New genetic algorithm based on dissimilarities and similarities

• Autorzy: Al-Jawadi R. , Studniarski M. , Younus A.

Identyfikatory

DOI

10.7494/csci.2018.19.1.2522

• Języki publikacji: EN

Abstrakty

EN

Optimization is essential for nding suitable answers to real life problems. In particular, genetic (or more generally, evolutionary) algorithms can provide satisfactory approximate solutions to many problems to which exact analytcal results are not accessible. In this paper we present both theoretical and experimental results on a new genetic algorithm called Dissimilarity and Simlarity of Chromosomes (DSC). This methodology constructs new chromosomes starting with the pairs of existing ones by exploring their dissimilarities and similarities. To demonstrate the performance of the algorithm, it is run on 17 two-dimensional, one four-dimensional and two ten-dimensional optimization problems described in the literature, and compared with the well-known GA, CMA-ES and DE algorithms. The results of tests show the superiority of our strategy in the majority of cases.

Słowa kluczowe

EN

genetic algorithm; forma analysis; similarity and dissimilarity of chromosomes; chromosome injection

• Wydawca: Wydawnictwa AGH
• Czasopismo: Computer Science
• Rocznik: 2018
• Tom: Vol. 19 (1)
• Strony: 23--41
• Opis fizyczny: Bibliogr. 18 poz., rys., wykr., tab.

Twórcy

autor

Al-Jawadi R.

• University of Warsaw, Faculty of Mathematics, Informatics and Mechanics, Poland, (permanent address: Technical College of Mosul, FTE, Iraq)

autor

Studniarski M.

• University of Lodz, Faculty of Mathematics and Computer Science, Lodz, Poland

autor

Younus A.

• University of Lodz, Faculty of Mathematics and Computer Science, Lodz, Poland

Bibliografia

• [1] CMA-ES. Matlab program. URL https://www.mathworks.com/ matlabcentral/fileexchange/52898-cma-es-in-matlab.
• [2] Differential Evolution Optimization Lecture. Matlab program. URL http://www.dii.unipd.it/~alotto/didattica/corsi/Elettrotecnica% 20computazionale/DE.pdf.
• [3] Auger A., Hansen N.: A restart CMA evolution strategy with increasing population size. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC), pp. 1769-1776. 2005.
• [4] Berretta R., Cotta C., Moscato P.: Enhancing the performance of Memetic algorithms by using a matching-based recombination algorithm. Results on the number partitioning problem. In: M. Resende, J. Pinho de Sousa, eds., Metaheuristics: Computer-Decision Making, pp. 65-90. 2003.
• [5] Eesa A., Brifcani A., Orman Z.: A new tool for global optimization problems - Cuttlefish Algorithm. In: International Journal of Mathematical, Computational, Natural and Physical Engineering, vol. 8(9), pp. 1208-1211, 2014.
• [6] Eiben A., Smith J.: Introduction to Evolutionary Computing. Springer, 2003.
• [7] Gen M.: Network Models and Optimization: Multiobjective GA Approach, 2009. URL http://logistics.iem.yzu.edu.tw/Gen/seminarII-YZU-IEM-MGen. pdf.
• [8] Han X., Liang Y., Li Z., Li G., Wu X., Wang B., Zhao G., Wu C.: An Efficient Ge-netic Algorithm for Optimization Problems with Time-Consuming Fitness Eval-uation. In: International Journal of Computational Methods, vol. 12(01), pp. 13501065-1 - 13501065-24, 2015.
• [9] Iqbal M., Khan N., Mujtaba H., Baig A.: A Novel Function Optimization Approach Using Opposition Based Genetic Algorithm with Gene Excitation. In: International Journal of Innovative Computing, Information and Control, vol. 7(7B), pp. 4263-4276, 2011.
• [10] Lewchuk M.: Genetic invariance: a new type of genetic algorithm. Technical report TR 92-05, Dept. of Computing Science, University of Alberta, 1992.
• [11] Manda K., Satapathy S., Poornasatyanarayana B.: Population based meta-heuristic techniques for solving optimization problems: A selective survey. In:IJETAE, vol. 2(11), pp. 206-211, 2012.
• [12] Michalewicz Z.: Genetic Algorithms + Data Structures = Evolutions Programs. Springer, Berlin, 1996.
• [13] Odili J., Kahar M.: Numerical Function Optimization Solutions Using the African Buffalo Optimization Algorithm (ABO). In: British Journal of Mathematics & Computer Science, vol. 10(1), pp. 1-12, 2015.
• [14] Radcliffe N.: The algebra of genetic algorithms. In: Annals Mathematic and Artificial Intelligence, vol. 10, pp. 339-384, 1994.
• [15] Ritthipakdee A., Thammano A., Premasathian N., Uyyanonvara B.: An Iproved Firefly Algorithm for Optimization Problem. In: The 5th International Symposium on Advanced Control of Industrial Processess (ADCONIP 2014), pp. 159-164. 2014.
• [16] Scott E., Jong K.: Understanding Simple Asynchronous Evolutionary Algo-rithms. In: FOGA'15 Proceedings of the 2015 ACM Conference on Foundations of Genetic Algorithms XIII, pp. 85-98. 2015.
• [17] Sultan A., Mahmod R., Sulaiman M., Bakar M.: Maintaining diversity for genetic algorithm: A case of timetabling problem. In: Jurnal Teknologi, vol. 44(D), pp. 123-130, 2006.
• [18] Yu Y., Zhou Z.H.: A new approach to estimating the expected first hitting time of evolutionary algorithms. In: Artificial Intelligence, vol. 172(15), pp. 1809-1832, 2008.

Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).