Forschungsschwerpunkt

Genetische Algorithmen

When Holland (1975) proposed genetic algorithms, he envisioned them as methods that were going to be efficient, easy to use, and applicable to a wide range of problems. But one thing that stands out from the current literature, is that genetic algorithms seem to require quite a bit of expertise in order to make them work well for a particular application. The expertise is needed because users are generally clueless on how to decide among the various codings and operators, as well as on deciding on a good set of parameter values for the GA. In the end, instead of being a robust and an easy-to-use method, the genetic algorithm turns out to be a method that needs a lot of tuning and parameter fiddling. This state of affairs is a kind of a paradox, and contradicts Holland's original goals.

The decisions that a user must make before applying a GA can be grouped in two categories. The first, is the choice of an appropriate coding and operators. The second, is the choice of appropriate parameter settings.

From the user's point of view, setting the parameters of a genetic algorithm (GA) is far from a trivial task. Moreover, the user is typically not interested in population sizes, crossover probabilities, selection rates, and other GA technicalities. He is just interested in solving a problem, and what he would really like to do, is to hand-in the problem to a blackbox algorithm, and simply press a start button. We investigate the development of a GA that fulfils this requirement. It has no parameters whatsoever. The development of the algorithm takes into account several aspects of the theory of GAs, including previous research works on population sizing, the schema theorem, building block mixing, and genetic drift.

If Genetic Algorithms should be made accessible for a broader public, further convergence criteria and proofs must be developed - in particular in order to place the selection of numerous parameters on a solid base.
 

Related Literature (Some of the publications which I have read.)

  1. Antonisse, J. (1989). A new interpretation of schema notation that overturns the binary encoding constraint. In Schaffer, J. D., editor, Proceedings of the Third International Conference on Genetic Algorithms (ICGA ’89), pages 86–91. Morgan Kaufmann Publishers.
  2. Bäck, T. (1991). Optimization by means of genetic algorithms. In Köhler, E., editor, 36. Internationales wissenschaftliches Kolloquium, pages 163–169. Technische Universität Ilmenau.
  3. Bäck, T. (1992a). The interaction of mutation rate, selection, and self-adaption within a genetic algorithm. In Männer, R. and Manderick, B., editors, Parallel Problem Solving from Nature 2, pages 85–94. Elsevier Science Publishers.
  4. Bäck, T. (1992b). Self-adaptation in genetic algorithms. In Proceedings of the 1st European Conference on Artificial Life (1991), pages 263–271. MIT Press.
  5. Bäck, T. (1993). Optimal mutation rates in genetic search. In Forrest, S., editor, Proceedings of the Fifth International Conference on Genetic Algorithms (ICGA ’93), pages 2–9, San Mateo, CA. Morgan Kaufmann Publishers.
  6. Bäck, T., Hammel, U., and Schwefel, H.-P. (1997). Evolutionary computation: Comments on the history and current state. IEEE Transactions on Evolutionary Computation, 1(1):3–17.
  7. Bäck, T. and Hoffmeister, F. (1991). Extended selection mechanisms in genetic algorithms. In Belew, R. K. and Booker, L. B., editors, Proceedings of the Fourth International Conference on Genetic Algorithms (ICGA ’91), pages 92–99. Morgan Kaufmann Publishers.
  8. Bäck, T., Hoffmeister, F., and Schwefel, H.-P. (1991). A survey of evolution strategies. In Belew, R. K. and Booker, L. B., editors, Proceedings of the Fourth International Conference on Genetic Algorithms (ICGA ’91), pages 2–9. Morgan Kaufmann Publishers.
  9. Bäck, T. and Schwefel, H.-P. (1995a). Evolution Strategies I: Variants and their computational implementation. In Winter, G., Périeaux, J., Galá, M., and Cuesta, P., editors, Genetic Algorithms in Engineering and Computer Science, pages 111–126, Chicester. Wiley.
  10. Bäck, T. and Schwefel, H.-P. (1995b). Evolution Strategies II: Theoretical aspects implementation. In Winter, G., Périeaux, J., Galá, M., and Cuesta, P., editors, Genetic Algorithms in Engineering and Computer Science, pages 127–140, Chicester. Wiley. 
  11. Beasley, D., Bull, D. R., and Martin, R. R. (1993a). An overview of genetic algorithms: Part 1, fundamentals. University Computing, 15(2):58–69.
