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A Gentle Introduction to Memetic Algorithms

  • Pablo Moscato
  • Carlos Cotta
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 57)

Keywords

Genetic Algorithm Schedule Problem Local Search Evolutionary Computation Travel Salesman 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.

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Bibliography

  1. [1]
    H.S. Abdinnour (1998) A hybrid heuristic for the uncapacitated hub location problem. European Journal of Operational Research, 106(2–3), 489–99.Google Scholar
  2. [2]
    C.C. Aggarwal, J.B. Orlin and R.P. Tai (1997) Optimized crossover for the independent set problem. Operations Research, 45(2), 226–234.MathSciNetGoogle Scholar
  3. [3]
    J. Aguilar and A. Colmenares (1998) Resolution of pattern recognition problems using a hybrid genetic/random neural network learning algorithm. Pattern Analysis and Applications, 1(1), 52–61.CrossRefGoogle Scholar
  4. [4]
    D. Aldous and U. Vazirani (1994) “Go with the winners” algorithms. In: Proceedings of the 35th Annual Symposium on Foundations of Computer Science. IEEE, Los Alamitos, CA, pp. 492–501.Google Scholar
  5. [5]
    A. Augugliaro, L. Dusonchet and E. Riva-Sanseverino (1998) Service restoration in compensated distribution networks using a hybrid genetic algorithm. Electric Power Systems Research, 46(1), 59–66.CrossRefGoogle Scholar
  6. [6]
    R. Axelrod and W.D. Hamilton (1981) The evolution of cooperation. Science, 211(4489), 1390–1396.MathSciNetGoogle Scholar
  7. [7]
    K. Aygun, D.S. Weile and E. Michielssen (1997) Design of multilayered periodic strip gratings by genetic algorithms. Microwave and Optical Technology Letters, 14(2), 81–85.CrossRefGoogle Scholar
  8. [8]
    T. Bäck and F. Hoffmeister (1991) Adaptive search by evolutionary algorithms. In: W. Ebeling, M. Peschel and W. Weidlich (eds.), Models of Selforganization in Complex Systems, number 64 in Mathematical Research, Akademie-Verlag, pp. 17–21.Google Scholar
  9. [9]
    Th. Bäck (1996) Evolutionary Algorithms in Theory and Practice. Oxford University Press, New York.Google Scholar
  10. [10]
    M.J. Bayley, G. Jones, P. Willett and M.P. Williamson (1998) Genfold: A genetic algorithm for folding protein structures using NMR restraints. Protein Science, 7(2), 491–499.Google Scholar
  11. [11]
    J. Beasley and P.C. Chu (1996) A genetic algorithm for the set covering problem. European Journal of Operational Research, 94(2), 393–404.CrossRefGoogle Scholar
  12. [12]
    J. Beasley and P.C. Chu (1998) A genetic algorithm for the multidimensional knapsack problem. Journal of Heuristics, 4, 63–86.Google Scholar
  13. [13]
    B. Becker and R. Drechsler (1994) Ofdd based minimization of fixed polarity Reed-Muller expressions using hybrid genetic algorithms. In: Proceedings IEEE International Conference on Computer Design: VLSI in Computers and Processor. IEEE, Los Alamitos, CA, pp. 106–110.Google Scholar
  14. [14]
    R. Berretta and P. Moscato (1999) The number partitioning problem: An open challenge for evolutionary computation? In: D. Corne, M. Dorigo and F. Glover (eds.), New Ideas in Optimization. McGraw-Hill, pp. 261–278.Google Scholar
  15. [15]
    K.D. Boese (1995) Cost versus Distance in the Traveling Salesman Problem. Technical Report TR-950018, UCLA CS Department.Google Scholar
  16. [16]
    A.H.W. Bos (1998) Aircraft conceptual design by genetic/gradient-guided optimization. Engineering Applications of Artificial Intelligence, 11(3), 377–382.CrossRefGoogle Scholar
  17. [17]
    D. Brown, C. Huntley and A. Spillane (1989) A parallel genetic heuristic for the quadratic assignment problem. In: J. Schaffer (ed.), Proceedings of the Third International Conference on Genetic Algorithms. Morgan Kaufmann, pp. 406–415.Google Scholar
  18. [18]
    T.N. Bui and B.R. Moon (1996) Genetic algorithm and graph partitioning. IEEE Transactions on Computers, 45(7), 841–855.MathSciNetGoogle Scholar
  19. [19]
    T.N. Bui and B.R. Moon (1998) GRCA: A hybrid genetic algorithm for circuit ratio-cut partitioning. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 17(3), 193–204.Google Scholar
  20. [20]
    E.K. Burke, D.G. Elliman and R.F. Weare (1995) A hybrid genetic algorithm for highly constrained timetabling problems. In: Proceedings of the Sixth International Conference on Genetic Algorithms. Morgan Kaufmann, San Francisco, CA, pp. 605–610.Google Scholar
  21. [21]
    E.K. Burke, K.S. Jackson, J.H. Kingston and R.F. Weare (1997) Automated timetabling: The state of the art. The Computer Journal, 40(9), 565–571.CrossRefGoogle Scholar
  22. [22]
    E.K. Burke and J.P. Newall (1997) A phased evolutionary approach for the timetable problem: An initial study. In: Proceedings of the ICONIP/ANZIIS/ANNES’ 97 Conference. Springer-Verlag, Dunedin, New Zealand, pp. 1038–1041.Google Scholar
  23. [23]
    E.K. Burke, J.P. Newall and R.F. Weare (1996) A memetic algorithm for university exam timetabling. In: E.K. Burke and P. Ross (eds.), The Practice and Theory of Automated Timetabling, volume 1153 of Lecture Notes in Computer Science. Springer-Verlag, pp. 241–250.Google Scholar
  24. [24]
    E.K. Burke, J.P. Newall and R.F. Weare (1998) Initialisation strategies and diversity in evolutionary timetabling. Evolutionary Computation, 6(1), 81–103.Google Scholar
  25. [25]
    E.K. Burke and A.J. Smith (1997) A memetic algorithm for the maintenance scheduling problem. In: Proceedings of the ICONIP/ANZIIS/ANNES’ 97 Conference. Springer-Verlag, Dunedin, New Zealand, pp. 469-472.Google Scholar
  26. [26]
    E.K. Burke and A.J. Smith (1999) A memetic algorithm to schedule grid maintenance. In: Proceedings of the International Conference on Computational Intelligence for Modelling Control and Automation, Vienna: Evolutionary Computation and Fuzzy Logic for Intelligent Control, Knowledge Acquisition and Information Retrieval. IOS Press 1999, pp. 122–127.Google Scholar
  27. [27]
    E.K. Burke and A.J. Smith (1999) A multi-stage approach for the thermal generator maintenance scheduling problem. In: Proceedings of the 1999 Congress on Evolutionary Computation. IEEE, Washington D.C., pp. 1085–1092.Google Scholar
  28. [28]
    S. Cadieux, N. Tanizaki and T. Okamura (1997) Time efficient and robust 3-D brain image centering and realignment using hybrid genetic algorithm. In: Proceedings of the 36th SICE Annual Conference. IEEE, pp. 1279–1284.Google Scholar
  29. [29]
    J. Carrizo, F.G. Tinetti and P. Moscato (1992) A computational ecology for the quadratic assignment problem. In: Proceedings of the 21st Meeting on Informatics and Operations Research. Buenos Aires, SADIO.Google Scholar
  30. [30]
    S. Cavalieri and P. Gaiardelli (1998) Hybrid genetic algorithms for a multiple-objective scheduling problem. Journal of Intelligent Manufacturing, 9(4), 361–367.CrossRefGoogle Scholar
  31. [31]
    N. Chaiyaratana and A.M.S. Zalzala (1999) Hybridisation of neural networks and genetic algorithms for time-optimal control. In: Proceedings of the 1999 Congress on Evolutionary Computation. IEEE, Washington D.C., pp. 389–396.Google Scholar
  32. [32]
    Jianer Chen, Iyad A. Kanj and Weijia Jia (1999) Vertex cover: further observations and further improvements. In: Proceedings of the 25th International Worksh. Graph-Theoretic Concepts in Computer Science, number 1665 in Lecture Notes in Computer Science. Springer-Verlag, pp. 313–324.Google Scholar
  33. [33]
    R. Cheng and M. Gen (1996) Parallel machine scheduling problems using memetic algorithms. In: 1996 IEEE International Conference on Systems, Man and Cybernetics. Information Intelligence and Systems, Vol. 4. IEEE, New York, NY, pp. 2665–2670.Google Scholar
  34. [34]
    R. Cheng, M. Gen and Y. Tsujimura (1999) A tutorial survey of job-shop scheduling problems using genetic algorithms, ii. hybrid genetic search strategies. Computers & Industrial Engineering, 37(1–2), 51–55.Google Scholar
  35. [35]
    R.W. Cheng and M. Gen (1997) Parallel machine scheduling problems using memetic algorithms. Computers & Industrial Engineering, 33(3–4), 761–764.Google Scholar
  36. [36]
    P.C. Chu and J. Beasley (1997) A genetic algorithm for the generalised assignment problem. Computers & Operations Research, 24, 17–23.CrossRefMathSciNetGoogle Scholar
  37. [37]
    D.E. Clark and D.R. Westhead (1996) Evolutionary algorithms in computer-aided molecular design. Journal of Computer-aided Molecular Design, 10(4), 337–358.CrossRefGoogle Scholar
  38. [38]
    H.G. Cobb and J.J. Grefenstette (1993) Genetic algorithms for tracking changing environments. In: S. Forrest (ed.), Proceedings of the Fifth International Conference on Genetic Algorithms. Morgan Kaufmann, San Mateo, CA, pp. 529–530.Google Scholar
  39. [39]
    P.E. Coll, G.A. Durán and P. Moscato (1999) On worst-case and comparative analysis as design principles for efficient recombination operators: A graph coloring case study. In: D. Corne, M. Dorigo and F. Glover (eds.), New Ideas in Optimization. McGraw-Hill, pp. 279–294.Google Scholar
  40. [40]
    D. Costa (1995) An evolutionary tabu search algorithm and the NHL scheduling problem. INFOR, 33(3), 161–178.zbMATHGoogle Scholar
  41. [41]
    D. Costa, N. Dubuis and A. Hertz (1995) Embedding of a sequential procedure within an evolutionary algorithm for coloring problems in graphs. Journal of Heuristics, 1(1), 105–128.Google Scholar
  42. [42]
    C. Cotta (1998) A study of hybridisation techniques and their application to the design of evolutionary algorithms. AI Communications, 11(3–4), 223–224.Google Scholar
  43. [43]
    C. Cotta, E. Alba and J.M. Troya (1998) Utilising dynastically optimal form a recombination in hybrid genetic algorithms. In: A.E. Eiben, Th. Bäck, M. Schoenauer and H.-P. Schwefel (eds.), Parallel Problem Solving From Nature V, number 1498 of Lecture Notes in Computer Science. Springer-Verlag, Berlin, pp. 305–314.Google Scholar
  44. [44]
    C. Cotta, E. Alba and J.M. Troya (1999) Stochastic reverse hillclimbing and iterated local search. In: Proceedings of the 1999 Congress on Evolutionary Computation. IEEE, Washington D.C., pp. 1558–1565.Google Scholar
  45. [45]
    C. Cotta and J.M. Troya (1998) A hybrid genetic algorithm for the 0–1 multiple knapsack problem. In: G.D. Smith, N.C. Steele and R.F. Albrecht (eds.), Artificial Neural Nets and Genetic Algorithms 3. Springer-Verlag, Wien New York, pp.251–255.Google Scholar
  46. [46]
    C. Cotta and J.M. Troya (2000) On the influ ence of the representation granularity in heuristic forma recombination. In: J. Carroll, E. Damiani, H. Haddad and D. Oppenheim (eds.), ACM Symposium on Applied Computing 2000. ACM Press, pp. 433–439.Google Scholar
  47. [47]
    C. Cotta and J.M. Troya (2000) Using a hybrid evolutionary A* approach for learning reactive behaviors. In: S. Cagnoni et al. (eds.), Real-World Applications of Evolutionary Computation, volume 1803 of tLecture Notes in Computer Science. Springer-Verlag, Edinburgh, pp. 347–356.Google Scholar
  48. [48]
    T. Crain, R. Bishop, W. Fowler and K. Rock (1999) Optimal interplanetary trajectory design via hybrid genetic algorithm/recursive quadratic program search. In: Ninth AAS/AIAA Space Flight Mechanics Meeting. Breckenridge CO, pp. 99–133.Google Scholar
  49. [49]
    T. Dandekar and P. Argos (1996) Identifying the tertiary fold of small proteins with different topologies from sequence and secondary structure using the genetic algorithm and extended criteria specific for strand regions. Journal of Molecular Biology, 256(3), 645–660.CrossRefGoogle Scholar
  50. [50]
    Y. Davidor and O. Ben-Kiki (1992) The interplay among the genetic algorithm operators: Information theory tools used in a holistic way. In: R. Männer and B. Manderick (eds.), Parallel Problem Solving From Nature II. Elsevier Science Publishers B.V., Amsterdam, pp. 75–84.Google Scholar
  51. [51]
    L. Davis (1991) Handbook of Genetic Algorithms. Van Nostrand Reinhold Computer Library, New York.Google Scholar
  52. [52]
    R. Dawkins (1976) The Selfish Gene. Clarendon Press, Oxford.Google Scholar
  53. [53]
    P. de Causmaecker, G. van den Berghe and E.K. Burke (1999) Using tabu search as a local heuristic in a memetic algorithm for the nurse rostering problem. In: Proceedings of the Thirteenth Conference on Quantitative Methods for Decision Making, pages abstract only, poster presentation. Brussels, Belgium.Google Scholar
  54. [54]
    P.A. de Souza, R. Garg and V.K. Garg (1998) Automation of the analysis of Mossbauer spectra. Hyperfine Interactions, 112(1–4), 275–278.Google Scholar
  55. [55]
    D.M. Deaven and K.O. Ho (1995) Molecular-geometry optimization with a genetic algorithm. Physical Review Letters, 75(2), 288–291.CrossRefGoogle Scholar
  56. [56]
    D.M. Deaven, N. Tit, J.R. Morris and K.M. Ho (1996) Structural optimization of Lennard-Jones clusters by a genetic algorithm. Chemical Physics Letters, 256(1–2), 195–200.Google Scholar
  57. [57]
    N. Dellaert and J. Jeunet (2000) Solving large unconstrained multilevel lot-sizing problems using a hybrid genetic algorithm. International Journal of Production Research, 38(5), 1083–1099.Google Scholar
  58. [58]
    J.R. Desjarlais and T.M. Handel (1995) New strategies in protein design. Current Opinion in Biotechnology, 6(4), 460–466.CrossRefGoogle Scholar
  59. [59]
