PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2006 | 16 | 1 | 59-84
Tytuł artykułu

Niching mechanisms in evolutionary computations

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Different types of niching can be used in genetic algorithms (GAs) or evolutionary computations (ECs) to sustain the diversity of the sought optimal solutions and to increase the effectiveness of evolutionary multi-objective optimization solvers. In this paper four schemes of niching are proposed, which are also considered in two versions with respect to the method of invoking: a continuous realization and a periodic one. The characteristics of these mechanisms are discussed, while as their performance and effectiveness are analyzed by considering exemplary multi-objective optimization tasks both of a synthetic and an engineering (FDI) design nature.
Rocznik
Tom
16
Numer
1
Strony
59-84
Opis fizyczny
Daty
wydano
2006
otrzymano
2005-11-18
poprawiono
2006-01-08
Twórcy
  • Faculty of Electronics, Telecommunications and Computer Science, Gdańsk University of Technology, ul. Narutowicza 11/12, 80-952 Gdańsk, Poland
  • Faculty of Electronics, Telecommunications and Computer Science, Gdańsk University of Technology, ul. Narutowicza 11/12, 80-952 Gdańsk, Poland
Bibliografia
  • Brogan W.L. (1991): Modern Control Theory. - Englewood Cliffs, NJ: Prentice Hall.
  • Chen J., Patton R.J. and Liu G. (1996): Optimal residual design for fault diagnosis using multi-objective optimization and genetic algorithms. - Int. J. Syst. Sci., Vol. 27, No. 6, pp. 567-576.
  • Chambers L. (Ed.) (1995): Practical Handbook of Genetic Algorithms. - Boca Raton, FL: CRC Press.
  • Coello C.C.A. (2001): A short tutorial on evolutionary multiobjective optimization. - Proc. 1st Int. Conf. Evolutionary Multi-Criterion Optimization, Lecture Notes in Computer Science, No. 1993, pp. 21-40, Berlin: Springer.
  • Cotta C. and Schaefer R. (Eds.) (2004): Evolutionary Computation. - Int. J. Appl. Math. Comput. Sci., Vol. 14, No. 3, pp. 279-440.
  • Deb K., Pratap A., Argarwal S. and Meyarivan T. (2000): A fast and elitist multi-objective genetic algorithm: NSGA-II. - Techn. Rep., No. 200001 (PIN 208 016), Kanpur, India: Kanpur Genetic Algorithms Laboratory.
  • De Jong K.A. (1975): An analysis of the behavior of a class of genetic adaptive systems. - Ph.D. thesis., Ann Arbor, MI: University of Michigan.
  • Dridi M. and Kacem I. (2004): A hybrid approach for scheduling transportation networks. - Int. J. Appl. Math. Comput. Sci., Vol. 14, No. 3, pp. 397-409.
  • Fogarty T.C. and Bull L. (1995): Optimizing individual control rules and multiple communicating rule-based control systems with parallel distributed genetic algorithms. - IEE Proc. Contr. Theory Applic., Vol. 142, No. 3, pp. 211-215.
  • Fonseca C.M. and Fleming P.J. (1993): Genetic algorithms for multi-objective optimization: Formulation, discussion and modification. - In: (Forrest, 1993), pp. 416-423.
  • Forrest S. (Ed.) (1993): Genetic Algorithms. - Proc. 5th Int. Conf., San Mateo, CA: Morgan Kaufmann.
  • Goldberg D.E. (1986): The genetic algorithm approach: Why, how, and what next. In: Adaptive and Learning Systems. Theory and Applications (K.S. Narendra, Ed.). - New York: Plenum Press, pp. 247-253.
  • Goldberg D.E. (1989): Genetic Algorithms in Search, Optimization and Machine Learning. - Reading, MA: Addison-Wesley.
  • Goldberg D.E. (1990): Real-coded genetic algorithms, virtual alphabets, and blocking. - Techn. Rep., No. 90001, Champaing, IL: University of Illinois at Urbana.
