Comparison of certain evolution-inspired algorithms

• Autorzy: Kluger, Roman
• Języki publikacji: EN

Abstrakty

EN

Purpose: This paper aims at making a comparison of three optimization algorithms - standard Genetic Algorithm and its two modifications: Extended Compact Genetic Algorithm and Population-based Incremental Learning. Design/methodology/approach: To reach the objectives of the paper the solver based on algorithms was developed. Certain test functions were applied to test them and evaluate their performance. Findings: Modifications of Genetic Algorithm reach optimal values faster and more precisely. Research limitations/implications: Problem of optimization of certain cost functions frequently occurs in many management problems of organizing the optimal workflow in organizations. It can be used also in engineering problems of designing optimal devices at lowest possible cost. Practical implications: One can optimize function faster using discussed algorithms than by using standard evolutionary algorithm. Originality/value: The paper shows results of comparisons of three algorithms, discusses how tuning meta parameters helps to increase their efficiency and accuracy.

Słowa kluczowe

PL

optymalizacja; algorytmy genetyczne; matematyka stosowana

EN

optimization; genetic algorithms; applied mathematics

• Czasopismo: Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska
• Rocznik: 2023
• Tom: z. 176
• Strony: 263--275
• Opis fizyczny: Bibliogr. 12 poz.

Twórcy

autor

Kluger, Roman

• Silesian University of Technology,

Bibliografia

• 1. Chen, Y.P., Chen, C.H., (2010). Enabling the extended compact genetic algorithm for realparameter optimization by using adaptive discretization. Evol Comput. 2010 Summer, pp. 199-228, doi: 10.1162/evco.2010.18.2.18202.
• 2. Duque, T., Goldberg, D., Sastry, K. (2008). Improving the Efficiency of the Extended Compact Genetic Algorithm. GECCO '08, pp. 467-468, doi: 10.1145/1389095.1389181.
• 3. Grisales-Norena, L.F., Gonzalez Montoya, D., Ramos-Paja, C.A. (2018). Optimal Sizing and Location of Distributed Generators Based on PBIL and PSO Techniques. Energies, 11, 1018. https://doi.org/10.3390/en11041018.
• 4. Goldberg., D. (2006). Genetic Algorithms in Search, Optimization, and Machine Learning. Boston: Addison-Wesley.
• 5. Kieszek, R., Kachel, S., Kozakiewicz, A. (2023). Modification of Genetic Algorithm Based on Extinction Events and Migration. Applied Sciences, 13, 5584, doi: 10.3390/app13095584.
• 6. Michalewicz, Z. (1996). Genetic Algorithms + Data Structures = Evolution Programs. Berlin/Heidelberg: Springer, https://doi.org/10.1007/978-3-662-03315-9.
• 7. Rastegar, R., Hariri, A. (2006). The Population-Based Incremental Learning Algorithm converges to local optima. Neurocomputing, pp. 17772-1775, doi: 10.1016/j .neucom.2005.12.116 8.
• 8. Satman, M.H., Akadal, E. (2020). Machine Coded Compact Genetic Algorithms for Real Parameter Optimization Problems. Alphanumeric Journal , 8(1) , 43-58 . DOI: 10.17093/alphanumeric.576919.
• 9. da Silva, M.H,, Legey, A.P., de A. Mól, A.C. (2018). The evolution of PBIL algorithm when used to solve the nuclear reload optimization problem. Annals of Nuclear Energy, Vol. 113, pp. 393-398, doi: https://doi.org/10.1016/_j.anucene.2017.11.043.
• 10. Tamilselvi, S. (2022). Introduction to Evolutionary Algorithms. IntechOpen. doi: 10.5772/intechopen.104198.
• 11. Verma, A., Llora, X., Venkataraman, S., Goldberg, D.E., Campbell, R.H. (2010). Scaling eCGA model building via data-intensive computing. IEEE Congress on Evolutionary Computation. Barcelona, Spain, pp. 1-8, doi: 10.1109/CEC.2010.5586468.
• 12. Vikhar, P.A. (2016). Evolutionary algorithms: A critical review and its future prospects. International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC). Jalgaon, India, pp. 261-265, doi: 10.1109/ICGTSPICC.2016.7955308.

Identyfikatory

DOI

10.29119/1641-3466.2023.176.16