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Biologically Inspired Parent Selection in Genetic Algorithms

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Abstract

In this paper we suggest a new rule for parent selection in genetic algorithms inspired by natural evolutionary processes. The new rule is simple to implement in any genetic or hybrid genetic algorithm. We also review some biological principles that inspire genetic algorithms and their extensions. The new rule is tested on the planar p-median problem, also termed the location–allocation problem or the multi-source Weber problem, and the quadratic assignment problem. The genetic algorithm incorporating the new rule provided better results without increasing the computing time including five new best known solutions to well researched problem instances.

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References

  • Ahmed, Z. H. (2016). Experimental analysis of crossover and mutation operators on the quadratic assignment problem. Annals of Operations Research, 247, 833–851.

    Google Scholar 

  • Anderson, J. T., Inouye, D. W., McKinney, A. M., Colautti, R. I., & Mitchell-Olds, T. (2012). Phenotypic plasticity and adaptive evolution contribute to advancing flowering phenology in response to climate change. Proceedings of the Royal Society of London B: Biological Sciences, 279(1743), 3843–3852.

    Google Scholar 

  • Andersson, M., & Simmons, L. W. (2006). Sexual selection and mate choice. Trends in Ecology & Evolution, 21, 296–302.

    Google Scholar 

  • Back, T. (1994). Selective pressure in evolutionary algorithms: A characterization of selection mechanisms. In Proceedings of the first IEEE conference on evolutionary computation, 1994. IEEE world congress on computational intelligence (pp. 57–62).

  • Bacon, C. D., Michonneau, F., Henderson, A. J., McKenna, M. J., Milroy, A. M., & Simmons, M. P. (2013). Geographic and taxonomic disparities in species diversity: Dispersal and diversification rates across Wallace’s line. Evolution, 67, 2058–2071.

    Google Scholar 

  • Bongartz, I., Calamai, P. H., & Conn, A. R. (1994). A projection method for \(\ell _p\) norm location-allocation problems. Mathematical Programming, 66, 238–312.

    Google Scholar 

  • Bozkaya, B., Zhang, J., & Erkut, E. (2002). An efficient genetic algorithm for the \(p\)-median problem. In Z. Drezner & H. W. Hamacher (Eds.), Facility location: Applications and theory. Berlin: Springer.

    Google Scholar 

  • Brimberg, J., Hansen, P., & Mladenović, N. (2006). Decomposition strategies for large-scale continuous location-allocation problems. IMA Journal of Management Mathematics, 17, 307–316.

    Google Scholar 

  • Brown, J. H., & Kodric-Brown, A. (1977). Turnover rates in insular biogeography: Effect of immigration on extinction. Ecology, 58, 445–449.

    Google Scholar 

  • Burkard, R. E. (1990). Locations with spatial interactions: The quadratic assignment problem. In P. B. Mirchandani & R. L. Francis (Eds.), Discrete location theory (pp. 387–437). New York, NY: Wiley.

    Google Scholar 

  • Cela, E. (1998). The quadratic assignment problem: Theory and algorithms. Dordrecht: Kluwer Academic Publishers.

    Google Scholar 

  • Chen, R. (1983). Solution of minisum and minimax location–allocation problems with Euclidean distances. Naval Research Logistics Quarterly, 30, 449–459.

    Google Scholar 

  • Cooper, L. (1963). Location–allocation problems. Operations Research, 11, 331–343.

    Google Scholar 

  • Cooper, L. (1964). Heuristic methods for location–allocation problems. SIAM Review, 6, 37–53.

    Google Scholar 

  • Crawley, M. J. (1997). Biodiversity. In M. J. Crawley (Ed.), Plant ecology (pp. 595–632). Oxford: Blackwell Science.

    Google Scholar 

  • Cwynar, L. C., & MacDonald, G. M. (1987). Geographical variation of lodgepole pine in relation to population history. The American Naturalist, 129, 463–469.

    Google Scholar 

  • Darwin, C. (1859). The origin of species by means of natural selection, or, The preservation of favored races in the struggle for life (Vol. 2). London: The Werner Company.

    Google Scholar 

  • de Carvalho Jr., S. A., & Rahmann, S. (2006). Microarray layout as a quadratic assignment problem. In D. Huson, O. Kohlbacher, A. Lupas, K. Nieselt, & A. Zell (Eds.), Proceedings of the German conference on bioinformatics (vol. 83, pp. 11–20). Bonn: Gesellschaft für Informatik.