  • Grefenstette J.J. (Ed.) (1985): Genetic Algorithms and their Applications. - Proc. Int. Conf., Pittsburgh, PA: Lawrence Erlbaum Associates.
  • Hajela P. and Lin C.-Y. (1992): Genetic search strategies in multicriterion optimal design. - Struct. Optim., Vol. 4, No. 2, pp. 99-107.
  • Holland H. (1975): Adaptation in Natural and Artificial Systems. - Ann Arbor, MI: University of Michigan Press.
  • Horn J. and Nafpliotis N. (1993): Multiobjective optimization using the niched Pareto genetic algorithm. - Techn. Rep., No. 93005, Genetic Algorithms Laboratory, University of Illinois at Urbana.
  • Horn J., Nafpliotis N. and Goldberg D.E. (1994): A niched Pareto genetic algorithm for multiobjective optimization. - Proc. 1st IEEE Conf. Evolutionary Computation, IEEE World Congress Computational Computation, Piscataway, NJ, Vol. 1, pp. 82-87.
  • Huang Y. and Wang S. (1997): The identification of fuzzy grey prediction systems by genetic algorithms. - Int. J. Syst. Sci., Vol. 28, No. 1, pp. 15-24.
  • Izadi-Zamanabadi R. and Blanke M. (1998): A ship propulsion system model for fault-tolerant control. - Techn. Rep., No. 4262, Aalborg University, Denmark.
  • Kirstinsson K. (1992): System identification and control using genetic algorithms. - IEEE Trans. Syst. Man Cybern., Vol. 22, No. 5, pp. 1033-1046.
  • Korbicz J., Kościelny J.M., Kowalczuk Z. and Cholewa W., (Eds.) (2004): Fault Diagnosis. Models, Artificial Intelligence, Applications. - Berlin: Springer.
  • Kowalczuk Z. and Białaszewski T. (2000a): Pareto-optimal observers for ship propulsion systems by evolutionary algorithms. - Proc. IFAC Symp. Safeprocess, Budapest, Hungary, Vol. 2, pp. 914-919.
  • Kowalczuk Z. and Białaszewski T. (2000b): Fitness and ranking individuals warped by niching mechanism. - Proc. Polish-German Symp. Science, Research, Education, Zielona Gora, Poland, pp. 97-102.
  • Kowalczuk Z. and Białaszewski T. (2001): Evolutionary multi-objective optimization with genetic sex recognition. - Proc. 7th IEEE Int. Conf. Methods and Models in Automation and Robotics, Międzyzdroje, Poland, Vol. 1, pp. 143-148.
  • Kowalczuk Z. and Białaszewski T. (2002): Performance and robustness design of control systems via genetic gender multi-objective optimization. - Proc. 15th IFAC World Congress, Barcelona, Spain, (CD-ROM, 2a).
  • Kowalczuk Z. and Białaszewski T. (2003): Multi-gender genetic optimization of diagnostic observers. - Proc. IFAC Workshop Control Applications of Optimization, Visegrad, Hungary, pp. 15-20.
  • Kowalczuk Z. and Białaszewski T. (2004a): Genetic algorithms in multi-objective optimization of detection observers. - In: (Korbicz et al., 2004), pp. 511-556.
  • Kowalczuk Z. and Białaszewski T. (2004b): Periodic and continuous niching in genetic optimization of detection observers. - Proc. 10-th IEEE Int. Conf. Methods and Models in Automation and Robotics, Międzyzdroje, Poland, Vol. 1, pp. 781-786.
  • Kowalczuk Z., Suchomski P. and Białaszewski T. (1999a): Evolutionary multi-objective Pareto optimization of diagnostic state observers. - Int. J. Appl. Math. Comput. Sci., Vol. 9, No. 3, pp. 689-709.
  • Kowalczuk Z., Suchomski P. and Białaszewski T. (1999b): Genetic multi-objective Pareto optimization of state observers for FDI. - Proc. Europ. Contr. Conf., Karlsruhe, Germany, (CD-ROM, CP-15:10).