  12. Beasley, D., Bull, D. R., and Martin, R. R. (1993b). An overview of genetic algorithms: Part 2, research topics. University Computing, 15(4):170–181. 
  13. Beasley, D., Bull, D. R., and Martin, R. R. (1993c). A sequential niche technique for multimodal function optimization. Evolutionary Computation, 1(2):101–125.
  14. Belding, T. C. (1995). The distributed genetic algorithm revisited. In Eshelman, D., editor, Proceedings of the Sixth International Conference on Genetic Algorithms (ICGA ’95), San Francisco. Morgan Kaufmann Publishers. 
  15. Benyahia, I. and Potvin, J.-Y. (1995). Generalization and refinement of route construction heuristics using genetic algorithms. In Proceedings of the IEEE International conference on Evolutionary Computation (ICEC 95), pages 39–43. 
  16. Bertoni, A. and Dorigo, M. (1993). Implicit parallelism in genetic algorithms. Artificial Intelligence, 2(61):307–314.
  17. Brittain, D., Sims Williams, J., and McMahon, C. A. (1997). A genetic algorithm approach to planning the telecommunications access. In Proceedings of the Seventh International Conference on Genetic Algorithms (ICGA ’97). Morgan Kaufmann Publishers.
  18. Bruns, R. (1995). Integration of constraint solving techniques in genetic algorithms. In Proceedings of the IEEE International conference on Evolutionary Computation (ICEC 95), pages 33–38.
  19. Bussieck, M. R., Kreuzer, P., and Zimmermann, U. T. (1996). Optimal lines for railway systems. European Journal of Operational Research, 96:54–63.
  20. Cantú-Paz, E. (1995). A summary of research on parallel genetic algorithms. Technical Report IlliGAL Report No. 95007, Illinois Genetic Algorithms Laboratory (IlliGAL). 
  21. Chang, C. S., Wang, W., Liew, A. C., Wen, F. S., and Srinivasan, D. (1995). Genetic algorithm based bicriterion optimisation for traction substations in dc railway system. In Proceedings of the IEEE International conference on Evolutionary Computation (ICEC 95), pages 11–16.
  22. Cleveland, G. A. and Smith, S. F. (1989). Using genetic algorithms to schedule flow shop releases. In Schaffer, J. D., editor, Proceedings of the Third International Conference on Genetic Algorithms (ICGA ’89), pages 160–169. Morgan Kaufmann Publishers.
  23. Coli, M. and Palazzari, P. (1995). Searching for the optimal coding in genetic algorithms. In Proceedings of the IEEE International conference on Evolutionary Computation (ICEC 95), pages 92–96.
  24. Corno, F., Sonza Reorda, M., and Squillero, G. (1998). The selfish gene algorithm: a new evolutionary optimization strategy. In SAC’98: 13th Annual ACM Symposium on Applied Computing, Atlanta, Georgia (USA). Politecnico di Torino, Dipartimento di Automatica e Informatica, Torino, Italy. 
  25. Davis, L. (1989). Adapting operator probabilities in genetic algorithms. In Schaffer, J. D., editor, Proceedings of the Third International Conference on Genetic Algorithms (ICGA ’89), pages 61–69. Morgan Kaufmann Publishers.
  26. de la Maza, M. and Tidor, B. (1993). An analysis of selection procedures with particular attention paid to proportional and boltzmann selection. In Forrest, S., editor, Proceedings of the Fifth International Conference on Genetic Algorithms (ICGA ’93), pages 124–131. Morgan Kaufmann Publishers.
  27. Deb, K. and Goldberg, D. E. (1989). An investigation of niche and species formation in genetic function optimization. In Schaffer, J. D., editor, Proceedings of the Third International Conference on Genetic Algorithms (ICGA ’89), pages 42–50. Morgan Kaufmann Publishers. 
  28. Deb, K. and Goldberg, D. E. (1991). mga in c: A messy genetic algorithm in c. Technical Report IlliGAL Report No. 91008, Illinois Genetic Algorithms Laboratory (IlliGAL). 
  29. Derrida, B. (1981). Random-energy model: An exactly solvable model of disordered systems. Physical Review B, 24(5):2613–2626.