  • Dieckmann, U., & Doebeli, M. (1999). On the origin of species by sympatric speciation. Nature, 400(6742), 354–357.

    Google Scholar 

  • Drezner, T., & Drezner, Z. (2005). Genetic algorithms: Mimicking evolution and natural selection in optimization models. In Y. Bar-Cohen (Ed.), Biomimetics—Biologically inspired technologies (pp. 157–175). Boca Raton, FL: CRC Press.

    Google Scholar 

  • Drezner, T., & Drezner, Z. (2006). Gender specific genetic algorithms. INFOR, Information Systems and Operations Research, 44, 117–127.

    Google Scholar 

  • Drezner, T. D. (2004). Few native species colonize on mud flats ten years after dam removal (Wisconsin). Ecological Restoration, 22, 50–51.

    Google Scholar 

  • Drezner, T. D. (2014). The keystone saguaro (Carnegiea gigantea, Cactaceae): A review of its ecology, associations, reproduction, limits, and demographics. Plant Ecology, 215, 581–595.

    Google Scholar 

  • Drezner, Z. (2005). Compounded genetic algorithms for the quadratic assignment problem. Operations Research Letters, 33, 475–480.

    Google Scholar 

  • Drezner, Z. (2008a). Extensive experiments with hybrid genetic algorithms for the solution of the quadratic assignment problem. Computers & Operations Research, 35, 717–736.

    Google Scholar 

  • Drezner, Z. (2008b). Tabu search and hybrid genetic algorithms for quadratic assignment problems. In W. Jaziri (Ed.) Tabu Search, (pp. 89–108). In-Tech. Available free on: http://books.i-techonline.com.

  • Drezner, Z. (2015). The quadratic assignment problem. In G. Laporte, S. Nickel, & F. S. da Gama (Eds.), Location science (pp. 345–363). Chum, Heidelberg: Springer.

    Google Scholar 

  • Drezner, Z., Brimberg, J., Salhi, S., & Mladenović, N. (2015). New heuristic algorithms for solving the planar \(p\)-median problem. Computers & Operations Research, 62, 296–304.

    Google Scholar 

  • Drezner, Z., Brimberg, J., Salhi, S., & Mladenović, N. (2016). New local searches for solving the multi-source Weber problem. Annals of Operations Research, 246, 181–203.

    Google Scholar 

  • Drezner, Z., & Marcoulides, G. A. (2003). A distance-based selection of parents in genetic algorithms. In M. G. C. Resende & J. P. de Sousa (Eds.), Metaheuristics: Computer decision-making (pp. 257–278). Boston: Kluwer Academic Publishers.

    Google Scholar 

  • Drezner, Z., & Marcoulides, G. A. (2009). On the range of tabu tenure in solving quadratic assignment problems. Recent Advances in computing and management information systems (pp. 157–168). Athens: Athens Institute for Education and Research.

    Google Scholar 

  • Drezner, Z., & Misevičius, A. (2013). Enhancing the performance of hybrid genetic algorithms by differential improvement. Computers & Operations Research, 40, 1038–1046.

    Google Scholar 

  • Drezner, Z., & Salhi, S. (2017). Incorporating neighborhood reduction for the solution of the planar \(p\)-median problem. Annals of Operations Research, 258, 639–654.

    Google Scholar 

  • Edmands, S. (2007). Between a rock and a hard place: Evaluating the relative risks of inbreeding and outbreeding for conservation and management. Molecular Ecology, 16, 463–475.

    Google Scholar 

  • Eiben, A. E., & Smith, J. E. (2003). Introduction to evolutionary computing (Vol. 53). Berlin: Springer.

    Google Scholar 

  • Eilon, S., Watson-Gandy, C. D. T., & Christofides, N. (1971). Distribution management. New York: Hafner.

    Google Scholar 

  • Epelman, M. A., Pollock, S., Netter, B., & Low, B. S. (2005). Anisogamy, expenditure of reproductive effort, and the optimality of having two sexes. Operations Research, 53, 560–567.

    Google Scholar 

  • Fenster, C. B., & Galloway, L. F. (2000). Inbreeding and outbreeding depression in natural populations of Chamaecrista fasciculata (Fabaceae). Conservation Biology, 14, 1406–1412.

    Google Scholar 

  • Freeman, S., Harrington, M., & Sharp, J. C. (2014). Biological science (Second Canadian ed.). Toronto: Pearson.

