22C:133 Genetic Algorithms: Student Projects

• Bejan, Alina

  Knapsack Problem

  1. M. Gen and R. Cheng, Genetic Algorithms and Engineering Design , John Wiley and Sons, Inc. 1997, Chapter 3, Section 3.2
  2. Z. Michalewicz, Genetic Algorithm + Data Structures = Evolution Programs , Springer Verlag, 1996, Chapter 4, Section 4.5
  3. S. Skiena, The Algorithm Design Manual , Springer Verlag, 1998, Chapter 8, Section 8.2
  Presentation date:

• Doerder, Adamn

  Simulation how populations react to afflictions and circumstances

  1. A. Shah, Ecology and the crisis of overpopulation , North Hampton, 1998
  2. G.S. Howard, Ecological Psychology , University of Notre Dame Press, 1997g
  Presentation date:
  • Munteanu, Stefan

    Timetable problem

    1. Weare, R.F. et al, A hybrid genetic algorithm for highly constrained timetabling problems , Proceedings of the 6-th International Conference on Genetic Algorithms
    2. Michalewicz, Z., Genetic Algorithms + Data Structures = Evolution Programs , Springer-Verlag, 1992
    3. Shamir, A. et al, On the complexity of timetable and multicommodity flow problems , SIAM Journal on Computing 1976
    Presentation date:

  • Ouyang, Huei-Tau,

    Shape optimization of 2D airfoil by genetic algorithms

    1. Kim, H.-J. and Rho, O.-H., Aerodynamic design of transonic wings using the target pressure optimization approach , Journal of Aircraft, Vol. 35, Nr. 5, pp. 671--677.
    2. Obayashi, S. and Takanashi, S., Genetic optimization of target pressure distribution for inverse design method , AIAA Journal, Vol. 34, Nr. 5, pp. 881--886.
    3. Vicini, A. and Quagliarella, D., Inverse and direct airfoil design using a multiobjective genetic algorithm , AIAA Journal, Vol. 35, Nr. 9, pp. 1499--1505.
    4. Sharatchandra, M.C. and Gad-elHak, M., New approach to constrained shape optimization using genetic algorithms , AIAA Journal, Vol. 36, Nr. 1, pp. 55--61
    5. Anderson, M.B., The potential of genetic algorithms for subsonic wing design , AIAA paper 95--3925
    6. Yamamoto, K. and Inoue, O., Applications of genetic algorithms to aerodynamic shape optimization , AIAA paper 95--1650
    7. Vicini, A. and Quagliarella, D., Multipoint transonic airfoil design by means of multiobjective genetic algorithms , AIAA paper 97--0082.
    8. Crispin, C., Aircraft conceptual optimization using simulated evolution , AIAA paper 94--0092
    9. No author, Genetic algorithms in engineering and computer science , pp. 251--270 and 371--442, John Wiley, New York 1995,
    Presentation date:

  • Qidwai, Misbah

    Parallel Scheduling Using the Frequency Based Model for a Vehicle Dynamics Code

    1. Michalewicz, Z., Genetic Algorithms + Data Structures = Evolution Programs , Springer Verlag, 1997, Chapter 11, Section 11.1 Scheduling
    2. Gen, M and Cheng, R., Genetic Algorithms and Engineering Design , John Wiley and Sons, Inc., 1997, Chapter 3, Section 3.4 Minimum Spanning Tree Problem
    Presentation date:

  • Rao, Shrisha

    Nonlinear optimization

    No specific literature has been indicated
    Presentation date:

  • Soricut, Radu,

    Path planning in a mobile robot environment

    1. Gill, Mark, A.C. and Zomaya Albert, Obstacle avoidance in multi-robot systems , World Scientific 1998.
    2. Michalewicz, Z., Genetic Algorithms + Data Structures = Evolution Programs , Springer Verlag, 1997, Chapter 11, Section 11.4 Path planning in a mobile robot environment
    Presentation date:

  • Tucker, Matt,

    Traveling Salesperson Problem

    1. Michalewicz, Z., Genetic Algorithms + Data Structures = Evolution Programs , Springer Verlag, 1997.
    2. Price, K. and Storn, R., Dr. Dobb's Journal, April 1997
    Presentation date:

  • Xiao, Ningchuan

    Using genetic algorithm to search the solution space of ill-defined spatio-temporal environmental models

    1. Bennett, D.A. and Armstrong, M.P. and Wade, G.A., Agent mediated consensus-building for environmental problems: a genetic algorithm approach , Proceedings of Third International Conference/Workshop on Integrating GIS and Environmental Modeling
    2. Bennett, D.A. and Wade, G.A. and Armstrong, M.P., Exploring the solution space of semi-structured spatial problems using genetic algorithms , Proceedings of Auto-Carto 13, 234--243, Bethesda, MD, 1997.
    3. Bennett, D.A. and Wade, G.A. and Armstrong, M.P., Exploring the solution space of semi-structured spatial problems using genetic algorithms , Transaction in GIS3 (1):51--71 234--243, Bethesda, MD, 1997.
    4. Hobbs, M., Spatial clustering with a genetic algorithm , in Innovations GIS3 85--93, London 1996
    5. Openshaw, S., Neural networks, genetic and fuzzy logic models of spatial interaction , Environment and Planning A 30: 1857--1872, 1998
    6. Zhou, J. and Civco, D., Using genetic learning neural networks for spatial decision making in GIS , Photogrammetric Engineering and Remote Sensing 62, 1287-1295, 1996.
    Presentation date: