New approach to fast and precise optimization with evolutionary algorithms

• Autorzy: Wawrzyniak D. , Obuchowicz A.
• Konferencja: Evolutionary Computation and Global Optimization 2006 / National Conference (9 ; 31.05-2.06.2006 ; Murzasichle, Poland)
• Języki publikacji: EN

Abstrakty

EN

The paper deals with an evolutionary algorithm which uses new methods to control the range of mutation. In order to significantly increase the efficiency of finding the optimum, it discovers and exploits knowledge about the state of population in environment in every generation. It allows to be found a solution both quickly and precisely. Due to the division of population into objects dealing with different functions of optimization, it can simultaneously explore as well as exploit solution space. The algorithm works in such a way that it is unable to undergo a premature convergence. There is no possibility of falling into a trap of local optimum. Therefore, it is possible to increase a selective pressure safely, for example with the help of elitist succession.

Słowa kluczowe

EN

optimization; evolutionary algorithm; EA; evolutionary computation

PL

optymalizacja; algorytm ewolucyjny; obliczenia ewolucyjne

• Wydawca: Oficyna Wydawnicza Politechniki Warszawskiej
• Czasopismo: Prace Naukowe Politechniki Warszawskiej. Elektronika
• Rocznik: 2006
• Tom: z. 156
• Strony: 397--404
• Opis fizyczny: Bibliogr. 6 poz., tab., rys., wykr.

Twórcy

autor

Wawrzyniak D.

autor

Obuchowicz A.

• Faculty of Electrical Engineering, Computer Science and Telecommunications, University of Zielona Góra, Poland,

Bibliografia

• [1] J. Arabas. Wykłady z algorytmów ewolucyjnych. Wydawnictwo Naukowo Techniczne, 2001.
• [2] D.E. Goldberg. Genetic Algorithms in Search, Optimization and Machine Learning. - Addison-Wesley, Reading, MA, 1989.
• [3] Z. Michalewicz. Genetic Algorithms + Data Structures = Evolution Programs. Springer-Verlag, 1996.
• [4] A. Obuchowicz. Evolutionary Algorithms for Global Optimization and Dynamic System Diagnosis. Lubuskie Scientific Society Press, 2003.
• [5] G. Cervone, R.S. Michalski and K. Kaufman. Speeding Up Evolution through Learning: LEM. Proc. Ninth Int. Symp. Intelligent Information Systems, 2000.
• [6] K. Trojanowski. Evolutionary Algorithm with Redundant Genetic Material for Non-stationary Environments. Polish Academy of Science, 2003.