  30. Dupas, R., Fourmaux, D., and Goncalvez, G. (1997). Optimizing machine stoppages by a genetic algorithm in the context of multiple machines assigned to one operator. In Sydow, A., editor, Proceedings of the 15th IMACS World Congress on Scientific Computation, Modelling and Applied Mathematics, volume 2 : Numerical Mathematics, pages 627–631. Wissenschaft & Technik Verlag, Berlin, August 1997.
  31. Eddelbüttel, D. (1992). A genetic algorithm for passive management. Master’s thesis, GREQE-EHESS, Centre de la Vieille Charit’e, Marseille. 
  32. Eshelman, L. J., Caruana, R. A., and Schaffer, J. D. (1989). Biases in the crossover landscape. In Schaffer, J. D., editor, Proceedings of the Third International Conference on Genetic Algorithms (ICGA ’89), pages 10–19. Morgan Kaufmann Publishers. 
  33. Fang, H.-L., Ross, P., and Corne, D. (1993). A promising genetic algorithm approach to job-shop scheduling, rescheduling, and open-shop scheduling problems. In Forrest, S., editor, Proceedings of the Fifth International Conference on Genetic Algorithms (ICGA ’93), pages 375–382, San Mateo. Morgan Kaufmann Publishers.
  34. Field, P. (1994). Walsh and partition functions made easy. In missing. Presented as a poster at the 1994 AISB Workshop on Evolutionary Computing.
  35. Filho, J., Alippi, C., and Treleaven, P. (1994). Genetic algorithm programming environments. IEEE Computer, (February/1994).
  36. Fleury, G., Goujon, J.-Y., Gourgand, M., and Lacomme, P. (1997). Stochastic optimization for manufacturing systems with random events. In Sydow, A., editor, Proceedings of the 15th IMACS World Congress on Scientific Computation, Modelling and Applied Mathematics, volume 5 : Systems Engineering, pages 427–432. Wissenschaft & Technik Verlag, Berlin, August 1997.
  37. Floréen, P. B. and Kok, J. N. (1994). Tracing the moments of distributions in genetic algorithms. In Alander, J. T., editor, Proceedings of the Second Finnish Workshop on Genetic Algorithms and their Applications (2FWGA), pages 51–60. Department of Information Technology and Production Economics, University of Vaasa, Finland. 
  38. Frick, A. (1997). A universal object-oriented framework for evolution programs. In Sydow, A., editor, Proceedings of the 15th IMACS World Congress on Scientific Computation, Modelling and Applied Mathematics, volume 2 : Numerical Mathematics, pages 639–644. Wissenschaft & Technik Verlag, Berlin, August 1997.
  39. Fukunaga, A. S. and Kahng, A. B. (1995). Improving the performance of evolutionary optimization by dynamically scaling the evaluation function. In Proceedings of the IEEE International conference on Evolutionary Computation (ICEC 95), pages 182–187. 
  40. Galavíz, J. and Kuri, A. (1996). A self-adaptive genetic algorithm for function optimization. In Proceedings of the Nineth International Symposium on Artificial Intelligence (ISAI-96). 
  41. Goldberg, D. E. (1989). Sizing population for serial anf parallel genetic algorithms. In Schaffer, J. D., editor, Proceedings of the Third International Conference on Genetic Algorithms (ICGA ’89), pages 70–79. Morgan Kaufmann Publishers.
  42. Goldberg, D. E. (1991). Real-coded genetic algorithms, virtual alphabets, and blocking. Complex Systems, 5(2):139–167.
  43. Goldberg, D. E. (1992). Construction of high-order deceptive functions using low-order walsh coefficients. Annals of Mathematics and Artificial Intelligence, 5(1):35–48.
  44. Goldberg, D. E. (1994a). Change in engineering education: One myth, two scenarios, and three foci. Technical Report IlliGAL Report No. 94003, Illinois Genetic Algorithms Laboratory (IlliGAL).
  45. Goldberg, D. E. (1994b). The existential pleasures of genetic algorithms. Technical Report IlliGAL Report No. 94010, Illinois Genetic Algorithms Laboratory (IlliGAL).
  46. Goldberg, D. E. (1994c). First flights at genetic-algorithm kitty hawk. Technical Report IlliGAL Report No. 94008, Illinois Genetic Algorithms Laboratory (IlliGAL).
  47. Goldberg, D. E., Deb, K., and Clark, J. H. (1991). Genetic algorithms, noise, and the sizing of populations. Technical Report IlliGAL Report No. 91010, Illinois Genetic Algorithms Laboratory (IlliGAL).
