Evolutionary Computation for Real-World Problems

  • Mohammad Reza BonyadiEmail author
  • Zbigniew Michalewicz
Part of the Studies in Computational Intelligence book series (SCI, volume 605)


In this paper we discuss three topics that are present in the area of real-world optimization, but are often neglected in academic research in evolutionary computation community. First, problems that are a combination of several interacting sub-problems (so-called multi-component problems) are common in many real-world applications and they deserve better attention of research community. Second, research on optimisation algorithms that focus the search on the edges of feasible regions of the search space is important as high quality solutions usually are the boundary points between feasible and infeasible parts of the search space in many real-world problems. Third, finding bottlenecks and best possible investment in real-world processes are important topics that are also of interest in real-world optimization. In this chapter we discuss application opportunities for evolutionary computation methods in these three areas.


Integer Linear Program Travel Salesman Problem Travel Salesman Problem Knapsack Problem Vehicle Rout Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Ackoff RL (1979) The future of operational research is past. J Oper Res Soc 53(3):93–104. ISSN 0160–5682Google Scholar
  2. 2.
    Auger A, Doerr B (2011) Theory of randomized search heuristics: foundations and recent developments, vol 1. World Scientific. ISBN 9814282669Google Scholar
  3. 3.
    Bertsimas D, Brown DB, Caramanis C (2011) Theory and applications of robust optimization. SIAM Rev 53(3):464–501. ISSN 0036–1445Google Scholar
  4. 4.
    Bonyadi MR, Michalewicz Z (2014) Locating potentially disjoint feasible regions of a search space with a particle swarm optimizer, book section to appear. Springer, New YorkGoogle Scholar
  5. 5.
    Bonyadi MR, Michalewicz Z (2014) On the edge of feasibility: a case study of the particle swarm optimizer. In: Congress on evolutionary computation, IEEE, pp 3059–3066Google Scholar
  6. 6.
    Bonyadi MR, Li X, Michalewicz Z (2013) A hybrid particle swarm with velocity mutation for constraint optimization problems. In: Genetic and evolutionary computation conference, ACM, pp 1–8. doi: 10.1145/2463372.2463378
  7. 7.
    Bonyadi MR, Michalewicz Z, Barone L (2013) The travelling thief problem: the first step in the transition from theoretical problems to realistic problems. In: Congress on evolutionary computation, IEEEGoogle Scholar
  8. 8.
    Bonyadi MR, Li X, Michalewicz Z (2014) A hybrid particle swarm with a time-adaptive topology for constrained optimization. Swarm Evol Comput 18:22–37. doi: 10.1016/j.swevo.2014.06.001
  9. 9.
    Bonyadi MR, Michalewicz Z, Neumann F, Wagner M (2014) Evolutionary computation for multi-component problems: opportunities and future directions. Frontiers in Robotics and AI, Computational Intelligence, under review, 2014Google Scholar
  10. 10.
    Bonyadi MR, Michalewicz Z, Przybyek MR, Wierzbicki A (2014) Socially inspired algorithms for the travelling thief problem. In: Genetic and evolutionary computation conference (GECCO), ACMGoogle Scholar
  11. 11.
    Bonyadi MR, Michalewicz Z, Wagner M (2014) Beyond the edge of feasibility: analysis of bottlenecks. In: International conference on simulated evolution and learning (SEAL), volume To appear, SpringerGoogle Scholar
  12. 12.
    Charnes A, Cooper WW (1957) Management models and industrial applications of linear programming. Manag Sci 4(1):38–91. ISSN 0025–1909Google Scholar
  13. 13.
    Chatterjee A, Mukherjee S (2006) Unified concept of bottleneck. Report, Indian Institute of Management Ahmedabad, Research and Publication DepartmentGoogle Scholar
  14. 14.
    Cho S, Kim S (1992) Average shadow prices in mathematical programming. J Optim Theory Appl 74(1):57–74MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Crema A (1995) Average shadow price in a mixed integer linear programming problem. Eur J Oper Res 85(3):625–635. ISSN 0377–2217Google Scholar
  16. 16.
    Frieze A (1975) Bottleneck linear programming. Oper Res Q 26(4):871–874Google Scholar
  17. 17.
