{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T17:55:02Z","timestamp":1754157302615,"version":"3.41.2"},"reference-count":41,"publisher":"Emerald","issue":"2","license":[{"start":{"date-parts":[[2011,6,7]],"date-time":"2011-06-07T00:00:00Z","timestamp":1307404800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2011,6,7]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-heading\">Purpose<\/jats:title><jats:p>A novel sexual adaptive genetic algorithm (AGA) based on Baldwin effect for global optimization is proposed to overcome the shortcomings of traditional GAs, such as premature convergence, stochastic roaming, and poor capabilities in local exploring. This paper seeks to discuss the issues.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Design\/methodology\/approach<\/jats:title><jats:p>The proposed algorithm simulates sexual reproduction and adopts an effective gender determination method to divide the population into two subgroups of different genders. Based on the competition, cooperation, and innate differences between two gender subgroups, the proposed algorithm adjusts adaptively sexual genetic operators. Furthermore, inspired by the acquired reinforcement learning theory based on Baldwin effect, the proposed algorithm guides individuals to forward or reverse learning and enables the transmission of fitness information between parents and offspring to adapt individuals' acquired fitness.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Findings<\/jats:title><jats:p>Global convergence of the proposed algorithm is proved in detail. Numerical simulations are conducted for a set of benchmark functions with different dimensional decision variables. The performance of the proposed algorithm is compared with that of the other evolutionary algorithms published recently. The results indicate that the proposed algorithm can find optimal or closer\u2010to\u2010optimal solutions, and is more competitive than the compared algorithms.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Originality\/value<\/jats:title><jats:p>The proposed algorithm introduces, integrates and simulates correctly and adequately, for the first time, the mechanisms of sexual reproduction, Baldwin effect and adaptation to GAs by referring to the latest research results of modern biology and evolution theory.<\/jats:p><\/jats:sec>","DOI":"10.1108\/17563781111136702","type":"journal-article","created":{"date-parts":[[2011,6,18]],"date-time":"2011-06-18T07:17:43Z","timestamp":1308381463000},"page":"207-227","source":"Crossref","is-referenced-by-count":2,"title":["A novel sexual adaptive genetic algorithm based on Baldwin effect for global optimization"],"prefix":"10.1108","volume":"4","author":[{"given":"Mingming","family":"Zhang","sequence":"first","affiliation":[]}],"member":"140","reference":[{"key":"key2022020420122867000_b22","unstructured":"Ackley, D.H. and Littman, M. (1992), \u201cInteractions between learning and evolution\u201d, in Langton, C.G., Taylor, C., Farmer, J.D. and Rasmussen, S. (Eds), Artificial Life II, Addison\u2010Wesley, Redwood City, CA, pp. 487\u2010509."},{"key":"key2022020420122867000_b36","unstructured":"Allen, A.O. (1990), Probability Statistics and Queuing Theory with Computer Science Applications, 2nd ed., Academic Press, Boston, MA."},{"key":"key2022020420122867000_b20","doi-asserted-by":"crossref","unstructured":"Anderson, R.W. (1995), \u201cLearning and evolution: a quantitative genetics approach\u201d, J. Theor. Biol., Vol. 175, pp. 89\u2010101.","DOI":"10.1006\/jtbi.1995.0123"},{"key":"key2022020420122867000_b10","unstructured":"Andersson, M. (1994), Sexual Selection, Princeton University Press, Princeton, NJ."