1. Affenzeller, M., Wagner, S., Winkler, S.: Genetic algorithms and genetic programming: modern concepts and practical applications, vol. 6. Chapman & Hall/CRC (2009)

   Google Scholar 

2. Albayrak, M., Allahverdi, N.: Development a new mutation operator to solve the traveling salesman problem by aid of genetic algorithms. Expert Systems with Applications 38(3), 1313–1320 (2011)

   Article  Google Scholar 

3. Anbuudayasankar, S., Ganesh, K., Lenny Koh, S., Ducq, Y.: Modified savings heuristics and genetic algorithm for bi-objective vehicle routing problem with forced backhauls. Expert Systems with Applications 39(3), 2296–2305 (2012)

   Article  Google Scholar 

4. Bae, J., Rathinam, S.: Approximation algorithms for multiple terminal, hamiltonian path problems. Optimization Letters 6(1), 69–85 (2012)

   Article MathSciNet MATH  Google Scholar 

5. Bianchessi, N., Righini, G.: Heuristic algorithms for the vehicle routing problem with simultaneous pick-up and delivery. Computers & Operations Research 34(2), 578–594 (2007)

   Article MATH  Google Scholar 

6. Davis, L.: Applying adaptive algorithms to epistatic domains. In: Proceedings of the International Joint Conference on Artificial Intelligence, vol. 1, pp. 161–163 (1985)

   Google Scholar 

7. Davis, L.: Adapting operator probabilities in genetic algorithms. In: Proceeding of the Third International Conference on Genetic Algorithms, pp. 61–69 (1989)

   Google Scholar 

8. De Jong, K.: Analysis of the behavior of a class of genetic adaptive systems. PhD thesis, University of Michigan, Michigan, USA (1975)

   Google Scholar 

9. Fernandez-Prieto, J., Gadeo-Martos, M., Velasco, J.R., et al.: Optimisation of control parameters for genetic algorithms to test computer networks under realistic traffic loads. Applied Soft Computing 11(4), 3744–3752 (2011)

   Article  Google Scholar 

10. Gajpal, Y., Abad, P.: Multi-ant colony system (macs) for a vehicle routing problem with backhauls. European Journal of Operational Research 196(1), 102–117 (2009)

    Article MATH  Google Scholar 

11. Gao, J., Gen, M., Sun, L., Zhao, X.: A hybrid of genetic algorithm and bottleneck shifting for multiobjective flexible job shop scheduling problems. Computers & Industrial Engineering 53(1), 149–162 (2007)

    Article MATH  Google Scholar 

12. Goldberg, D.: Genetic algorithms in search, optimization, and machine learning. Addison-Wesley Professional (1989)

    Google Scholar 

13. Golden, B., Baker, E., Alfaro, J., Schaffer, J.: The vehicle routing problem with backhauling: two approaches. In: Proceedings of the Twenty-first Annual Meeting of SE TIMS, South Carolina, USA, pp. 90–92 (1985)

    Google Scholar 

14. Grefenstette, J.J.: Optimization of control parameters for genetic algorithms. IEEE Transactions on Systems, Man and Cybernetics 16(1), 122–128 (1986)

    Article  Google Scholar 

15. Harvey, I.: The microbial genetic algorithm. In: Kampis, G., Karsai, I., Szathmáry, E. (eds.) ECAL 2009, Part II. LNCS, vol. 5778, pp. 126–133. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

16. Holland, J.H.: Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT Press (1975)

    Google Scholar 

17. Laporte, G.: The vehicle routing problem: An overview of exact and approximate algorithms. European Journal of Operational Research 59(3), 345–358 (1992)

    Article MATH  Google Scholar 

18. Lawler, E., Lenstra, J., Kan, A., Shmoys, D.: The traveling salesman problem: a guided tour of combinatorial optimization, vol. 3. Wiley, New York (1985)

    MATH  Google Scholar 

19. Li, W., Shi, Y.: On the maximum tsp withγ-parameterized triangle inequality. Optimization Letters 6(3), 415–420 (2012)

    Article MathSciNet MATH  Google Scholar 

20. Liefooghe, A., Humeau, J., Mesmoudi, S., Jourdan, L., Talbi, E.: On dominance-based multiobjective local search: design, implementation and experimental analysis on scheduling and traveling salesman problems. Journal of Heuristics 18(2), 317–352 (2012)

    Article  Google Scholar 

21. Lin, S.: Computer solutions of the traveling salesman problem. Bell System Technical Journal 44(10), 2245–2269 (1965)

    Article MathSciNet MATH  Google Scholar 

22. Martínez-Torres, M.: A genetic search of patterns of behaviour in oss communities. Expert Systems with Applications 39(18), 13,182–13,192 (2012)

    Google Scholar 

23. Mattos Ribeiro, G., Laporte, G.: An adaptive large neighborhood search heuristic for the cumulative capacitated vehicle routing problem. Computers & Operations Research 39(3), 728–735 (2012)

    Article MathSciNet MATH  Google Scholar 

24. Moon, I., Lee, J.H., Seong, J.: Vehicle routing problem with time windows considering overtime and outsourcing vehicles. Expert Systems with Applications 39(18), 13,202–13,213 (2012)