    Google Scholar 

  • García, S., Labbé, M., & Marín, A. (2011). Solving large \(p\)-median problems with a radius formulation. INFORMS Journal on Computing, 23, 546–556.

    Google Scholar 

  • Goldberg, D. E. (2006). Genetic algorithms. Delhi, India: Pearson Education.

    Google Scholar 

  • Goldberg, D. E., & Deb, K. (1991). A comparative analysis of selection schemes used in genetic algorithms. Foundations of Genetic Algorithms, 1, 69–93.

    Google Scholar 

  • Holland, J. H. (1975). Adaptation in natural and artificial systems. Ann Arbor, MI: University of Michigan Press.

    Google Scholar 

  • Kent, A., Drezner, T. D., & Bello, R. (2018). Climate warming and the arrival of potentially invasive species into boreal forest and tundra in the Hudson Bay Lowlands, Canada. Polar Biology, 41, 2007–2022.

    Google Scholar 

  • Koopmans, T. C., & Beckmann, M. J. (1957). Assignment problems and the location of economic activities. Econometrica, 25, 53–76.

    Google Scholar 

  • Krau, S. (1997). Extensions du problème de Weber. Ph.D. thesis, École Polytechnique de Montréal.

  • Ku, K., & Mak, M. (1998). Empirical analysis of the factors that affect the Baldwin effect. In A. E. Eiben, T. Bck, M. Schoenauer, & H. P. Schwefel (Eds.) Proceedings of 5th international conference on parallel problem solving from nature—PPSN V. Lecture Notes in Computer Science (vol. 1498, pp. 481–490). Springer, Berlin.

  • Li, Y., Pardalos, P. M., & Resende, M. G. C. (1994). A greedy randomized adaptive search procedure for the quadratic assignment problem. In P. M. Pardalos & H. Wolkowicz (Eds.), Quadratic assignment and related problems, DIMACS series in discrete mathematics and theoretical computer science (Vol. 16, pp. 237–261). Providence, Rhode Island: American Mathematical Society.

    Google Scholar 

  • Megiddo, N., & Supowit, K. J. (1984). On the complexity of some common geometric location problems. SIAM Journal on Computing, 13, 182–196.

    Google Scholar 

  • Merrill, R. M., Wallbank, R. W., Bull, V., Salazar, P. C., Mallet, J., Stevens, M., et al. (2012). Disruptive ecological selection on a mating cue. Proceedings of the Royal Society of London B: Biological Sciences, 279(1749), 4907–4913.

    Google Scholar 

  • Michalewicz, Z., & Fogel, D. B. (2013). How to solve it: Modern heuristics. New-York: Springer.

    Google Scholar 

  • Miller, B. L., & Goldberg, D. E. (1995). Genetic algorithms, tournament selection, and the effects of noise. Complex Systems, 9, 193–212.

    Google Scholar 

  • Misevičius, A., & Guogis, E. (2012). Computational study of four genetic algorithm variants for solving the quadratic assignment problem. In T. Skersys, R. Butkienè, & R. Butleris (Eds.) Information and Software Technologies, 18th International Conference, ICIST 2012, Proceedings, Communications in Computer and Information Science (CCIS) (vol. 319, pp. 24–37). Berlin: Springer.

  • Misevičius, A., Rubliauskas, D., & Barkauskas, V. (2009). Some further experiments with the genetic algorithm for the quadratic assignment problem. Information Technology and Control, 38. ISSN 1392–124X.

  • Moreno-Perez, J., Moreno-Vega, J., & Mladenovic, N. (1994). Tabu search and simulated annealing in \(p\)-median problem. In Proceedings of the Canadian operational research society conference, Montreal.

  • Moscato, P. (2002). Memetic algorithms. In P. M. Pardalos & M. G. C. Resende (Eds.), Handbook of applied optimization (pp. 157–167). Oxford: Oxford University Press.

    Google Scholar 

  • Murtagh, B. A., & Niwattisyawong, S. R. (1982). An efficient method for the multi-depot location-allocation problem. Journal of the Operational Research Society, 33, 629–634.

    Google Scholar 

  • Oliveira, C. A. S., Pardalos, P. M., & Resende, M. G. C. (2004). GRASP with path-relinking for the quadratic assignment problem. In C. C. Ribeiro & S. L. Martins (Eds.), Efficient and experimental algorithms (pp. 237–261). Berlin: Springer.