    R. Doll and M.A. VanHove (1996) Global optimization in LEED structure determination using genetic algorithms. Surface Science, 355(1–3), L393–L398.Google Scholar
  60. [60]
    R. Dorne and J.K. Hao (1998) A new genetic local search algorithm for graph coloring. In: A.E. Eiben, Th. Bäck, M. Schoenauer and H.-P. Schwefel (eds.), Parallel Problem Solving From Nature V, volume 1498 of Lecture Notes in Computer Science. Springer-Verlag, Berlin, pp. 745–754.Google Scholar
  61. [61]
    R. Englemore and T. Morgan (eds.) (1988) Blackboard Systems. Addison-Wesley.Google Scholar
  62. [62]
    C. Ersoy and S.S. Panwar (1993) Topological design of interconnected LAN/MAN networks. IEEE Journal on Selected Areas in Communications, 11(8), 1172–1182.CrossRefGoogle Scholar
  63. [63]
    J. Fang and Y. Xi (1997) A rolling horizon job shop rescheduling strategy in the dynamic environment. International Journal of Advanced Manufacturing Technology, 13(3), 227–232.Google Scholar
  64. [64]
    C. Fleurent and J.A. Ferland (1997) Genetic and hybrid algorithms for graph coloring. Annals of Operations Research, 63, 437–461.Google Scholar
  65. [65]
    P.M. França, A.S. Mendes and P. Moscato (1999) Memetic algorithms to minimize tardiness on a single machine with sequence-dependent setup times. In: Proceedings of the 5th International Conference of the Decision Sciences Institute, Athens, Greece, pp. 1708–1710.Google Scholar
  66. [66]
    B. Freisleben and P. Merz (1996) A genetic local search algorithm for solving symmetric and asymmetric traveling salesman problems. In: Proceedings of the 1996 IEEE International Conference on Evolutionary Computation, Nagoya, Japan. IEEE Press, pp. 616–621.Google Scholar
  67. [67]
    B. Freisleben and P. Merz (1996) New genetic local search operators for the traveling salesman problem. In: H.-M. Voigt, W. Ebeling, I. Rechenberg and H.-P. Schwefel (eds.), Parallel Problem Solving from Nature IV, volume 1141 of Lecture Notes in Computer Science. Springer, pp. 890–900.Google Scholar
  68. [68]
    R.T. Fu, K. Esfarjani, Y. Hashi, J. Wu, X. Sun and Y. Kawazoe (1997) Surface reconstruction of Si (001) by genetic algorithm and simulated annealing method. Science Reports of The Research Institutes Tohoku University Series A-Physics Chemistry and Metallurgy, 44(1), 77–81.Google Scholar
  69. [69]
    B.L. Garcia, P. Mahey and L.J. LeBlanc (1998) Iterative improvement methods for a multiperiod network design problem. European Journal of Operational Research, 110(1), 150–165.CrossRefGoogle Scholar
  70. [70]
    M. Gen and R. Cheng (1997) Genetic Algorithms and Engineering Design. Wiley Series in Engineering Design and Automation. John Wiley & Sons (Sd).Google Scholar
  71. [71]
    M. Gen, K. Ida and L. Yinzhen (1998) Bicriteria transportation problem by hybrid genetic algorithm. Computers & Industrial Engineering, 35(1–2), 363–366.Google Scholar
  72. [72]
    A.J. Goldstein and A.B. Lesk (1975) Common feature techniques for discrete optimization. Comp. Sci. Tech. Report 27, Bell. Tel. Labs, March.Google Scholar
  73. [73]
    M. Gorges-Schleuter (1989) ASPARAGOS: An asynchronous parallel genetic optimization strategy. In: J. David Schaffer (ed.), Proceedings of the Third International Conference on Genetic Algorithms. Morgan Kaufmann Publishers, pp. 422–427.Google Scholar
  74. [74]
    M. Gorges-Schleuter (1991) Explicit parallelism of genetic algorithms through population structures. In: H.-P. Schwefel and R. Manner (eds.), Parallel Problem Solving from Nature. Springer-Verlag, pp. 150–159.Google Scholar
  75. [75]
    M. Gorges-Schleuter (1991) Genetic Algorithms and Population Structures—A Massively Parallel Algorithm. PhD thesis, University of Dortmund, Germany.Google Scholar
  76. [76]
    M. Gorges-Schleuter (1997) Asparagos96 and the Traveling Salesman Problem. In: T. Baeck, Z. Michalewicz and X. Yao (eds.), Proceedings of the 1997 IEEE International Conference on Evolutionary Computation. Indianapolis, USA. IEEE Press, pp. 171–174.Google Scholar
  77. [77]
    J. Gottlieb (2000) Permutation-based evolutionary algorithms for multidimensional knapsack problems. In: J. Carroll, E. Damiani, H. Haddad and D. Oppenheim (eds.), ACM Symposium on Applied Computing 2000. ACM Press, pp. 408–114.Google Scholar
  78. [78]
    J. Gottlieb and T. Kruse (2000) Selection in evolutionary algorithms for the traveling salesman problem. In: J. Carroll, E. Damiani, H. Haddad and D. Oppenheim (eds.), ACM Symposium on Applied Computing 2000. ACM Press, pp. 415–421.Google Scholar
  79. [79]
    J. J. Grefenstette (1987) Incorporating Problem Specific Knowledge into Genetic Algorithms. In: L. Davis (ed.), Genetic Algorithms and Simulated Annealing, Research Notes in Artificial Intelligence. Morgan Kaufmann Publishers, pp. 42–60.Google Scholar
  80. [80]
    P. Grim (1997) The undecidability of the spatialized prisoner’s dilemma. Theory and Decision, 42(1), 53–80.CrossRefzbMATHMathSciNetGoogle Scholar
  81. [81]
    J.B. Grimbleby (1999) Hybrid genetic algorithms for analogue network synthesis. In: Proceedings of the 1999 Congress on Evolutionary Computation, IEEE, Washington D.C., pp. 1781–1787.Google Scholar
  82. [82]
    J.R. Gunn (1997) Sampling protein conformations using segment libraries and a genetic algorithm. Journal of Chemical Physics, 106(10), 4270–4281.CrossRefGoogle Scholar
  83. [83]
    M. Guotian and L. Changhong (1999) Optimal design of the broadband stepped impedance transformer based on the hybrid genetic algorithm. Journal of Xidian University, 26(1), 8–12.Google Scholar
  84. [84]
    O.C.L. Haas, K.J. Burnham and J.A. Mills (1998) Optimization of beam orientation in radiotherapy using planar geometry. Physics in Medicine and Biology, 43(8), 2179–2193.CrossRefGoogle Scholar
  85. [85]
    O.C.L. Haas, K.J. Burnham, J.A. Mills, C.R. Reeves and M.H. Fisher (1996) Hybrid genetic algorithms applied to beam orientation in radiotherapy. In: Fourth European Congress on Intelligent Techniques and Soft Computing Proceedings, Vol. 3. Verlag Mainz, Aachen, Germany, pp. 2050–2055.Google Scholar
  86. [86]
    A.B. Hadj-Alouane, J.C. Bean and K.G. Murty (1999) A hybrid genetic/optimization algorithm for a task allocation problem. Journal of Scheduling, 2(4).Google Scholar
  87. [87]
    S.P. Harris and E.G. Ifeachor (1998) Automatic design of frequency sampling filters by hybrid genetic algorithm techniques. IEEE Transactions on Signal Processing, 46(12), 3304–3314.CrossRefGoogle Scholar
  88. [88]
    W.E. Hart and R.K. Belew (1991) Optimizing an arbitrary function is hard for the genetic algorithm. In: R.K. Belew and L.B. Booker (eds.), Proceedings of the Fourth International Conference on Genetic Algorithms. Morgan Kaufmann, San Mateo CA, pp. 190–195.Google Scholar