    Google Scholar 

25. Mukherjee, S., Ganguly, S., Das, S.: A strategy adaptive genetic algorithm for solving the travelling salesman problem. In: Panigrahi, B.K., Das, S., Suganthan, P.N., Nanda, P.K. (eds.) SEMCCO 2012. LNCS, vol. 7677, pp. 778–784. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

26. Ngueveu, S., Prins, C., Wolfler Calvo, R.: An effective memetic algorithm for the cumulative capacitated vehicle routing problem. Computers & Operations Research 37(11), 1877–1885 (2010)

    Article MathSciNet MATH  Google Scholar 

27. Nikolić, M., Teodorović, D.: Empirical study of the bee colony optimization (bco) algorithm. Expert Systems with Applications 40(1), 4609–4620 (2013)

    Article  Google Scholar 

28. Osaba, E., Carballedo, R., Diaz, F., Perallos, A.: Analysis of the suitability of using blind crossover operators in genetic algorithms for solving routing problems. In: Proceedings of the 8th International Symposium on Applied Computational Intelligence and Informatics, pp. 17–23. IEEE (2013)

    Google Scholar 

29. Prins, C.: A simple and effective evolutionary algorithm for the vehicle routing problem. Computers & Operations Research 31(12), 1985–2002 (2004)

    Article MathSciNet MATH  Google Scholar 

30. Ray, S., Bandyopadhyay, S., Pal, S.: New operators of genetic algorithms for traveling salesman problem. In: Proceedings of the 17th International Conference on Pattern Recognition, vol. 2, pp. 497–500. IEEE (2004)

    Google Scholar 

31. Reinelt, G.: Tsplib: A traveling salesman problem library. ORSA Journal on Computing 3(4), 376–384 (1991)

    Article MATH  Google Scholar 

32. Rocha, M., Sousa, P., Cortez, P., Rio, M.: Quality of service constrained routing optimization using evolutionary computation. Applied Soft Computing 11(1), 356–364 (2011)

    Article  Google Scholar 

33. Sarin, S.C., Sherali, H.D., Yao, L.: New formulation for the high multiplicity asymmetric traveling salesman problem with application to the chesapeake problem. Optimization Letters 5(2), 259–272 (2011)

    Article MathSciNet MATH  Google Scholar 

34. Schaffer, J.D., Morishima, A.: An adaptive crossover distribution mechanism for genetic algorithms. In: Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and Their Application, pp. 36–40. L. Erlbaum Associates Inc. (1987)

    Google Scholar 

35. Sharma, S., Gupta, K.: Solving the traveling salesmen problem through genetic algorithm with new variation order crossover. In: International Conference on Emerging Trends in Networks and Computer Communications, pp. 274–276. IEEE (2011)

    Google Scholar 

36. Spears, W.M.: Adapting crossover in evolutionary algorithms. In: Proceedings of the Conference on Evolutionary Programming, pp. 367–384 (1995)

    Google Scholar 

37. Srinivas, M., Patnaik, L.M.: Adaptive probabilities of crossover and mutation in genetic algorithms. IEEE Transactions on Systems, Man and Cybernetics 24(4), 656–667 (1994)

    Article  Google Scholar 

38. Syswerda, G.: Schedule optimization using genetic algorithms. In: Handbook of Genetic Algorithms, pp. 332–349 (1991)

    Google Scholar 

39. Tarantilis, C., Kiranoudis, C.: A flexible adaptive memory-based algorithm for real-life transportation operations: Two case studies from dairy and construction sector. European Journal of Operational Research 179(3), 806–822 (2007)

    Article MATH  Google Scholar 

40. Vafaee, F., Nelson, P.C.: A genetic algorithm that incorporates an adaptive mutation based on an evolutionary model. In: Proceedings of the International Conference on Machine Learning and Applications, pp. 101–107. IEEE (2009)

    Google Scholar 

41. Wang, C., Zhang, J., Yang, J., Hu, C., Liu, J.: A modified particle swarm optimization algorithm and its application for solving traveling salesman problem. In: Proceedings of the International Conference on Neural Networks and Brain, vol. 2, pp. 689–694. IEEE (2005)

    Google Scholar 

42. Wang, L., Tang, D.: An improved adaptive genetic algorithm based on hormone modulation mechanism for job-shop scheduling problem. Expert Systems with Applications 38(6), 7243–7250 (2011)

    Article  Google Scholar 

43. Xu, P., Zheng, J., Chen, H., Liu, P.: Optimal design of high pressure hydrogen storage vessel using an adaptive genetic algorithm. International Journal of Hydrogen Energy 35(7), 2840–2846 (2010)

    Article  Google Scholar 

44. Ye, Z., Li, Z., Xie, M.: Some improvements on adaptive genetic algorithms for reliability-related applications. Reliability Engineering & System Safety 95(2), 120–126 (2010)

    Article  Google Scholar 

45. Zhang, J., Chung, H.S., Zhong, J.: Adaptive crossover and mutation in genetic algorithms based on clustering technique. In: Proceedings of the Conference on Genetic and Evolutionary Computation, pp. 1577–1578. ACM (2005)

    Google Scholar 

46. Zhang, J., Chung, H.S., Lo, W.L.: Clustering-based adaptive crossover and mutation probabilities for genetic algorithms. IEEE Transactions on Evolutionary Computation 11(3), 326–335 (2007)

    Article  Google Scholar 

Download references