  48. Goldberg, D. E., Deb, K., and Horn, J. (1992a). Massive multimodality, deception, and genetic algorithms. In Männer, R. and Manderick, B., editors, Parallel Problem Solving from Nature (PPSN), number 2, pages 37–46, Amsterdam. North-Holland.
  49. Goldberg, D. E., Deb, K., Kargupta, H., and Harik, G. (1993). Rapid, accurate optimization of difficult problems using fast messy genetic algorithms. Technical Report IlliGAL Report No. 93004, Illinois Genetic Algorithms Laboratory (IlliGAL).
  50. Goldberg, D. E., Deb, K., and Thierens, D. (1992b). Toward a better understanding of mixing in genetic algorithms. Technical Report IlliGAL Report No. 92009, Illinois Genetic Algorithms Laboratory (IlliGAL).
  51. Goldberg, D. E., Horn, J., and Deb, K. (1992c). What makes a problem hard for a classifier system? Technical Report IlliGAL Report No. 92007, Illinois Genetic Algorithms Laboratory (IlliGAL).
  52. Goldberg, D. E., Milman, K., and Tidd, C. (1992d). Genetic algorithms: A bibliography. Technical Report IlliGAL Report No. 92008, Illinois Genetic Algorithms Laboratory (IlliGAL).
  53. Grefenstette, J. J. (1989). A system for learning control strategies with genetic algorithms. In Schaffer, J. D., editor, Proceedings of the Third International Conference on Genetic Algorithms (ICGA ’89), pages 183–190. Morgan Kaufmann Publishers.
  54. Grefenstette, J. J. and Baker, J. E. (1989). How genetic algorithms work: A critical look at implicit parallelism. In Schaffer, J. D., editor, Proceedings of the Third International Conference on Genetic Algorithms (ICGA ’89), pages 20–27. Morgan Kaufmann Publishers. 
  55. Handa, K. and Kuga, S. (1995). Polycell placement for analog lsi chip designs by genetic algorithms and tabu search. In Proceedings of the IEEE International conference on Evolutionary Computation (ICEC 95), pages 716–721.
  56. Harik, G. (1994). Finding multiple solutions in problems of bounded difficulty. Technical Report IlliGAL Report No. 94002, Illinois Genetic Algorithms Laboratory (IlliGAL). 
  57. Harik, G., Cantú-Paz, E., Goldberg, D. E., and Miller, B. L. (1997). The gambler’s ruin problem, genetic algorithms and the sizing of populations. In Bäck, T., editor, Proceedings of the 1997 IEEE Conference on Evolutionary Computation, pages 7–12. IEEE Press.
  58. Harvey, I. (1993). The puzzle of the persistent question marks: A case study of genetic drift. In Forrest, S., editor, Proceedings of the Fifth International Conference on Genetic Algorithms (ICGA ’93), pages 15–22. Morgan Kaufmann Publishers.
  59. Hong, I., Kahng, A. B., and Moon, B. R. (1995). Exploiting synergies of multiple crossovers: Initial studies. In Proceedings of the IEEE International conference on Evolutionary Computation (ICEC 95), pages 245–250.
  60. Horn, J. (1993a). Finite markov chain analysis of genetic algorithms with niching. In Forrest, S., editor, Proceedings of The Fifth International Conference on Genetic Algorithms (ICGA ’93), number III, pages 110–117, San Mateo, CA. Illinois Genetic Algorithms Laboratory (IlliGAL), Morgan Kaufmann Publishers. 
  61. Horn, J. (1993b). Finite markov chain analysis of genetic algorithms with niching. In Forrest, S., editor, Proceedings of the Fifth International Conference on Genetic Algorithms (ICGA ’93), pages 110–117. Morgan Kaufmann Publishers.
  62. Horn, J. (1995). Genetic algorithms, problem difficulty, and the modality of fitness landscapes. Technical Report IlliGAL Report No. 95004, Illinois Genetic Algorithms Laboratory (IlliGAL). 
  63. Horn, J. and Goldberg, D. E. (1994). Genetic algorithm difficulty and the modality of fitness landscapes. In Proceedings of the Foundations of Genetic Algorithms (FOGA) 3 workshop. Illinois Genetic Algorithms Laboratory (IlliGAL).