  89. [89]
    B. Hartke (1993) Global geometry optimization of clusters using genetic algorithms. Journal of Physical Chemistry, 97(39), 9973–9976.CrossRefGoogle Scholar
  90. [90]
    M. Hifi (1997) A genetic algorithm-based heuristic for solving the weighted maximum independent set and some equivalent problems. Journal of the Operational Research Society, 48(6), 612–622.CrossRefzbMATHGoogle Scholar
  91. [91]
    R. Hirsch and C.C. Mullergoymann (1995) Fitting of diffusion-coefficients in a 3-compartment sustained-release drug formulation using a genetic algorithm. International Journal of Pharmaceutics, 120(2), 229–234.CrossRefGoogle Scholar
  92. [92]
    K.M. Ho, A.A. Shvartsburg, B.C. Pan, Z. Y. Lu, C.Z. Wang, J.G. Wacker, J.L. Fye and M.F. Jarrold (1998) Structures of medium-sized silicon clusters. Nature, 392(6676), 582–585.Google Scholar
  93. [93]
    S. Hobday and R. Smith (1997) Optimisation of carbon cluster geometry using a genetic algorithm. Journal of The Chemical Society-Faraday Transactions, 93(22), 3919–3926.Google Scholar
  94. [94]
    R.J.W. Hodgson (2000) Memetic algorithms and the molecular geometry optimization problem. In: Proceedings of the 2000 Congress on Evolutionary Computation. IEEE Service Center, Piscataway, NJ, pp. 625–632.Google Scholar
  95. [95]
    Reimar Hofmann (1993) Examinations on the algebra of genetic algorithms. Master’s thesis, Technische Universität München, Institut fü Informatik.Google Scholar
  96. [96]
    D.R. Hofstadter (1983) Computer tournaments of the prisoners-dilemma suggest how cooperation evolves. Scientific American, 248(5), 16–23.Google Scholar
  97. [97]
    D. Holstein and P. Muscat (1999) Memetic algorithms using guided local search: A case study. In: D. Corne, M. Dorigo and F. Glover (eds.), New Ideas in Optimization. McGraw-Hill, pp. 235–244.Google Scholar
  98. [98]
    E. Hopper and B. Turton (1999) A genetic algorithm for a 2d industrial packing problem. Computers & Industrial Engineering, 37(1–2), 375–378.Google Scholar
  99. [99]
    M. Hulin (1997) An optimal stop criterion for genetic algorithms: A bayesian approach. In: Th. Bäck (ed.), Proceedings of the Seventh International Conference on Genetic Algorithms. Morgan Kaufmann, San Mateo, CA, pp. 135–143.Google Scholar
  100. [100]
    T. Ichimura and Y. Kuriyama (1998) Learning of neural networks with parallel hybrid ga using a royal road function. In: 1998 IEEE International Joint Conference on Neural Networks, Vol. 2, IEEE, New York, NY, pp. 1131–1136.Google Scholar
  101. [101]
    J. Berger, M. Salois and R. Begin (1998) A hybrid genetic algorithm for the vehicle routing problem with time windows. In: R.E. Mercer and E. Neufeld (eds.), Advances in Artificial Intelligence. 12th Biennial Conference of the Canadian Society for Computational Studies of Intelligence. Springer-Verlag, Berlin, pp. 114–127.Google Scholar
  102. [102]
    W.R. Jih and Y.J. Hsu (1999) Dynamic vehicle routing using hybrid genetic algorithms. In: Proceedings of the 1999 Congress on Evolutionary Computation. IEEE, Washington D.C., pp. 453–458.Google Scholar
  103. [103]
    D.S. Johnson and L.A. McGeoch (1997) The traveling salesman problem: Acase study. In: E.H.L. Aarts and J.K. Lenstra (eds,), Local Search in Combinatorial Optimization. Wiley, Chichester, pp. 215–310.Google Scholar
  104. [104]
    D.S. Johnson, C.H. Papadimitriou and M. Yannakakis (1988) How easy is local search? Journal of Computers and System Sciences, 37, 79–100.MathSciNetGoogle Scholar
  105. [105]
    G. Jones, P. Willett, R.C. Glen, A.R. Leach and R. Taylor (1997) Development and validation of a genetic algorithm for flexible docking. Journal of Molecular Biology, 267(3), 727–748.CrossRefGoogle Scholar
  106. [106]
    T.C. Jones (1995) Evolutionary Algorithms, Fitness Landscapes and Search. PhD thesis, University ofNew Mexico.Google Scholar
  107. [107]
    B.M. Kariuki, H. Serrano-Gonzalez, R.L. Johnston and K.D.M. Harris (1997) The application of a genetic algorithm for solving crystal structures from powder diffraction data. Chemical Physics Letters, 280(3–4), 189–195.Google Scholar
  108. [108]
    R.M. Karp (1972) Reducibility among combinatorial problems. In: R.E. Miller and J.W. Thatcher (eds.), Complexity of Computer Computations. Plenum, New York NY, pp. 85–103.Google Scholar
  109. [109]
    I.E. Kassotakis, M.E. Markaki and A.V. Vasilakos (2000) A hybrid genetic approach for channel reuse in multiple access telecommunication networks. IEEE Journal on Selected Areas in Communications, 18(2), 234–243.CrossRefGoogle Scholar
  110. [110]
    K. Katayama, H. Hirabayashi and H. Narihisa (1998) Performance analysis for crossover operators of genetic algorithm. Transactions of the Institute of Electronics, Information and Communication Engineers, J81D-I(6), 639–650.Google Scholar
  111. [111]
    T.S. Kim and G.S. May (1999) Intelligent control of via formation by photosensitive BCB for MCM-L/D applications. IEEE Transactions on Semiconductor Manufacturing, 12, 503–515.Google Scholar
  112. [112]
    S. Kirkpatrick and G. Toulouse (1985) Configuration space analysis of traveling salesman problems. J. Physique, 46, 1277–1292.MathSciNetGoogle Scholar
  113. [113]
    K. Krishna and M. Narasimha-Murty (1999) Genetic k-means algorithm. IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 29(3), 433–439.CrossRefGoogle Scholar
  114. [114]
    K. Krishna, K.R. Ramakrishnan and M.A.L. Thathachar (1997) Vector quantization using genetic k-means algorithm for image compression. In: 1997 International Conference on Information, Communications and Signal Processing, Vol. 3, IEEE, New York, NY, pp. 1585–1587.Google Scholar
  115. [115]
    R.M. Krzanowski and J. Raper (1999) Hybrid genetic algorithm for transmitter location in wireless networks. Computers, Environment and Urban Systems, 23(5), 359–382.CrossRefGoogle Scholar
  116. [116]
    E. Lamma, L.M. Pereira and F. Riguzzi (2000) Multi-agent logic aided lamarckian learning. Technical Report DEIS-LIA-00-004, Dipartimento di Elettronica, Informatica e Sistemistica, University of Bologna (Italy), LIA Series no. 44 (submitted for publication).Google Scholar
  117. [117]
    E. Landree, C. Collazo-Davila and L.D. Marks (1997) Multi-solution genetic algorithm approach to surface structure determination using direct methods. Acta Crystallographica Section B—Structural Science, 53, 916–922.Google Scholar
  118. [118]
    G.A. Lazar, J.R. Desjarlais and T.M. Handel (1997) De novo design of the hydrophobic core ofubiquitin. Protein Science, 6(6), 1167–1178.Google Scholar
  119. [119]
