LAC, Fontes DBMM, Fontes FACC (2014) A hybrid biased random key genetic algorithm approach for the unit commitment problem. J Comb Optim 28:140–166

    Google Scholar 

  53. Ruiz M, Pedrola O, Velasco L, Careglio D, Fernández-Palacios J, Junyent G (2011) Survivable IP/MPLS-over-WSON multilayer network optimization. J Optic Commun Netw 3:629–640

    Google Scholar 

  54. Ruiz E, Albareda-Sambola M, Fernández E, Resende MGC (2013) A biased random-key genetic algorithm for the capacitated minimum spanning tree problem. Technical report, AT&T Labs Research Technical, Florham Park

    Google Scholar 

  55. Silva RMA, Resende MGC, Pardalos PM, Gonçalves JF (2012) Biased random-key genetic algorithm for bound-constrained global optimization. In: Proceedings of global optimization workshop (GO2012), Natal, pp 133–136

    Google Scholar 

  56. Silva RMA, Resende MGC, Pardalos PM (2014) Finding multiple roots of box-constrained system of nonlinear equations with a biased random-key genetic algorithm. J Glob Optim 60(2):289–306

    Google Scholar 

  57. Silva RMA, Resende MGC, Pardalos PM (2015) A Python/C++ library for bound-constrained global optimization using biased random-key genetic algorithm. J Comb Optim 30(3):710–728

    Google Scholar 

  58. Silva RMA, Resende MGC, Pardalos PM, Facó JLD (2013) Biased random-key genetic algorithm for non-linearly constrained global optimization. In: Proceedings of the 2013 IEEE congress on evolutionary computation (CEC), Cancun, pp 2201–2206

    Google Scholar 

  59. Spears WM, DeJong KA (1991) On the virtues of parameterized uniform crossover. In: Proceedings of the fourth international conference on genetic algorithms, San Mateo, pp 230–236

    Google Scholar 

  60. Stefanello F, Buriol LS, Hirsch MJ, Pardalos PM, Querido T, Resende MGC, Ritt M (2013) On the minimization of traffic congestion in road networks with tolls. Technical report, AT&T Labs Research, Florham Park

    Google Scholar 

  61. Tangpattanakul P, Jozefowiez N, Lopez P (2012) Multi-objective optimization for selecting and scheduling observations by agile earth observing satellites. In: Parallel problem solving from nature – PPSN XII. Lecture notes in computer science, vol 7492. Springer, Berlin/New York, pp 112–121

    Google Scholar 

  62. Toso RF, Resende MGC (2015) A C++ application programming interface for biased random key genetic algorithms. Optim Methods Softw 30(1):81–93

    Article  Google Scholar 

  63. Valente JMS, Gonçalves JF (2008) A genetic algorithm approach for the single machine scheduling problem with linear earliness and quadratic tardiness penalties. Comput Oper Res 35:3696–3713

    Article  MathSciNet  Google Scholar 

  64. Valente JMS, Gonçalves JF, Alves RAFS (2006) A hybrid genetic algorithm for the early/tardy scheduling problem. Asia-Pac J Oper Res 23:393–405

    Article  MathSciNet  Google Scholar 

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Acknowledgements

The first author was partially supported by funds granted by the ERDF through the program COMPETE and by the Portuguese government through the FCT – Foundation for Science and Technology, project PTDC/ EGE-GES/ 117692/ 2010.

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Authors and Affiliations

  1. INESC TEC, Porto, Portugal

    José Fernando Gonçalves

  2. Faculdade de Economia da, Universidade do Porto, Porto, Portugal

    José Fernando Gonçalves

  3. Amazon.com, Inc. and University of Washington, Seattle, WA, USA

    Mauricio G. C. Resende

Authors
  1. José Fernando Gonçalves
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  2. Mauricio G. C. Resende
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Corresponding author

Correspondence to José Fernando Gonçalves .

Editor information

Editors and Affiliations

  1. Statistics and Operations Research Department, University of Valencia, Valencia, Spain

    Rafael Martí

  2. Department of Industrial and Systems Engineering, University of Florida, Gainesville, Florida, USA

    Panos M. Pardalos

  3. Amazon.com, Inc. and University of Washington, Seattle, Washington, USA

    Mauricio G. C. Resende

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Gonçalves, J.F., Resende, M.G.C. (2018). Random-Key Genetic Algorithms. In: Martí, R., Pardalos, P., Resende, M. (eds) Handbook of Heuristics. Springer, Cham. https://doi.org/10.1007/978-3-319-07124-4_30

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