  64. Horn, J., Goldberg, D. E., and Deb, K. (1992). Research note: Long path problems for mutation-based algorithms. In Davidor, Y., Schwefel, H.-P., and Männer, R., editors, Proceedings of The Third Conference on Parallel Problem Solving from Nature (1994), number III, pages 149–158, Berlin, Germany. Illinois Genetic Algorithms Laboratory (IlliGAL), Springer-Verlag.
  65. Horn, J., Goldberg, D. E., and Deb, K. (1994). Implicit niching in a learning classifier system: Nature’s way. In Evolutionary Computation, 1994, number 2(1). Illinois Genetic Algorithms Laboratory (IlliGAL).
  66. Horn, J. and Nafpliotis, N. (1993). Multiobjective optimization using the niched pareto genetic algorithm. In Proceedings of The First IEEE Conference on Computational Intelligence (ICEC ’94), number I, pages 82–87, Piscataway, NJ. Illinois Genetic Algorithms Laboratory (IlliGAL), IEEE Service Center.
  67. Jones, T. C. (1995a). Crossover, macromutation, and population-based search. In Eshelman, L. J., editor, Proceedings of the Sixth International Conference on Genetic Algorithms (ICGA ’95), pages 73–80. 
  68. Jones, T. C. (1995b). Evolutionary Algorithms, Fitness Landscapes and Search. PhD thesis, University of New Mexico.
  69. Jones, T. C. and Forrest, S. (1995). Fitness distance correlation as a measure of problem difficulty for genetic algorithms. In Eshelman, L. J., editor, Proceedings of the Sixth International Conference on Genetic Algorithms (ICGA ’95), pages 184–192.
  70. Juels, A. and Wattenberg, M. (1994). Stochastic hillclimbing as a baseline method for evaluating genetic algorithms. Technical Report csd-94-834, University of California. 
  71. Kargupta, H. (1993). Information transmission in genetic algorithm and shannon’s second theorem. In missing. Illinois Genetic Algorithms Laboratory (IlliGAL).
  72. Kargupta, H. and Goldberg, D. E. (1994). Decision making in genetic algorithms: A signal-to-noise perspective. Technical Report IlliGAL Report No. 94004, Illinois Genetic Algorithms Laboratory (IlliGAL).
  73. Keller, B. and Lutz, R. (1997). A new crossover operator for rapid function optimisation using a genetic algorithm. In Proceedings of the Eight Ireland Conference on Artificial Intelligence (AI-97), volume 2, pages 48–55. School of Information and Software Engineering, Faculty of Informatics, University of Ulster, Magee College, Northern Ireland. 
  74. Khuri, S. (1994). Walsh and haar functions in genetic algorithms. In Proceedings of the 1994 ACM Symposium on Applied Computing (SAC’94) ”Genetic Algorithms and Optimization Track”. Phoenix Arizona, ACM Press.
  75. Khuri, S. and Bäck, T. (1994). An evolutionary heuristic for the minimum vertex cover problem. In Hopf, J., editor, Genetic Algorithms within the Framework of Evolutionary Computation, pages 86–90. Max Planck Institut für Informatik, Saarbrücken.
  76. Khuri, S., Bäck, T., and Heitkötter, J. (1994a). An evolutionary approach to combinatorial optimization problems. In Proceedings of the 1994 Computer Science Conference (CSC’94), pages 66–73. Phoenix Arizona, ACM Press.
  77. Khuri, S., Bäck, T., and Heitkötter, J. (1994b). The zero/one multiple knapsack problem and genetic algorithms. In Proceedings of the 1994 ACM Symposium on Applied Computing (SAC’94) ”Genetic Algorithms and Optimization Track”, pages 188–193. Phoenix Arizona, ACM Press.
  78. Khuri, S., Schütz, M., and Heitkötter, J. (1995). Evolutionary heuristics for the bin packing problem. In The proceedings of ICANNGA ’95 Int’l Conf on Artificial NNs and GAs. 
  79. Kolarov, K. (1995). The role of selection in evolutionary algorithms. In Proceedings of the IEEE International conference on Evolutionary Computation (ICEC 95), pages 86–91. 
  80. Krishnakumar, K. (1989). Micro-genetic algorithms for stationary and non-stationary function optimization. SPIE - Intelligent Control and Adaptive Systems, 1196:289–296. 
  81. Kwasnicka, H. (1997). The role of redundant genes in evolutionary algorithms - simulation study. In Sydow, A., editor, Proceedings of the 15th IMACS World Congress on Scientific Computation, Modelling and Applied Mathematics, volume 2 : Numerical Mathematics, pages 645–650. Wissenschaft & Technik Verlag, Berlin, August 1997.