    C.Y. Lee (1994) Genetic algorithms for single machine job scheduling with common due date and symmetric penalties. Journal of the Operations Research Society of Japan, 37(2), 83–95.zbMATHMathSciNetGoogle Scholar
  120. [120]
    D. Levine (1996) A parallel genetic algorithm for the set partitioning problem. In: I.H. Osman and J.P. Kelly (eds.), Meta-Heuristics: Theory & Applications. Kluwer Academic Publishers, pp. 23–35.Google Scholar
  121. [121]
    H.R. Lewis and C.H. Papadimitriou (1998) Elements of the Theory of Computation. Prentice-Hall, Inc., Upper Saddle River, New Jersey.Google Scholar
  122. [122]
    F. Li, R. Morgan and D. Williams (1996) Economic environmental dispatch made easy with hybrid genetic algorithms. In: Proceedings of the International Conference on Electrical Engineering, Vol. 2. Int. Acad. Publishers, Beijing, China, pp. 965–969.Google Scholar
  123. [123]
    L.P. Li, T.A. Darden, S.J. Freedman, B.C. Furie, B. Furie, J.D. Baleja, H. Smith, R.G. Hiskey and L.G. Pedersen (1997) Refinement of the NMR solution structure of the gamma-carboxyglutamic acid domain of coagulation factor IX using molecular dynamics simulation with initial Ca2+ positions determined by a genetic algorithm. Biochemistry, 36(8), 2132–2138.CrossRefGoogle Scholar
  124. [124]
    C.F. Liaw (2000) A hybrid genetic algorithm for the open shop scheduling problem. European Journal of Oprational Research, 124(1), 28–42.zbMATHMathSciNetGoogle Scholar
  125. [125]
    G.E. Liepins and M.R. Hilliard (1987) Greedy Genetics. In: J.J. Grefenstette (ed.), Proceedings of the Second International Conference on Genetic Algorithms and their Applications. Lawrence Erlbaum Associates, Cambridge, MA, pp. 90–99.Google Scholar
  126. [126]
    S. Lin (1965) Computer solutions of the traveling salesman problem. Bell System Technical Journal, 10, 2245–2269.Google Scholar
  127. [127]
    S. Lin and B. Kernighan (1973) An Effective Heuristic Algorithm for the Traveling Salesman Problem. Operations Research, 21, 498–516.MathSciNetGoogle Scholar
  128. [128]
    S.E. Ling (1992) Integrating genetic algorithms with a prolog assignment program as a hybrid solution for a polytechnic timetable problem. In: Parallel Problem Solvingfrom Nature II. Elsevier Science Publisher B.V., pp. 321–329.Google Scholar
  129. [129]
    D.M. Lorber and B.K. Shoichet (1998) Flexible ligand docking using conformational ensembles. Protein Science, 7(4), 938–950.Google Scholar
  130. [130]
    S.J, Louis, X. Yin and Z.Y. Yuan (1999) Multiple vehicle routing with time windows using genetic algorithms. In: Proceedings of the 1999 Congress on Evolutionary Computation. Washington D.C., pp. 1804–1808. IEEE Neural Network Council—Evolutionary Programming Society—Institution of Electrical Engineers.Google Scholar
  131. [131]
    A.L. MacKay (1995) Generalized crystallography. THEOCHEM-Journal of Molecular Structure, 336(2–3), 293–303.Google Scholar
  132. [132]
    J. Maddox (1995) Genetics helping molecular-dynamics. Nature, 376(6537), 209–209.Google Scholar
  133. [133]
    K.E. Mathias and L.D. Whitley (1994) Noisy function evaluation and the delta coding algorithm. In: Proceedings of the SPIE—The International Society for Optical Engineering, pp. 53–64.Google Scholar
  134. [134]
    A.C.W. May and M.S. Johnson (1994) Protein-structure comparisons using a combination of a genetic algorithm, dynamic-programming and least-squares minimization. Protein Engineering, 7(4), 475–485.Google Scholar
  135. [135]
    A.S. Mendes, F.M. Muller, P.M. França and P. Moscato (1999) Comparing meta-heuristic approaches for parallel machine scheduling problems with sequence-dependent setup times. In: Proceedings of the 15th International Conference on CAD/CAM Robotics & Factories of the Future, Aguas de Lindoia, Brazil.Google Scholar
  136. [136]
    L.D. Merkle, G.B. Lament, G.H. Jr. Gates and R. Pachter (1996) Hybrid genetic algorithms for minimization of a polypeptide specific energy model. In: Proceedings of 1996 IEEE International Conference on Evolutionary Computation. IEEE, New York, NY, pp. 396–400.Google Scholar
  137. [137]
    P. Merz and B. Freisleben (1997) A Genetic Local Search Approach to the Quadratic Assignment Problem. In: T. Bäck (ed.), Proceedings of the Seventh International Conference on Genetic Algorithms. Morgan Kaufmann, San Mateo, CA, pp. 465–172.Google Scholar
  138. [138]
    P. Merz and B. Freisleben (1997) Genetic local search for the TSP: new results. In: Proceedings of the 1997 IEEE International Conference on Evolutionary Computation. IEEE Press, pp. 159–164.Google Scholar
  139. [139]
    P. Merz and B. Freisleben (1998) Memetic algorithms and the fitness landscape of the graph bi-partitioning problem. In: A.E. Eiben, T. Back, M. Schoenauer and H.-P. Schwefel (eds.), Parallel Problem Solving from Nature V, volume 1498 of Lecture Notes in Computer Science. Springer-Verlag, pp. 765–774.Google Scholar
  140. [140]
    P. Merz and B. Freisleben (1998) On the effectiveness of evolutionary search in high-dimensional NK-landscapes. In: Proceedings of the 1998 IEEE International Conference on Evolutionary Computation. IEEE Press, pp. 741–745.Google Scholar
  141. [141]
    P. Merz and B. Freisleben (1999) A comparion of Memetic Algorithms, Tabu Search, and ant colonies for the quadratic assignment problem. In: Proceedings of the 1999 Congress on Evolutionary Computation. Washington D.C. IEEE Service Center, Piscataway, NJ, pp. 2063–2070.Google Scholar
  142. [142]
    P. Merz and B. Freisleben (1999) Fitness landscapes and memetic algorithm design. In: D. Corne, M. Dorigo and F. Glover (eds.), New Ideas in Optimization. McGraw-Hill, pp. 245–260.Google Scholar
  143. [143]
    P. Merz and B. Freisleben (2000) Fitness landscapes, memetic algorithms, and greedy operators for graph bipartitioning. Evolutionary Computation, 8(1), 61–91.CrossRefGoogle Scholar
  144. [144]
    J.C. Meza, R.S. Judson, T.R. Faulkner and A.M. Treasurywala (1996) A comparison of a direct search method and a genetic algorithm for conformational searching. Journal of Computational Chemistry, 17(9), 1142–1151.CrossRefGoogle Scholar
  145. [145]
    M. Mignotte, C. Collet, P. Pérez and P. Bouthemy (2000) Hybrid genetic optimization and statistical model based approach for the classification of shadow shapes in sonar imagery. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(2), 129–141.CrossRefGoogle Scholar
  146. [146]
    D.M. Miller, H.C. Chen, J. Matson and Q. Liu (1999) A hybrid genetic algorithm for the single machine scheduling problem. Journal of Heuristics, 5(4), 437–454.CrossRefGoogle Scholar
  147. [147]
    S.T. Miller, J.M. Hogle and D.J. Filman (1996) A genetic algorithm for the ab initio phasing of icosahedral viruses. Acta Crystallographica Section D—Biological Crystallography, 52, 235–251.Google Scholar
  148. [148]
    L. Min and W. Cheng (1998) Identical parallel machine scheduling problem for minimizing the makespan using genetic algorithm combined with simulated annealing. Chinese Journal of Electronics, 7(4), 317–321.Google Scholar
  149. [149]
    X.G. Ming and K.L. Mak (2000) A hybrid hopfield network-genetic algorithm approach to optimal process plan selection. International Journal of Production Research, 38(8), 1823–1839.Google Scholar
  150. [150]
    M. Minsky (1994) Negative expertise. International Journal of Expert Systems, 7(1), 13–19.MathSciNetGoogle Scholar
  151. [151]
    A. Monfroglio (1996) Hybrid genetic algorithms for timetabling. International Journal of Intelligent Systems, 11(8), 477–523.CrossRefzbMATHGoogle Scholar
  152. [152]
    A. Monfroglio (1996) Timetabling through constrained heuristic search and genetic algorithms. Software—Practice and Experience, 26(3), 251–279.Google Scholar
  153. [153]
    A. Montfroglio (1996) Hybrid genetic algorithms for a rostering problem. Software—Practice and Experience, 26(7), 851–862.Google Scholar
  154. [154]
    P. Moscato (1989) On evolution, search, optimization, genetic algorithms and martial arts: towards memetic algorithms. Technical Report Caltech Concurrent Computation Program, Report 826, California Institute of Technology, Pasadena, California, USA.Google Scholar
  155. [155]
    P. Moscato (1993) An introduction to population approaches for optimization and hierarchical objective functions: the role of tabu search. Annals of Operations Research, 41(1–4), 85–121.zbMATHGoogle Scholar
  156. [156]
    P. Moscato and M.G. Norman (1992) A Memetic Approach for the Traveling Salesman Problem Implementation of a Computational Ecology for Combinatorial Optimization on Message-Passing Systems. In: M. Valero, E. Onate, M. Jane, J.L. Larriba and B. Suarez (eds.), Parallel Computing and Transputer Applications. IOS Press, Amsterdam, pp. 177–186.Google Scholar
  157. [157]
    H. Mühlenbein (1991) Evolution in time and space—the parallel genetic algorithm. In: Gregory J.E. Rawlins (ed.), Foundations of Genetic Algorithms. Morgan Kaufmann Publishers, pp. 316–337.Google Scholar
  158. [158]
    H. Mühlenbein M. Gorges-Schleuter and O. Krämer (1988) Evolution Algorithms in Combinatorial Optimization. Parallel Computing, 7, 65–88.CrossRefGoogle Scholar
  159. [159]
    T. Murata and H. Ishibuchi (1994) Performance evaluation of genetic algorithms for flowshop scheduling problems. In: Proceedings of the First IEEE Conference on Evolutionary Computation, Vol. 2. IEEE, New York, NY, pp. 812–817.Google Scholar
  160. [160]
    T. Murata, H. Ishibuchi and H. Tanaka (1996) Genetic algorithms for flowshop scheduling problems. Computers & Industrial Engineering, 30(4), 1061–1071.Google Scholar
  161. [161]
    M. Musil, M.J. Wilmut and N.R. Chapman (1999) A hybrid simplex genetic algorithm for estimating geoacoustic parameters using matched-field inversion. IEEE Journal of Oceanic Engineering, 24(3), 358–369.CrossRefGoogle Scholar
  162. [162]
    Y. Nagata and Sh. Kobayashi (1997) Edge assembly crossover: a high-power genetic algorithm for the traveling salesman problem. In: Th. Bäck (ed.), Proceedings of the Seventh International Conference on Genetic Algorithms, East Lansing, EUA. Morgan Kaufmann, San Mateo, CA, pp. 450–457.Google Scholar
  163. [163]
    M. Nakamaru, H. Matsuda and Y. Iwasa (1998) The evolution of social interaction in lattice models. Sociological Theory and Methods, 12(2), 149–162.Google Scholar
  164. [164]
    M. Nakamaru, H. Nogami and Y. Iwasa (1998) Score-dependent fertility model for the evolution of cooperation in a lattice. Journal of Theoretical Biology, 194(1), 101–124.CrossRefGoogle Scholar
  165. [165]
    R. Niedermeier and P. Rossmanith (2000) An efficient fixed parameter algorithm for 3-hitting set. Technical Report WSI-99-18, Universität Tübingen, Wilhelm-Schickard-Institut für Informatik, 1999. Technical Report, Revised version accepted in Journal of Discrete Algorithms, August.Google Scholar
  166. [166]
    R. Niedermeier and P. Rossmanith (2000) A general method to speed up fixed-parameter-tractable algorithms. Information Processing Letters, 73, 125–129.CrossRefMathSciNetGoogle Scholar
  167. [167]
    J.A. Niesse and H.R. Mayne (1996) Global geometry optimization of atomic clusters using a modified genetic algorithm in space-fixed coordinates. Journal of Chemical Physics, 105(11), 4700–1706.CrossRefGoogle Scholar
  168. [168]
    A.L. Nordstrom and S. Tufekci (1994) A genetic algorithm for the talent scheduling problem. Computers & Operations-Research, 21(8), 927–940.Google Scholar
  169. [169]
    M.G. Norman and P. Moscato (1991) A competitive and cooperative approach to complex combinatorial search. Technical Report Caltech Concurrent Computation Program, Report. 790, California Institute of Technology, Pasadena, California, USA, 1989. expanded version published at the Proceedings of the 20th Informatics and Operations Research Meeting, Buenos Aires (20th JAIIO), August pp. 3.15–3.29.Google Scholar
  170. [170]
    A.G.N. Novaes, J.E.S. De-Cursi and O.D. Graciolli (2000) A continuous approach to the design of physical distribution systems. Computers & Operations Research, 27(9), 877–893.CrossRefGoogle Scholar
  171. [171]
    M.A. Nowak and K. Sigmund (1998) Evolution of indirect reciprocity by image scoring. Nature, 393(6685), 573–577.CrossRefGoogle Scholar
  172. [172]
    P. Osmera (1995) Hybrid and distributed genetic algorithms for motion control. In: V. Chundy and E. Kurekova (eds.), Proceedings of the Fourth International Symposium on Measurement and Control in Robotics, pp. 297–300.Google Scholar
  173. [173]
    R. Ostermark (1999) A neuro-genetic algorithm for heteroskedastic time-series processes: empirical tests on global asset returns. Soft Computing, 3(4), 206–220.Google Scholar
  174. [174]
    R. Ostermark (1999) Solving a nonlinear non-convex trim loss problem with a genetic hybrid algorithm. Computers & Operations Research, 26(6), 623–635.Google Scholar
  175. [175]
    R. Ostermark (1999) Solving irregular econometric and mathematical optimization problems with a genetic hybrid algorithm. Computational Economics, 13(2), 103–115.Google Scholar
  176. [176]
    E. Ozcan and C.K. Mohan (1998) Steady state memetic algorithm for partial shape matching. In: V.W. Porto, N. Saravanan and D. Waagen (eds.), Evolutionary Programming VII, volume 1447 of Lecture Notes in Computer Science. Springer, Berlin, pp. 527–236.Google Scholar