    Goldratt EM (1990) Theory of constraints. North River, Croton-on-HudsonGoogle Scholar
  18. 18.
    Goldratt EM, Cox J (1993) The goal: a process of ongoing improvement. Gower, AldershotGoogle Scholar
  19. 19.
    Heywood MI, Lichodzijewski P (2010) Symbiogenesis as a mechanism for building complex adaptive systems: a review. In: Applications of evolutionary computation, Springer, pp 51–60Google Scholar
  20. 20.
    Hillis WD (1990) Co-evolving parasites improve simulated evolution as an optimization procedure. Phys D: Nonlinear Phenom 42(1):228–234. ISSN 0167–2789Google Scholar
  21. 21.
    Jacob Stolk AMZM, Mann I (2013) Combining vehicle routing and packing for optimal delivery schedules of water tanks. OR Insight 26(3):167190. doi: 10.1057/ori.2013.1
  22. 22.
    Jin Y, Branke J (2005) Evolutionary optimization in uncertain environments-a survey. IEEE Trans Evol Comput 9(3):303–317. ISSN 1089–778XGoogle Scholar
  23. 23.
    Keane A (1994) Genetic algoritm digest.
  24. 24.
    Keen PG (1981) Value analysis: justifying decision support systems. MIS Q 5:1–15. ISSN 0276–7783Google Scholar
  25. 25.
    Kim S, Cho S-C (1988) A shadow price in integer programming for management decision. Eur J Oper Res 37(3):328–335. ISSN 0377–2217Google Scholar
  26. 26.
    Koopmans TC (1977) Concepts of optimality and their uses. Am Econ Rev 67:261–274. ISSN 0002–8282Google Scholar
  27. 27.
    Lau HC, Song Y (2002) Combining two heuristics to solve a supply chain optimization problem. Eur Conf Artif Intell 15:581–585Google Scholar
  28. 28.
    Leguizamon G, Coello CAC (2009) Boundary search for constrained numerical optimization problems with an algorithm inspired by the ant colony metaphor. IEEE Trans Evol Comput 13(2):350–368. ISSN 1089–778XGoogle Scholar
  29. 29.
    Li X, Bonyadi MR, Michalewicz Z, Barone L (2013) Solving a real-world wheat blending problem using a hybrid evolutionary algorithm. In: Congress on evolutionary computation, IEEE, pp 2665–2671. ISBN 1479904538Google Scholar
  30. 30.
    Luebbe R, Finch B (1992) Theory of constraints and linear programming: a comparison. Int J Prod Res 30(6):1471–1478. ISSN 0020–7543Google Scholar
  31. 31.
    Maksud Ibrahimov SSZM, Mohais A (2012) Evolutionary approaches for supply chain optimisation part 1. Int J Intell Comput Cybern 5(4):444–472Google Scholar
  32. 32.
    Maksud Ibrahimov SSZM, Mohais A (2012) Evolutionary approaches for supply chain optimisation part 2. Int J Intell Comput Cybern 5(4):473–499Google Scholar
  33. 33.
    Martello S, Toth P (1990) Knapsack problems: algorithms and computer implementations. Wiley, ChichesterzbMATHGoogle Scholar
  34. 34.
    Mersmann O, Bischl B, Trautmann H, Wagner M, Bossek J, Neumann F (2013) A novel feature-based approach to characterize algorithm performance for the traveling salesperson problem. Ann Math Artif Intell 1–32. ISSN 1012–2443Google Scholar
  35. 35.
    Michalewicz Z (1992) Genetic algorithms + data structures = evolution programs. Springer. ISBN 3540606769Google Scholar
  36. 36.
    Michalewicz Z (2012) Quo vadis, evolutionary computation? Adv Comput Intell 98–121Google Scholar
  37. 37.
    Michalewicz Z (2012) Ubiquity symposium: evolutionary computation and the processes of life: the emperor is naked: evolutionary algorithms for real-world applications. Ubiquity, 2012(November):3Google Scholar
  38. 38.
    Michalewicz Z, Fogel D (2004) How to solve it: modern heuristics. Springer, New York. ISBN 3540224947Google Scholar
  39. 39.