  82. Leitch, D. D. (1995). A New Genetic Algorithm for the Evolution of Fuzzy Systems. PhD thesis, Robotics Research Group, Department of Engineering Science, University of Oxford. 
  83. Levine, D. (1994). A parallel genetic algorithm for the set partitioning problem. Technical Report ANL-94/23, Argonne National Laboratory.
  84. Mahfoud, S. W. (1992). Crowding and preselection revisited. In Manner, R. and Manderick, B., editors, Parallel Problem Solving from Nature, number 2, pages 27–36, Amsterdam. Illinois Genetic Algorithms Laboratory (IlliGAL), Elsevier Science Publishers (North Holland). 
  85. Mahfoud, S. W. (1993a). Finite markov chain models of an alternative selection strategy for the genetic algorithm. Complex Systems, 7(2):155–170.
  86. Mahfoud, S. W. (1993b). Simple analytical models of genetic algorithms for multimodal function optimization. Technical Report IlliGAL Report No. 93001, Illinois Genetic Algorithms Laboratory (IlliGAL).
  87. Mahfoud, S. W. (1994). Population sizing for sharing methods. In The Proceedings of the Third Workshop on Foundations of Genetic Algorithms. Illinois Genetic Algorithms Laboratory (IlliGAL).
  88. Mahfoud, S. W. (1995). Niching methods for genetic algorithms. Technical Report IlliGAL Report No. 95001, Illinois Genetic Algorithms Laboratory (IlliGAL).
  89. Mahfoud, S. W. and Goldberg, D. E. (1995). Parallel recombinative simulated annealing: A genetic algorithm. Parallel Computing, 21(1):1–28.
  90. Mason, A. (1995). A non-linearity measure of a problem’s crossover suitability. In Proceedings of the IEEE International conference on Evolutionary Computation (ICEC 95), pages 68–73.
  91. Mesghouni, K. and Hammadi, S.and Borne, P. (1997). Hybrid representation for genetic algorithm to solve flexible job shop scheduling. In Sydow, A., editor, Proceedings of the 15th IMACS World Congress on Scientific Computation, Modelling and Applied Mathematics, volume 5 : Systems Engineering, pages 433–438. Wissenschaft & Technik Verlag, Berlin, August 1997.
  92. Miller, B. L. and Goldberg, D. E. (1995). Genetic algorithms, tournament selection, and the effects of noise. Technical Report IlliGAL Report No. 95006, Illinois Genetic Algorithms Laboratory (IlliGAL).
  93. Mitchell, M. and Forrest, S. (1993). Genetic algorithms and artificial life. Technical Report 93-11-072, SFI.
  94. Monteiro, J. (missing). Optimal finite state machine encoding using genetic algorithms. In missing. Department of EECS. Massachusetts Institute of Technology, Cambridge. 
  95. Mühlenbein, H. (missing). Evolutionary algorithms: Theory and applications. In missing. 
  96. Mühlenbein, H. and Schlierkamp-Voosen, D. (1993a). Optimal interaction of mutation and crossover in the breeder genetic algorithm. In Forrest, S., editor, Proceedings of the Fifth International Conference on Genetic Algorithms (ICGA ’93), pages 648–657. Morgan Kaufmann Publishers. 
  97. Mühlenbein, H. and Schlierkamp-Voosen, D. (1993b). Predictive models for the breeder genetic algorithm, i. continuous parameter optimization. Evolutionary Computation, 1(4):25–49.
  98. Mühlenbein, H. and Schlierkamp-Voosen, D. (1994). The science of breeding and its application to the breeder genetic algorithm bga. Evolutionary Computation, 1(4):335–360. 
  99. Oei, C. K., Goldberg, D. E., and Chang, S.-J. (1991). Tournament selection, niching, and the preservation of diversity. Technical Report IlliGAL Report No. 91011, Illinois Genetic Algorithms Laboratory (IlliGAL).
  100. Ohmori, K. (1995). High-level synthesis using genetic algorithm. In Proceedings of the IEEE International conference on Evolutionary Computation (ICEC 95), pages 209–213. 
  101. Palmer, C. C. (1994). An Appproach To A Problem In Network Design Using Genetic Algorithms. PhD thesis, Department of Computer Science.