  • Kowalczuk Z. and Suchomski P. (2004a): Control theory methods in diagnostic system design. In: (Korbicz et al., 2004), pp. 155-218.
  • Kowalczuk Z. and Suchomski P. (2004b): Optimal detection observers based on eigenstructure assignment. In: (Korbicz et al., 2004), pp. 219-259.
  • Li C.J., Tzeng T. and Jeon Y.C. (1997): A learning controller based on nonlinear ARX inverse model identified by genetic algorithm. - Int. J. Syst. Sci., Vol. 28, No. 8, pp. 847-855.
  • Linkens D.A. and Nyongensa H.O. (1995): Genetic algorithms for fuzzy control, Part 1: Offline system development and application. - IEE Proc. Contr. Theory Applic., Vol. 142, No. 3, pp. 161-185.
  • Man K.S., Tang K.S., Kwong S. and Lang W.A.H. (1997): Genetic Algorithms for Control and Signal Processing. - London: Springer.
  • Martinez M., Senent J. and Blacso X. (1996): A comparative study of classical vs. genetic algorithm optimization applied in GPC controller. - Proc. IFAC 13th Triennial Word Congress, San Francisco, CA, pp. 327-332.
  • Michalewicz Z. (1996): Genetic Algorithms + Data Structures = Evolution Programs. - Berlin: Springer.
  • Obuchowicz A. and Pretki P. (2004): Phenotypic evolution with mutation based on symmetric, α-stable distributions. - Int. J. Appl. Math. Comput. Sci., Vol. 14, No. 3, pp. 289-316.
  • Park D. and Kandel A. (1994): Genetic-based new fuzzy reasoning models with application to fuzzy control. - IEEE Trans. Syst., Man Cybern., Vol. 24, No. 1, pp. 39-47.
  • Patton R.J., Frank P.M. and Clark R.N., (Eds.) (1989): Fault Diagnosisin Dynamic Systems. Theory and Application. - New York: Prentice Hall.
  • Ryan C. (1995): Niche and species formation in genetic algorithms, In: (Chambers, 1995). - Vol. 1, No. 2, pp. 55-74.
  • Schaffer J.D. (1985): Multiple objective optimization with vector evaluated genetic algorithms. In: (Grefenstette, 1985). - pp. 93-100.
  • Silverman B.W. (1986): Density Estimation for Statistics and Data Analysis. - London: Chapman and Hall.
  • Srinivas N. and Deb K. (1994): Multiobjective optimization using nondominated sorting in genetic algorithms. - Evolut. Comput., Vol. 2, No. 3, pp. 221-248.
  • Suchomski P. and Kowalczuk Z. (2004): Robust H^∞-optimal synthesis of FDI systems, In: (Korbicz et al., 2004), pp. 261-298.
  • Viennet R., Fontiex C. and Marc I. (1996): Multicriteria optimisation using a genetic algorithm for determining a Pareto set. - Int. J. Syst. Sci., Vol. 27, No. 2, pp. 255-260.
  • Zakian V. and Al-Naib U. (1973): Design of dynamical and control systems by the method of inequalities. - IEE Proc. Contr. Theory Applic., Vol. 120, No. 11, pp. 1421-1427.
  • Zitzler E. and Thiele L. (1998): An evolutionary algorithm for multiobjective optimization: The Strength Pareto Evolutionary Algorithm. - Techn. Rep., No. 43, Zurich, Switzerland: Computer Engineering and Networks Laboratory, ETH.
  • Zitzler E., Laumanns M. and Thiele L. (2001): SPEA-2: Improving the strength Pareto evolutionary algorithm. - Techn. Rep., No. 103, Zurich, Switzerland: Computer Engineering and Networks Laboratory, Dept. of Electrical Engineering, ETH.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.bwnjournal-article-amcv16i1p59bwm
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.