  177. [177]
    L. Ozdamar (1999) A genetic algorithm approach to a general category project scheduling problem. IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), 29(1), 44–59.CrossRefGoogle Scholar
  178. [178]
    M.N. Pacey, X.Z. Wang, S.J. Haake and E.A. Patterson (1999) The application of evolutionary and maximum entropy algorithms to photoelastic spectral analysis. Experimental Mechanics, 39(4), 265–273.Google Scholar
  179. [179]
    B. Paechter, A. Cumming, M.G. Norman and H. Luchian Extensions to a Memetic timetabling system. In: E.K. Burke and P. Ross (eds.), The Practice and Theory of Automated Timetabling, volume 1153 of Lecture Notes in Computer Science. Springer Verlag, pp. 251–265.Google Scholar
  180. [180]
    B. Paechter, R.C. Rankin and A. Cumming (1998) Improving a lecture timetabling system for university wide use. In: E.K. Burke and M. Carter (eds.), The Practice and Theory of Automated Timetabling II, volume 1408 of Lecture Notes in Computer Science. Springer Verlag, pp. 156–165.Google Scholar
  181. [181]
    C.H. Papadimitriou and K. Steiglitz (1982) Combinatorial Optimization: Algorithms and Complexity. Prentice-Hall, Inc., Englewood Cliffs, New Jersey.Google Scholar
  182. [182]
    M. Peinado and T. Lengauer (1997) Parallel “go with the winners algorithms” in the LogP Model. In: IEEE Computer Society Press (ed.), Proceedings of the 11th International Parallel Processing Symposium. Los Alamitos, California, pp. 656–664.Google Scholar
  183. [183]
    O.K. Pratihar, K. Deb and A. Ghosh (1999) Fuzzy-genetic algorithms andmobile robot navigation among static obstacles. In: Proceedings of the 1999 Congress on Evolutionary Computation. IEEE, Washington D.C., pp. 327–334.Google Scholar
  184. [184]
    N. Pucello, M. Rosati, G. D’Agostino, F. Pisacane, V. Rosato and M. Celino (1997) Search of molecular ground state via genetic algorithm: Implementation on a hybrid SIMD-MIMD platform. International Journal of Modern Physics C. 8(2), 239–252.Google Scholar
  185. [185]
    W.J. Pullan (1997) Structure prediction of benzene clusters using a genetic algorithm. Journal of Chemical Information and Computer Sciences, 37(6), 1189–1193.CrossRefGoogle Scholar
  186. [186]
    D. Quagliarella and A. Vicini (1998) Hybrid genetic algorithms as tools for complex optimisation problems. In: P. Blonda, M. Castellano and A. Petrosino (eds.), New Trends in Fuzzy Logic II. Proceedings of the Second Italian Workshop on Fuzzy Logic. World Scientific, Singapore, pp. 300–307.Google Scholar
  187. [187]
    N.J. Radcliffe (1994) The algebra of genetic algorithms. Annals of Mathematics and Artificial Intelligence, 10, 339–384.CrossRefzbMATHMathSciNetGoogle Scholar
  188. [188]
    N.J. Radcliffe and P.D. Surry (1994) Fitness Variance of Formae and Performance Prediction. In: L.D. Whitley and M.D. Vose (eds.), Proceedings of the Third Workshop on Foundations of Genetic Algorithms. Morgan Kaufmann, San Francisco, pp. 51–72.Google Scholar
  189. [189]
    N.J. Radcliffe and P.D. Surry (1994) Formal Memetic Algorithms. In: T. Fogarty (ed.), Evolutionary Computing: AISB Workshop, volume 865 of Lecture Notes in Computer Science. Springer-Verlag, Berlin, pp. 1–16.Google Scholar
  190. [190]
    G.R. Raidl and B.A. Julstron (2000) A weighted coding in a genetic algorithm for the degree-constrained minimum spanning tree problem. In: J. Carroll, E. Damiani, H. Haddad and D. Oppenheim (eds.), ACM Symposium on Applied Computing 2000. ACM Press, pp. 440–45.Google Scholar
  191. [191]
    E. Ramat, G. Venturini, C. Lente and M. Slimane (1997) Solving the multiple resource constrained project scheduling problem with a hybrid genetic algorithm. In: Th. Bäck (ed.), Proceedings of the Seventh International Conference on Genetic Algorithms. Morgan Kaufmann, San Francisco CA, pp. 489–496.Google Scholar
  192. [192]
    R.C. Rankin (1996) Automatic timetabling in practice. In: Practice and Theory of Automated Timetabling. First International Conference. Selected Papers. Springer-Verlag, Berlin, pp. 266–279.Google Scholar
  193. [193]
    M.L. Raymer, P.C. Sanschagrin, W.F. Punch, S. Venkataraman, E.D. Goodman and L.A. Kuhn (1997) Predicting conserved water-mediated and polar ligand interactions in proteins using a k-nearest-neighbors genetic algorithm. Journal of Molecular Biology, 265(4), 445–464.CrossRefGoogle Scholar
  194. [194]
    I. Rechenberg (1973) Evolutionsstrategie: Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. Frommann-Holzboog Verlag, Stuttgart.Google Scholar
  195. [195]
    C. Reeves (1996) Hybrid genetic algorithms for bin-packing and related problems. Annals of Operations Research, 63, 371–396.CrossRefzbMATHGoogle Scholar
  196. [196]
    C. Reich (2000) Simulation if imprecise ordinary differential equations using evolutionary algorithms. In: J. Carroll, E. Damiani, H. Haddad and D. Oppenheim (eds.), ACM Symposium on Applied Computing 2000. ACM Press, pp. 428–432.Google Scholar
  197. [197]
    M. A. Ridao, J. Riquelme, E.F. Camacho and M. Toro (1998) An evolutionary and local search algorithm for planning two manipulators motion. In: A.P. Del Pobil, J. Mira and M. Ali (eds.), Tasks and Methods in Applied Artificial Intelligence, volume 1416 of Lecture Notes in Computer Science, Springer-Verlag, Berlin Heidelberg, pp. 105–114.Google Scholar
  198. [198]
    C.F. Ruff, S.W. Hughes and D.J. Hawkes (1999) Volume estimation from sparse planar images using deformable models. Image and Vision Computing, 17(8), 559–565.CrossRefGoogle Scholar
  199. [199]
    S.M. Sait and H. Youssef (2000) VLSI Design Automation: Theory and Practice. McGraw-Hill Book Co. (copublished by IEEE), Europe.Google Scholar
  200. [200]
    A. Sakamoto, X.Z. Liu and T. Shimamoto (1997) A genetic approach for maximum independent set problems. IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences, E80A(3), 551–556.Google Scholar
  201. [201]
    V. Schnecke and O. Vornberger (1997) Hybrid genetic algorithms for constrained placement problems. IEEE Transactions on Evolutionary Computation, 1(4), 266–277.CrossRefGoogle Scholar
  202. [202]
    H.-P. Schwefel (1984) Evolution strategies: a family ofnon-linear optimization techniques based on imitating some principles of natural evolution. Annals of Operations Research, 1, 165–167.CrossRefGoogle Scholar
  203. [203]
    K. Shankland, W.I.F. David and T. Csoka (1997) Crystal structure determination from powder diffraction data by the application of a genetic algorithm. Zeitschrift Fur Kristallographie, 212(8), 550–552.Google Scholar
  204. [204]
