Linear projection procedure in an evolutionary algorithm

• Autorzy: Kaminski W. , Tomczak E.
• Konferencja: Evolutionary Computation and Global Optimization 2006 / National Conference (9 ; 31.05-2.06.2006 ; Murzasichle, Poland)
• Języki publikacji: EN

Abstrakty

EN

From the view point of practical applications and the theory of evolutionary algorithms, an important problem is the location of many local extrema showing at the same time the position of a global extremum. To accomplish this task, a new space exploration procedure is proposed using a so-called "linear projection". Additionally, a clustering technique is introduced to the selection procedure. Operation of the method is illustrated by searching the function of two variables with extrema separated by a tableland with a box-type limitation. Each time, irrespective of the number of clusters, calculations enabled location of all three extrema.

Słowa kluczowe

EN

evolutionary computation; evolutionary algorithm; EA; linear projection; global optimization

PL

obliczenia ewolucyjne; algorytm ewolucyjny; rzut liniowy; projekcja liniowa; optymalizacja globalna

• Wydawca: Oficyna Wydawnicza Politechniki Warszawskiej
• Czasopismo: Prace Naukowe Politechniki Warszawskiej. Elektronika
• Rocznik: 2006
• Tom: z. 156
• Strony: 189--194
• Opis fizyczny: Bibliogr. 7 poz., tab., rys., wykr.

Twórcy

autor

Kaminski W.

autor

Tomczak E.

• Technical University of Lodz, Faculty of Process and Environmental Engineering, Lodz, Poland,

Bibliografia

• [1] Arabas J., Lecture Notes in Evolutionary Computation (in Polish), WNT, 2001.
• [2] Chorążyczewski A., Galar R., The Impact of Nonheritable Variance on Simulated Evolutionary Adaptation, pp. 49-56, Proceedings of VI Conference on Evolutionary Algorithms and Global Optimization, Łagów, 2004
• [3] Rao S.S., Engineering Optimization. Theory and Practice, John Wiley &Sons, 1996
• [4] Schitt L.M., Theory of genetic algorithms, Theoretical Computer Science, Vol. 259, Issue 1-2, pp. 1-61, 2001
• [5] Rutkowski L., Methods and Techniques in Artificiai Intelligence, (in Polish), WNT, 2005
• [6] Schaefer R., Adamska K., On Genetic Clustering Using Finite Mixture Models-Error Estimation and Practical Tests, pp. 183-190, Proceedings of VI Conference on Evolutionary Algorithms and Global Optimization, Łagów, 2004
• [7] Maulik, Ujjwal, Bandyopadhyay, Sanghamitra, Genetic Algorithm-based Clustering Technique, Pattern Recognition, Vol. 33, Issue 9, pp. 1455-1465, 2000