Population Symmetrization in Genetic Algorithms
Source abstract
Abstract
The paper presents a memetic modification of the classical genetic algorithm by introducing a cyclic symmetrization of the population, symmetrizing the parental points around the current population leader. Such an operator provides a more spherical distribution of the population around the current leader, which significantly improves exploitation. The proposed algorithm was described, illustrated by examples, and theoretically analyzed. Its effectiveness was examined using a recognized benchmark, which includes the continuous functions test set on a multidimensional cube, to be minimized.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Metaheuristic Optimization Algorithms Research · Evolutionary Algorithms and Applications · Advanced Multi-Objective Optimization Algorithms
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Centre of Mathematics and Physics, Lodz University of Technology, 11 Politechniki Street, 93-590 Lodz, Poland
Institute of Applied Computer Science, Lodz University of Technology, 18 Stefanowskiego Street, 90-537 Lodz, Poland
References (43)
Show source-provided references
1. A survey on optimization metaheuristics10.1016/j.ins.2013.02.041 ↗
2. Optimization by Simulated Annealing10.1126/science.220.4598.671 ↗
3. Paths for Integer Programming10.1016/0305-0548(86)90048-1 ↗
4. General variable neighborhood search for the continuous optimization10.1016/j.ejor.2006.12.064 ↗
5. Guided Local Search—An Illustrative Example in Function Optimisation10.1023/A:1009665513140 ↗
6. Stutzle, T.G. (1998). Local Search Algorithms for Combinatorial Problems. [Ph.D. Thesis, Darmstadt University of Technology].
7. Holland, J.H. (1975). Adaptation in Natural and Artificial Systems, University of Michigan Press. [1st ed.].
8. Rechenberg, I. (1989). Evolution Strategy: Nature’s Way of Optimization. Optimization: Methods and Applications, Possibilities and Limitations, Springer.10.1007/978-3-642-83814-9_6 ↗
9. Genetic programming as a means for programming computers by natural selection10.1007/BF00175355 ↗
10. Differential evolution—A simple and efficient heuristic for global optimization over continuous spaces10.1023/A:1008202821328 ↗
11. Reynolds, R.G. (1994, January 24–26). An introduction to cultural algorithms. Proceedings of the Third Annual Conference on Evolutionary Programming, San Diego, CA, USA.
12. Dorigo, M., Maniezzo, V., Colorni, A., and Dorigo, M. (2022, April 01). Positive Feedback as a Search Strategy. Technical Report 91-016. 1991. pp. 1–20. Available online: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.52.6342.
13. Kennedy, J., and Eberhart, R. (December, January 27). Particle Swarm Optimization. Proceedings of the ICNN’95—International Conference on Neural Networks, Perth, Australia.
14. A survey: Algorithms simulating bee swarm intelligence10.1007/s10462-009-9127-4 ↗
15. An interdisciplinary perspective on artificial immune systems10.1007/s12065-007-0004-2 ↗
16. Machine Learning into Metaheuristics
17. A review on genetic algorithm: Past, present, and future10.1007/s11042-020-10139-6 ↗
18. A review of applications of genetic algorithms in operations management10.1016/j.engappai.2018.08.011 ↗
19. A genetic algorithm approach for location-inventory-routing problem with perishable products10.1016/j.jmsy.2016.10.004 ↗
20. Parallel non-dominated sorting genetic algorithm-II-based image encryption technique10.1080/13682199.2018.1505327 ↗
21. No free lunch theorems for optimization10.1109/4235.585893 ↗
22. Wright, A.H. (1991). Genetic Algorithms for Real Parameter Optimization. Foundations of Genetic Algorithms, Elsevier.10.1016/B978-0-08-050684-5.50016-1 ↗
23. Michalewicz, Z. (1996). Genetic Algorithms + Data Structures = Evolution Programs, Springer.10.1007/978-3-662-03315-9 ↗