  102. Palmer, C. C. and Kershenbaum, A. (1994). Representing trees in genetic algorithms. In missing. Presented at WCCI 1994.
  103. Parmee, I. and Vekeria, H. (1997). Evolutionary/adaptive search strategies and model representation in engineering design. In Sydow, A., editor, Proceedings of the 15th IMACS World Congress on Scientific Computation, Modelling and Applied Mathematics, volume 2 : Numerical Mathematics, pages 615–620. Wissenschaft & Technik Verlag, Berlin, August 1997.
  104. Prügel-Bennett, A. and Shapiro, J. L. (1994). Analysis of genetic algorithms using statistical mechanics. Physical Review Letters, 72(9):1305–1309.
  105. Prügel-Bennett, A. and Shapiro, J. L. (1997). The dynamics of a genetic algorithm for simple random ising systems. Physica D, 104:75–114.
  106. Rudolph, G. (1994). Convergence analysis of canonical genetic algorithms. IEEE Transactions on Neural Networks, Special Issue on Evolutionary Programming. (In print) 
  107. Rudolph, G. and Schwefel, H.-P. (1994). Evolutionäre Algorithmen: Ein robustes Optimierkonzept. Physikalische Blätter, 50(3):236–238.
  108. Santos, A. and Dourado, A. (1997). Production and energy optimization in an industrial complex: A genetic algorithm approach. In Sydow, A., editor, Proceedings of the 15th IMACS World Congress on Scientific Computation, Modelling and Applied Mathematics, volume 4 : Knowledge-based Systems and Computer Science, pages 43–48. Wissenschaft & Technik Verlag, Berlin, August 1997.
  109. Schaerf, A., Shoham, Y., and Tennenholtz, M. (1995). Adaptive load balancing: A study in multi-agent learning. Journal of Artificial Intelligence Research, (2):475–500.
  110. Schaffer, J., Caruana, R. A., Eshelman, L. J., and Das, R. (1989). A study of control parameters affecting online performance of genetic algorithms for function optimization. In Schaffer, J. D., editor, Proceedings of the Third International Conference on Genetic Algorithms (ICGA ’89), pages 51–60. Morgan Kaufmann Publishers.
  111. Schraudolph, N. N. and Belew, R. K. (1992). Dynamic parameter encoding for genetic algorithms. Technical Report UCSD CS 90-175, University of California.
  112. Schwefel, H.-P. and Bäck, T. (1992). Künstliche Evolution — eine intelligente Problemlösungsstrategie? KI — Künstliche Intelligenz, 6(2):20–27.
  113. Syswerda, G. (1989). Uniform crossover in genetic algorithms. In Schaffer, J. D., editor, Proceedings of the Third International Conference on Genetic Algorithms (ICGA ’89), pages 2–9. Morgan Kaufmann Publishers.
  114. Thierens, D. and Goldberg, D. E. (1993). Mixing in genetic algorithms. In Forrest, S., editor, Proceedings of the Fifth International Conference on Genetic Algorithms (ICGA ’93), pages 38–45.
  115. Whitley, D. (1989). The genitor algorithm and selection pressure: Why rank-based allocation of reproductive trials is best. In Schaffer, J. D., editor, Proceedings of the Third International Conference on Genetic Algorithms (ICGA ’89), pages 116–121. Morgan Kaufmann Publishers.
  116. Whitley, D. (1994). A genetic algorithm tutorial. Statistics and Computing, (4):65–85. 
  117. Whitley, D., Starkweather, T., and Fuquay, D. (1989). Scheduling problems and traveling salesmen: The genetic edge recombination operator. In Schaffer, J. D., editor, Proceedings of the Third International Conference on Genetic Algorithms (ICGA ’89), pages 133–140. Morgan Kaufmann Publishers.
  118. Wilcox, J. R. (1995). Organizational learning within a learning classifier system. Technical Report IlliGAL Report No. 95003, Illinois Genetic Algorithms Laboratory (IlliGAL). 
  119. Wolpert, D. H. and Macready, W. G. (1997). No free lunch theorem for optimization. IEEE Transactions on Evolutionary Computation, 1(1):67–82.
Weitere Veröffentlichungen zu diesem und anderen Forschungschwerpunkten unseres Lehrstuhls finden Sie auf unserer Publikationsseite.

Ansprechpartner: Dipl.-Math. Stephan Würll