(μ +λ) Evolution Strategy with Socio-Cognitive Mutation

[1] Z. Michalewicz and D. B. Fogel, How to Solve It: Modern Heuristics. Springer Science & Business Media, 2013. Search in Google Scholar

[2] D. H. Wolpert and W. G. Macready, “No free lunch theorems for optimization,” IEEE Transactions on Evolutionary Computation, vol. 1, no. 1, Apr. 1997, pp. 67–82, doi: 10.1109/4235.585893. Search in Google Scholar

[3] E.-G. Talbi, Metaheuristics: From Design to Implementation. John Wiley & Sons, 2009. Search in Google Scholar

[4] K. Sörensen, “Metaheuristics—the metaphor exposed,” International Transactions in Operational Research, vol. 22, no. 1, 2015, pp. 3–18, doi: 10.1111/itor.12001. Search in Google Scholar

[5] G. Lakoff and M. Johnson, Metaphors We Live By. Chicago, IL: University of Chicago Press, 2003. Accessed: Feb. 16, 2024. [Online]. Available: https://press.uchicago.edu/ucp/books/book/chicago/M/bo3637992.html. Search in Google Scholar

[6] A. Bandura, “Self-efficacy: Toward a unifying theory of behavioral change,” Psychological Review, vol. 84, no. 2, 1977, pp. 191–215, doi: 10.1037/0033-295X.84.2.191. Search in Google Scholar

[7] A. Bandura, Social foundations of thought and action: A social cognitive theory. in Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ, US: Prentice-Hall, Inc, 1986, pp. xiii, 617. Search in Google Scholar

[8] A. Bandura, D. Ross, and S. A. Ross, “Transmission of aggression through imitation of aggressive models,” The Journal of Abnormal and Social Psychology, vol. 63, no. 3, 1961, pp. 575–582, doi: 10.1037/h0045925. Search in Google Scholar

[9] A. Byrski, et al., “Socio-cognitively inspired ant colony optimization,” Journal of Computational Science, vol. 21, Jul. 2017, pp. 397–406, doi: 10.1016/j.jocs.2016.10.010. Search in Google Scholar

[10] I. Bugajski, et al., “Enhancing Particle Swarm Optimization with Socio-cognitive Inspirations,” Procedia Computer Science, vol. 80, Jan. 2016, pp. 804–813, doi: 10.1016/j.procs.2016.05.370. Search in Google Scholar

[11] A. Urbanczyk, B. Nowak, P. Orzechowski, J. H. Moore, M. Kisiel-Dorohinicki, and A. Byrski, “Socio-cognitive Evolution Strategies,” in Computational Science – ICCS 2021, M. Paszynski, D. Kranzlmüller, V. V. Krzhizhanovskaya, J. J. Dongarra, and P. M. A. Sloot, Eds., in Lecture Notes in Computer Science. Cham: Springer International Publishing, 2021, pp. 329–342. doi: 10.1007/978-3-030-77964-1_26. Search in Google Scholar

[12] E.-G. Talbi, “A Taxonomy of Hybrid Metaheuristics,” Journal of Heuristics, vol. 8, no. 5, Sep. 2002, pp. 541–564, doi: 10.1023/A:1016540724870. Search in Google Scholar

[13] Y.-S. Ong, M.-H. Lim, N. Zhu, and K.-W. Wong, “Classification of adaptive memetic algorithms: a comparative study,” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 36, no. 1, Feb. 2006, pp. 141–152, doi: 10.1109/TSMCB.2005.856143. Search in Google Scholar

[14] Robert Schaefer, Aleksander Byrski, Joanna Kolodziej, and Maciej Smolka. An agent-based model of hierarchic genetic search. Comput. Math. Appl., 64(12):3763–3776, 2012. Search in Google Scholar

[15] Kamil Pietak, Adam Wos, Aleksander Byrski, and Marek Kisiel-Dorohinicki. Functional integrity of multi-agent computational system supported by component-based implementation. In Vladimír Marík, Thomas I. Strasser, and Alois Zoitl, editors, Holonic and Multi-Agent Systems for Manufacturing, 4th International Conference on Industrial Applications of Holonic and Multi- Agent Systems, HoloMAS 2009, Linz, Austria, August 31 - September 2, 2009. Proceedings, volume 5696 of Lecture Notes in Computer Science, pages 82–91. Springer, 2009. Search in Google Scholar

[16] Robert Schaefer, Aleksander Byrski, and Maciej Smolka. Stochastic model of evolutionary and immunological multi-agent systems: Parallel execution of local actions. Fundam. Informaticae, 95(2-3):325–348, 2009. Search in Google Scholar

[17] I. Rechenberg, Cybernetic Solution Path of an Experimental Problem by Ingo Rechenberg. Royal Aircraft Establishment, 1965. Search in Google Scholar