    Google Scholar 

  • Pelikan, M., Tsutsui, S., & Kalapala, R. (2007). Dependency trees, permutations, and quadratic assignment problem. In Genetic and evolutionary computation conference: Proceedings of the 9th annual conference on genetic and evolutionary computation, (vol. 7, pp. 629–629). Also Technical Report, Missouiri Estimation of Distribution Algorithms Laboratory (MEDAL), Report No. 2007003.

  • Reed, T. E., Grøtan, V., Jenouvrier, S., Sæther, B.-E., & Visser, M. E. (2013). Population growth in a wild bird is buffered against phenological mismatch. Science, 340(6131), 488–491.

    Google Scholar 

  • Reinelt, G. (1991). TSLIB a traveling salesman library. ORSA Journal on Computing, 3, 376–384.

    Google Scholar 

  • Rendl, F. (2002). The quadratic assignment problem. In Z. Drezner & H. Hamacher (Eds.), Facility location: Applications and theory. Berlin: Springer.

    Google Scholar 

  • Riechmann, T. (2001). Genetic algorithm learning and evolutionary games. Journal of Economic Dynamics and Control, 25, 1019–1037.

    Google Scholar 

  • Rodriguez, J. M., MacPhee, F. C., Bonham, D. J., Horton, J. D., & Bhavsar, V. C. (2004). Best permutations for the dynamic plant layout problem. In A. R. Dasgupta, S. S. Iyengar, & H. S. Bhatt (Eds.) Efficient and experimental algorithms: Proceedings of the 12th international conference on advances in computing and communications (ADCOM 2004), (pp. 173–178). New Delhi: Allied Publishers Pvt. Ltd.

  • Santos, M., Szathmáry, E., & Fontanari, J. F. (2015). Phenotypic plasticity, the Baldwin effect, and the speeding up of evolution: The computational roots of an illusion. Journal of Theoretical Biology, 371, 127–136.

    Google Scholar 

  • Sivanandam, S. N., & Deepa, S. N. (2008). Introduction to genetic algorithms. Berlin: Springer.

    Google Scholar 

  • Srinivas, M., & Patnaik, L. M. (1994). Adaptive probabilities of crossover and mutation in genetic algorithms. IEEE Transactions on Systems, Man and Cybernetics, 24, 656–667.

    Google Scholar 

  • Taillard, É. (2003). Heuristic methods for large centroid clustering problems. Journal of Heuristics, 9, 51–73.

    Google Scholar 

  • Tenorio, R. C., & Drezner, T. D. (2006). Native and invasive vegetation of karst springs in Wisconsin’s Driftless Area. Hydrobiologia, 568, 499–505.

    Google Scholar 

  • Van Welzen, P. C., Parnell, J. A. N., & Slik, J. W. F. (2011). Wallace’s line and plant distributions: Two or three phytogeographical areas and where to group Java? Biological Journal of the Linnean Society, 103, 531–545.

    Google Scholar 

  • Wagner, C. E., McCune, A., & Lovette, I. J. (2012). Recent speciation between sympatric Tanganyikan cichlid colour morphs. Molecular Ecology, 21, 3283–3292.

    Google Scholar 

  • Wallace, A. R. (1869). The Malay Archipelago: The land of the orang-utan and the bird of paradise; a narrative of travel, with studies of man and nature. North Chelmsford: Courier Corporation.

    Google Scholar 

  • Whitley, D., Gordon, S., & Mathias, K. (1994). Lamarckian evolution, the baldwin effect and function optimization. In Y. Davidor, H. P. Schwefel, & R. Manner (Eds.) Parallel problem solving from nature—PPSN III, international conference on evolutionary computation, the third conference on parallel problem solving from nature, proceedings. Lecture Notes in Computer Science (vol. 866, pp. 6–15). Berlin: Springer.

  • Williamson, M. (1996). Biological invasions. London: Chapman and Hall.

    Google Scholar 

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Authors and Affiliations

  1. Steven G. Mihaylo College of Business and Economics, California State University-Fullerton, Fullerton, CA, 92834, USA

    Zvi Drezner

  2. Department of Geography, York University, N430 Ross, 4700 Keele Street, Toronto, ON, M3J 1P3, Canada

    Taly Dawn Drezner

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  1. Zvi Drezner
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Drezner, Z., Drezner, T.D. Biologically Inspired Parent Selection in Genetic Algorithms. Ann Oper Res 287, 161–183 (2020). https://doi.org/10.1007/s10479-019-03343-7

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