    K. Shankland, W.I.F. David T. Csoka and L. McBride (1998) Structure solution of ibuprofen from powder diffraction data by the application of a genetic algorithm combined with prior conformational analysis. International Journal of Pharmaceutics, 165(1), 117–126.Google Scholar
  205. [205]
    D. Srinivasan, R.L. Cheu, Y.P. Poh and A.K.C. Ng (2000) Development of an intelligent technique for traffic network incident detection. Engineering Applications of Artificial Intelligence, 13(3), 311–322.CrossRefGoogle Scholar
  206. [206]
    K. Steiglitz and P. Weiner (1968) Some improved algorithms for computer solution of the traveling salesman problem. In: Proceedings of the Sixth Allerton Conference on Circuit and System Theory. Urbana, Illinois, pp. 814–821.Google Scholar
  207. [207]
    J.Y. Suh and Dirk Van Gucht (1987) Incorporating heuristic information into genetic search. In: J.J. Grefenstette (ed.), Proceedings of the Second International Conference on Genetic Algorithms and their Applications, Lawrence Erlbaum Associates. Cambridge, MA, pp. 100–107.Google Scholar
  208. [208]
    P.D. Surry and N. J. Radcliffe (1996) Inoculation to initialise evolutionary search. In: T.C. Fogarty (ed.), Evolutionary Computing: AISB Workshop, number 1143 in Lecture Notes in Computer Science. Springer-Verlag, pp. 269–285.Google Scholar
  209. [209]
    G. Syswerda (1989) Uniform crossover in genetic algorithms. In: J.D. Schaffer (ed.), Proceedings of the Third International Conference on Genetic Algorithms. Morgan Kaufmann, San Mateo, CA, pp. 2–9.Google Scholar
  210. [210]
    T. Taguchi, T. Yokota and M. Gen (1998) Reliability optimal design problem with interval coefficients using hybrid genetic algorithms. Computers & Industrial Engineering, 35(1–2), 373–376.Google Scholar
  211. [211]
    K.Y. Tam and R.G. Compton (1995) GAMATCH—a genetic algorithm-based program for indexing crystal faces. Journal of Applied Crystallography, 28, 640–645.CrossRefGoogle Scholar
  212. [212]
    A.P. Topchy, O.A. Lebedko and V.V. Miagkikh (1996) Fast learning in multi-layered networks by means of hybrid evolutionary and gradient algorithms. In: Proceedings of International Conference on Evolutionary Computation and its Applications, pp. 390–398.Google Scholar
  213. [213]
    A.J. Urdaneta, J.F. Gómez, E. Sorrentino, L. Flores and R. Díaz (1999) A hybrid genetic algorithm for optimal reactive power planning based upon successive linear programming. IEEE Transactions on Power Systems, 14(4), 1292–1298.CrossRefGoogle Scholar
  214. [214]
    A.H.C. vanKampen, C.S. Strom and L.M.C Buydens (1996) Legalization, penalty and repair functions for constraint handling in the genetic algorithm methodology. Chemometrics and Intelligent Laboratory Systems, 34(1), 55–68.Google Scholar
  215. [215]
    L. Wang and J. Yen (1999) Extracting fuzzy rules for system modeling using a hybrid of genetic algorithms and kalman filter. Fuzzy Sets and Systems, 101(3), 353–362.CrossRefMathSciNetGoogle Scholar
  216. [216]
    J.P. Watson, S. Rana, L.D. Whitley and A.E. Howe (1999) The impact of approximate evaluation on the performance of search algorithms for warehouse scheduling. Journal of Scheduling, 2(2), 79–98.CrossRefMathSciNetGoogle Scholar
  217. [217]
    R. Wehrens, C. Lucasius, L. Buydens and G. Kateman (1993) HIPS, A hybrid self-adapting expert system for nuclear magnetic resonance spectrum interpretation using genetic algorithms. Analytica Chimica ACTA, 277(2), 313–324.CrossRefGoogle Scholar
  218. [218]
    P. Wei and L.X. Cheng (1999) A hybrid genetic algorithm for function optimization. Journal of Software, 10(8), 819–823.Google Scholar
  219. [219]
    X. Wei and F. Kangling (2000) A hybrid genetic algorithm for global solution of nondifferentiable nonlinear function. Control Theory & Applications, 17(2), 180–183.Google Scholar
  220. [220]
    D.S. Weile and E. Michielssen (1999) Design of doubly periodic filter and polarizer structures using a hybridized genetic algorithm. Radio Science, 34(1), 51–63.CrossRefGoogle Scholar
  221. [221]
    R.P. White, J.A. Niesse and H.R. Mayne (1998) A study of genetic algorithm approaches to global geometry optimization of aromatic hydrocarbon microclusters. Journal of Chemical Physics, 108(5), 2208–2218.CrossRefGoogle Scholar
  222. [222]
    D. Whitley (1987) Using reproductive evaluation to improve genetic search and heuristic discovery. In: J.J. Grefenstette (ed.), Proceedings of the Second International Conference on Genetic Algorithms and their Applications. Lawrence Erlbaum Associates, Cambridge, MA, pp. 108–115.Google Scholar
  223. [223]
    P. Willett (1995) Genetic algorithms in molecular recognition and design. Trends in Biotechnology, 13(12), 516–521.CrossRefGoogle Scholar
  224. [224]
    D.H. Wolpert and W.G. Macready (1997) No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1(1), 67–82.CrossRefGoogle Scholar
  225. [225]
    J. Xiao and L. Zhang (1997) Adaptive evolutionary planner/navigator for mobile robots. IEEE Transactions on Evolutionary Computation, 1(1), 18–28.Google Scholar
  226. [226]
    M. Yannakakis (1997) Computational complexity. In: E.H.L. Aarts and J.K. Lenstra (eds.), Local Search in Combinatorial Optimization. Wiley, Chichester, pp. 19–55.Google Scholar
  227. [227]
    X. Yao (1993) Evolutionary artificial neural networks. International Journal of Neural Systems, 4(3), 203–222.CrossRefGoogle Scholar
  228. [228]
    I.C. Yeh (1999) Hybrid genetic algorithms for optimization of truss structures. Computer Aided Civil and Infrastructure Engineering, 14(3), 199–206.Google Scholar
  229. [229]
    M. Yoneyama, H. Komori and S. Nakamura (1999) Estimation of impulse response of vocal tract using hybrid genetic algorithm—a case of only glottal source. Journal of the Acoustical Society of Japan, 55(12), 821–830.Google Scholar
  230. [230]
    C.R. Zacharias, M.R. Lemes and A.D. Pino (1998) Combining genetic algorithm and simulated annealing: a molecular geometry optimization study. THEOCHEM—Journal of Molecular Structure, 430(29–39).Google Scholar
  231. [231]
    M. Zwick, B. Lovell and J. Marsh (1996) Global optimization studies on the 1-d phase problem. International Journal of General Systems, 25(1), 47–59.Google Scholar

Copyright information

© Kluwer Academic Publishers 2003

Authors and Affiliations

  • Pablo Moscato
    • 1
  • Carlos Cotta
    • 2
  1. 1.Grupo de Engenharia de Computação em Sistemas Complexos, Departamento de Engenharia de Computação e Automação Industrial, Faculdade de Engenharia Eletrónica e de ComputaçãoUniversidade Estadual de CampinasCampinas, SPBrazil
  2. 2.Departamento de Lenguajes y Ciencias de la Computación, Escuela Técnica Superior de Ingeniería InformáticaCampus de TeatinosMálagaSpain

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