24. A simple and efficient real-coded genetic algorithm for constrained optimization10.1016/j.asoc.2015.09.036 ↗
25. Das, A.K., and Pratihar, D.K. (2020). A Direction-Based Exponential Crossover Operator for Real-Coded Genetic Algorithm. Recent Advances in Theoretical, Applied, Computational and Experimental Mechanics, Springer.10.1007/978-981-15-1189-9_25 ↗
26. Ono, I., Kita, H., and Kobayashi, S. (2003). A Real-coded Genetic Algorithm using the Unimodal Normal Distribution Crossover. Advances in Evolutionary Computing, Springer.10.1007/978-3-642-18965-4_8 ↗
27. A new crossover operator for real coded genetic algorithms
28. Das, A.K., and Pratihar, D.K. (2018, January 12–13). A Direction-Based Exponential Mutation Operator for Real-Coded Genetic Algorithm. Proceedings of the 2018 Fifth International Conference on Emerging Applications of Information Technology (EAIT), Kolkata, India.10.1109/EAIT.2018.8470407 ↗
29. Adaptive directed mutation for real-coded genetic algorithms10.1016/j.asoc.2012.08.035 ↗
30. Trade-off between exploration and exploitation with genetic algorithm using a novel selection operator10.1007/s40747-019-0102-7 ↗
31. Chen, J.-C., Cao, M., Zhan, Z.-H., Liu, D., and Zhang, J. (2020, January 11–14). A New and Efficient Genetic Algorithm with Promotion Selection Operator. Proceedings of the 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Toronto, ON, Canada.10.1109/SMC42975.2020.9283258 ↗
32. Performance Evaluation and Population Reduction for a Self Adaptive Hybrid Genetic Algorithm (SAHGA)10.1007/3-540-45105-6_104 ↗
33. Elmihoub, T., Hopgood, A.A., Nolle, L., and Battersby, A. (2004, January 13–16). Performance of hybrid genetic algorithms incorporating local search. Proceedings of the 18th European Simulation Multiconference, Nottingham, UK. Available online: http://scs-europe.net/services/esm2004/pdf/esm-56.pdf.
34. Konak, A., and Smith, A. (1999, January 6–9). A hybrid genetic algorithm approach for backbone design of communication networks. Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406), Washington, DC, USA.
35. Hedar, A.-R., and Fukushima, M. (2003). Simplex Coding Genetic Algorithm for the Global Optimization of Nonlinear Functions. Multi-Objective Programming and Goal Programming, Springer.10.1007/978-3-540-36510-5_17 ↗
36. Chaiyaratana, N., and Zalzala, A.M.S. (2000, January 16–19). Hybridisation of neural networks and a genetic algorithm for friction compensation. Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No. 00TH8512), La Jolla, CA, USA.
37. Memetic algorithms and memetic computing optimization: A literature review10.1016/j.swevo.2011.11.003 ↗
38. Goldberg, D.E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley Longman Publishing Co., Inc.
39. Dawkins, R. (2006). The Selfish Gene: 30th Anniversary Edition, Oxford University Press.
40. Norman, M.G., and Moscato, P. (, January July). A Competitive-Cooperative Approach to Complex Combinatorial Search. Proceedings of the 20th Informatics and Operations Research Meeting, Available online: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.44.776&rep=rep1&type=pdf.
41. Hart, W.E., Krasnogor, N., and Smith, J.E. (2006). Memetic Evolutionary Algorithms. Recent Advances in Memetic Algorithms, Springer.10.1007/3-540-32363-5 ↗
42. Hansen, N., Auger, A., Finck, S., and Ros, R. (2010). Real-Parameter Black-Box Optimization Benchmarking BBOB-2010: Experimental Setup, INRIA. Available online: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.168.5204&rep=rep1&type=pdf.
43. Finck, S., and Ros, R. (2022, April 01). Real-Parameter Black-Box Optimization Benchmarking 2010: Noiseless Functions Definitions. 2010. pp. 1–12. Available online: http://coco.gforge.inria.fr/bbob2010-downloads.