    Michalewicz Z, Schoenauer M (1996) Evolutionary algorithms for constrained parameter optimization problems. Evol Comput 4(1):1–32. ISSN 1063–6560Google Scholar
  40. 40.
    Michalewicz Z, Nazhiyath G, Michalewicz M (1996) A note on usefulness of geometrical crossover for numerical optimization problems. In: Fifth annual conference on evolutionary programming, Citeseer, p 305312Google Scholar
  41. 41.
    Nallaperuma S, Wagner M, Neumann F, Bischl B, Mersmann O, Trautmann H (2013) A feature-based comparison of local search and the christofides algorithm for the travelling salesperson problem. In: Proceedings of the twelfth workshop on foundations of genetic algorithms XII, ACM, pp 147–160. ISBN 1450319904Google Scholar
  42. 42.
    Neumann F, Witt C (2012) Bioinspired computation in combinatorial optimization: algorithms and their computational complexity. In: Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference companion, ACM, pp 1035–1058. ISBN 1450311784Google Scholar
  43. 43.
    Nguyen T, Yao X (2012) Continuous dynamic constrained optimisation-the challenges. IEEE Trans Evol Comput 16(6):769–786. ISSN 1089–778XGoogle Scholar
  44. 44.
    Polyakovskiy S, Bonyadi MR, Wagner M, Michalewicz Z, Neumann F (2014) A comprehensive benchmark set and heuristics for the travelling thief problem. In: Genetic and evolutionary computation conference (GECCO), ACM. ISBN 978-1-4503-2662-9/14/07. doi: 10.1145/2576768.2598249
  45. 45.
    Potter M, De Jong K (1994) A cooperative coevolutionary approach to function optimization. In: Parallel problem solving from nature, Springer, Berlin Heidelberg, pp 249–257. doi: 10.1007/3-540-58484-6269
  46. 46.
    Rahman S-U (1998) Theory of constraints: a review of the philosophy and its applications. Int J Oper Prod Manage 18(4):336–355. ISSN 0144–3577Google Scholar
  47. 47.
    Rosin CD, Belew RK (1995) Methods for competitive co-evolution: finding opponents worth beating. In: ICGA, pp 373–381Google Scholar
  48. 48.
    Runarsson T, Yao X (2000) Stochastic ranking for constrained evolutionary optimization. IEEE Trans Evol Comput 4(3):284–294. ISSN 1089–778XGoogle Scholar
  49. 49.
    Schoenauer M, Michalewicz Z (1996) Evolutionary computation at the edge of feasibility. In: Parallel problem solving from nature PPSN IV, pp 245–254Google Scholar
  50. 50.
    Schoenauer M, Michalewicz Z (1997) Boundary operators for constrained parameter optimization problems. In: ICGA, pp 322–32Google Scholar
  51. 51.
    Smith-Miles K, van Hemert J, Lim XY (2010) Understanding TSP difficulty by learning from evolved instances, Springer, pp 266–280. ISBN 3642137997Google Scholar
  52. 52.
    Smith-Miles K, Baatar D, Wreford B, Lewis R (2014) Towards objective measures of algorithm performance across instance space. Comput Oper Res 45:12–24. ISSN 0305–0548Google Scholar
  53. 53.
    Weise T, Zapf M, Chiong R, Nebro A (2009) Why is optimization difficult? Nature-inspired algorithms for optimisation, pp 1–50Google Scholar
  54. 54.
    Wu ZY, Simpson AR (2002) A self-adaptive boundary search genetic algorithm and its application to water distribution systems. J Hydraul Res 40(2):191–203. ISSN 0022–1686Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Mohammad Reza Bonyadi
    • 1
    Email author
  • Zbigniew Michalewicz
    • 1
    • 2
    • 3
    • 4
  1. 1.Optimisation and LogisticsThe University of AdelaideAdelaideAustralia
  2. 2.Institute of Computer SciencePolish Academy of SciencesWarsawPoland
  3. 3.Polish-Japanese Institute of Information TechnologyWarsawPoland
  4. 4.Chief of Science, ComplexicaAdelaideAustralia

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