[18] H.-P. Schwefel, Numerische Optimierung von Computer-Modellen mittels der Evolutions strategie: Mit einer vergleichenden Einführung in die Hill-Climbing- und Zufallsstrategie. Basel: Birkhäuser, 1977. doi: 10. 1007/978-3-0348-5927-1. Search in Google Scholar

[19] D. V. Arnold, “Weighted multirecombination evolution strategies,” Theoretical Computer Science, vol. 361, no. 1, Aug. 2006, pp. 18–37, doi: 10.1016/j.tcs.2006.04.003. Search in Google Scholar

[20] D. Brockhoff, A. Auger, N. Hansen, D. V. Arnold, and T. Hohm, “Mirrored Sampling and Sequential Selection for Evolution Strategies,” in Parallel Problem Solving from Nature, PPSN XI, R. Schaefer, C. Cotta, J. Kołodziej, and G. Rudolph, Eds., in Lecture Notes in Computer Science. Berlin, Heidelberg: Springer, 2010, pp. 11–21. doi: 10.1007/978-3-642-15844-5_2. Search in Google Scholar

[21] T.-Y. Huang and Y.-Y. Chen, “Modified evolution strategies with a diversity-based parentinclusion scheme,” in Proceedings of the 2000. IEEE International Conference on Control Applications. Conference Proceedings (Cat. No.00CH37162), Sep. 2000, pp. 379–384. doi: 10.1109/CCA.2000.897454. Search in Google Scholar

[22] N. Hansen and A. Ostermeier, “Completely Derandomized Self-Adaptation in Evolution Strategies,” Evolutionary Computation, vol. 9, no. 2, Jun. 2001, pp. 159–195, doi: 10.1162/1063 65601750190398. Search in Google Scholar

[23] P. P. Repoussis, C. D. Tarantilis, O. Bräysy, and G. Ioannou, “A hybrid evolution strategy for the open vehicle routing problem,” Computers & Operations Research, vol. 37, no. 3, Mar. 2010, pp. 443–455, doi: 10.1016/j.cor.2008.11.003. Search in Google Scholar

[24] D. Koulocheris, H. Vrazopoulos, and V. Dertimanis, “Hybrid evolution strategy for the design of welded beams,” in Proc. of Int. Congress on Evolutionary Methods for Design, Optimization and Control with Applications to Industrial Problems EUROGEN 2003, 2003. Search in Google Scholar

[25] L. Dos Santos Coelho and P. Alotto, “Electromagnetic device optimization by hybrid evolution strategy approaches,” COMPEL – The international journal for computation and mathematics in electrical and electronic engineering, vol. 26, no. 2, Apr. 2007, pp. 269–279, doi: 10.1108/03321640710727638. Search in Google Scholar

[26] R. Storn and K. Price, “Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces,” Journal of Global Optimization, vol. 11, no. 4, Dec. 1997, pp. 341–359, doi: 10.1023/A:1008202821328. Search in Google Scholar

[27] Kenneth Price, Rainer M. Storn, and Jouni A. Lampinen. Differential Evolution. in Natural Computing Series. Berlin/Heidelberg: Springer- Verlag, 2005. doi: 10.1007/3-540-31306-0. Search in Google Scholar

[28] C.-L. Hwang, Y.-J. Lai, and T.-Y. Liu, “A new approach for multiple objective decision making,” Computers & Operations Research, vol. 20, no. 8, Oct. 1993, pp. 889–899, doi: 10.1016/0305-0548(93)90109-V. Search in Google Scholar

[29] M. Nabywaniec, et al., “Socio-cognitive Optimization of Time-delay Control Problems using Agent-based Metaheuristics,” in 2022 IEEE 11th International Conference on Intelligent Systems (IS), Oct. 2022, pp. 1–7. doi: 10.1109/IS57118. 2022.10019693. Search in Google Scholar

[30] P. Kipinski, et al., “Socio-cognitive Optimization of Time-delay Control Problems using Evolutionary Metaheuristics.” arXiv, Oct. 23, 2022. doi: 10.48550/arXiv.2210.12872. Search in Google Scholar

[31] J. Dieterich and B. Hartke, “Empirical Review of Standard Benchmark Functions Using Evolutionary Global Optimization,” Applied Mathematics, vol. 03, Jul. 2012, doi: 10.4236/am.2012.330215. Search in Google Scholar

[32] M. López-Ibáñez, J. Dubois-Lacoste, L. Pérez Cáceres, M. Birattari, and T. Stützle, “The irace package: Iterated racing for automatic algorithm configuration,” Operations Research Perspectives, vol. 3, Jan. 2016, pp. 43–58, doi: 10.1016/j.orp.2016.09.002. Search in Google Scholar