Abstract
A cellular genetic algorithm (cGA) is a powerful metaheuristic that has been successfully used since its creation to solve optimization problems. Over the past few years, interest in hybrid metaheuristics has also grown considerably. Research into cross fertilization between algorithms has provided extremely efficient search techniques in the past. In this paper we present a new way of hybridizing a metaheuristic through active components of other metaheuristics. We also introduce a novel methodology for identifying what an active component is. The active components detected are later inserted in a host metaheuristic so as to enhance its performance with regards to efficiency and accuracy (computational symbiosis). In the approach presented here we enhance a cGA, the host metaheuristic, with identified active components of other metaheuristics. After using this computational symbiosis, we analyze the performance of the new resulting algorithms by evaluating them on a set of different well-known discrete problems. The results obtained are objectively satisfactory in efficacy and efficiency.
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Alba E, Dorronsoro B (2008) Cellular genetic algorithms. Springer, Heidelberg
Alba E, Villagra A (2012) EVOLVE—a bridge between probability, set oriented numerics and evolutionary computation, vol 447, Springer Series, studies in computational intelligence, chap hybridizing cGAs with PSO-like mutation, pp 289–302
Alba E, Villagra A (2013) Hybrid metaheuristics, vol 434, chap hybridizing cellular GAs with active components of bio-inspired algorithms, Springer, Berlin Heidelberg, pp 121–133
Blum C, Roli A, Alba E (2005) An introduction to metaheuristic techniques. Parallel Metaheur New Class Algorithms 47:3–42
Cotta-Porras C (1998) A study of hybridisation techniques and their application to the design of evolutionary algorithms. AI Commun 11(3):223–224
Droste S, Jansen T, Wegener I (2000) A natural and simple function which is hard for all evolutionary algorithms. In: 3rd Asia-Pacific conference on simulted evolution and learning (SEAL), pp 2704–2709
El-Abd M, Kamel M (2005) A taxonomy of cooperative search algorithms. In: Blesa MJ, Blum C, Roli A, Sampels M (eds) Second international workshop on hybrid metaheuristics. Lecture notes in computer science, vol 3636. Springer, Heidelberg, pp 32–41
Fabian A (2006) Evolution: society, science and the universe, vol 9. Cambridge University Press, Cambridge
Fontdevila A, Moya A (2003) Evolución: origen, adaptación y divergencia de las especies. Síntesis
García S, Fernández A, Luengo J, Herrera F (2010) Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: experimental analysis of power. Inf Sci 180(10):2044–2064 (special issue on intelligent distributed information systems)
Goldberg D, Deb K, Horn J (1992) Massive multimodality, deception, and genetic algorithms. In: Männer R, Manderick B (eds) International conference on parallel problem solving from nature II, pp 37–46
Hart W, Krasnogor N, Smith J (2005) Recent advances in memetic algorithms. Springer, Berlin
Jong KD, Potter M, Spears W (1997) Using problem generators to explore the effects of epistasis. 7th international Morgan Kaufmann, conference genetic algorithms, pp 338–345
Kennedy J, Eberhart R (1995) Particle swarm optimization. IEEE Int Conf Neural Netw 4:1942–1948
Kennedy J, Eberhart R (1997) A discrete binary version of the particle swarm algorithm. In: Systems, man, and cybernetics, 1997. Computational cybernetics and Simulation, 1997 IEEE international conference on, IEEE, vol 5, pp 4104–4108
Khuri S, Bäck T, Heitkötter J (1994) An evolutionary approach to combinatorial optimization problems. In: 22nd annual ACM C.S. conference, pp 66–73
Kirkpatrick S, Gelatt J, Vecchi M (1983) Optimization by simulated annealing. Science 220:671–680
MacWilliams F, Sloane N (1977) The theory of error-correcting codes: part 2, vol 16. Elsevier, Amsterdam
Metropolis N, Rosenbluth A, Rosenbluth M, Teller A, Teller E (1953) Equations of state calculations by fast computing machines. Chem Phys 21(6):1087–1092
Papadimitriou C (1994) Computational complexity. Addison-Wesley, Reading
Puchinger J, Raidl G (2005) Combining metaheuristics and exact algorithms in combinatorial optimization: a survey and classification. In: Mira J, Alvarez JR (eds) Artificial intelligence and knowledge engineering applications: a bioinspired approach. Lecture notes in computer science, vol 3562. Springer, Berlin, Heidelberg, pp 41–53
Raidl G (2006) A unified view on hybrid metaheuristics. In: Proceedings of 3rd international workshop on hybrid metaheuristics. Springer, Heidelberg, pp 1–12
Schaffer J, Eshelman L (1991) On crossover as an evolutionary viable strategy. In: Belew R, Booker L (eds) Proceedings of the 4th ICGA, Morgan Kaufmann, pp 61–68
Stinson D (1985) An introduction to the design and analysis of algorithms. The Charles Babbage Research Centre, St. Pierre
Talbi EG (2002) A taxonomy of hybrid metaheuristics. J Heuristics 8(5):541–564
Tomassimi M (1993) The parallel genetic cellular automata: application to global function optimization. In: Albrecht R, Reeves C, Steele N (eds) International conference on artificial neural networks and genetic algorithms. Springer-Verlag, Heidelberg, pp 385–391
Tsutsui S, Fujimoto Y (1993) Forking genetic algorithm with blocking and shrinking modes. In: Forrest S (ed) 5th ICGA, Morgan Kaufmamann, pp 206–213
Villagra A, Leguizamón G, Alba E (2012) Cellular GAs with active components of PSO: mutation and crossover. In: XVIII Congreso Argentino de Ciencias de la Computación
Whitley D (1993) Cellular genetic algorithms. In: Forrest S (ed) 5th ICGA, Morgan Kaufmann, p 658
Acknowledgments
We acknowledge the constant support of the Universidad Nacional de la Patagonia Austral. The second author also acknowledges the constant support by the Universidad Nacional de San Luis and the ANPCYT that finances his current research. The third author acknowledges the Spanish Ministry of Sciences and Innovation (MICINN) and FEDER under contracts TIN2011-28194 (RoadMe http://roadme.lcc.uma.es) and contract 8.06/5.47.4142 with the VSB-Technical University of Ostrava (Czech Republic).
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Communicated by V. Loia.
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Villagra, A., Leguizamón, G. & Alba, E. Active components of metaheuristics in cellular genetic algorithms. Soft Comput 19, 1295–1309 (2015). https://doi.org/10.1007/s00500-014-1341-6
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DOI: https://doi.org/10.1007/s00500-014-1341-6