},{"key":"key2022020420122867000_b34","doi-asserted-by":"crossref","unstructured":"B\u00e4ck, T. (1996), Evolutionary Algorithms in Theory and Practice, Oxford University Press, New York, NY.","DOI":"10.1093\/oso\/9780195099713.003.0007"},{"key":"key2022020420122867000_b11","doi-asserted-by":"crossref","unstructured":"Baldwin, J.M. (1896), \u201cA new factor in evolution\u201d, American Naturalist, Vol. 30, pp. 441\u201051.","DOI":"10.1086\/276408"},{"key":"key2022020420122867000_b23","doi-asserted-by":"crossref","unstructured":"Boers, E.J.W., Borst, M.V. and Sprinkhuizen\u2010Kuyper, I.G. (1995), \u201cEvolving artificial neural networks using the \u2018Baldwin effect\u2019\u201d, Proceedings of International Conference on Artificial Neural Nets and Genetic Algorithms, Al\u00e9s, France, pp. 333\u20106.","DOI":"10.1007\/978-3-7091-7535-4_87"},{"key":"key2022020420122867000_b27","doi-asserted-by":"crossref","unstructured":"Bull, L. (1999), \u201cOn the Baldwin effect\u201d, Artificial Life, Vol. 5 No. 3, pp. 241\u20106.","DOI":"10.1162\/106454699568764"},{"key":"key2022020420122867000_b41","unstructured":"de Jong, K.A. (1975), \u201cAnalysis of the behavior of a class of genetic adaptive systems\u201d, Dissertation, University of Michigan, Ann Arbor, MI."},{"key":"key2022020420122867000_b33","unstructured":"Dennis, C. and Gallagher, R. (2001), The Human Genome, Nature Publishing Group, London."},{"key":"key2022020420122867000_b19","unstructured":"Depew, D. (2000), \u201cThe Baldwin effect: an archaeology\u201d, Cybernetics and Human Knowing, Vol. 7 No. 1, pp. 7\u201020."},{"key":"key2022020420122867000_b18","unstructured":"Gadagkar, R. (1997), Survival Strategies Cooperation and Conflict in Animal Societies, Harvard University Press, Cambridge, MA."},{"key":"key2022020420122867000_b8","doi-asserted-by":"crossref","unstructured":"Goh, K.S., Lim, A. and Rodrigues, B. (2003), \u201cSexual selection for genetic algorithms\u201d, Artificial Intelligence Reviews, Vol. 19 No. 2, pp. 123\u201052.","DOI":"10.1023\/A:1022692631328"},{"key":"key2022020420122867000_b24","doi-asserted-by":"crossref","unstructured":"Gruau, F. and Whitley, D. (1993), \u201cAdding learning to the cellular development of neural networks: evolution and the Baldwin effect\u201d, Evol. Comput., Vol. 1 No. 3, pp. 213\u201033.","DOI":"10.1162\/evco.1993.1.3.213"},{"key":"key2022020420122867000_b2","doi-asserted-by":"crossref","unstructured":"Hart, W.E., Kammeyer, T.E. and Belew, R.K. (1995), \u201cThe role of development in genetic algorithms\u201d, in Whitley, L.D. and Vose, M.D. (Eds), Foundations of Genetic Algorithms, Vol. 3, Morgan Kaufmann, San Mateo, CA, pp. 315\u201032.","DOI":"10.1016\/B978-1-55860-356-1.50019-4"},{"key":"key2022020420122867000_b6","doi-asserted-by":"crossref","unstructured":"Herrera, F. and Lozano, M. (2001), \u201cAdaptive genetic operators based on coevolution with fuzzy behaviors\u201d, IEEE Trans. Evol. Comput., Vol. 5 No. 2, pp. 149\u201065.","DOI":"10.1109\/4235.918435"},{"key":"key2022020420122867000_b21","unstructured":"Hinton, G.E. and Nowlan, S.J. (1987), \u201cHow learning can guide evolution\u201d, Complex Systems, Vol. 1, pp. 495\u2010502."},{"key":"key2022020420122867000_b1","unstructured":"Holland, J.H. (1975), Adaptation in Natural and Artificial Systems, The University of Michigan Press, Ann Arbor, MI."},{"key":"key2022020420122867000_b7","doi-asserted-by":"crossref","unstructured":"Hu, X.B., Paol, E.D. and Wu, S.F. (2008), \u201cA comprehensive fuzz\u2010rule\u2010based self\u2010adaptive genetic algorithm\u201d, Int. J. Intell. Comput. Cybernet., Vol. 1 No. 1, pp. 94\u2010109.","DOI":"10.1108\/17563780810857149"},{"key":"key2022020420122867000_b37","doi-asserted-by":"crossref","unstructured":"Kennedy, J. and Eberhart, R.C. (1997), \u201cA discrete binary version of the particle swarm algorithm\u201d, Proceedings of IEEE International Conference on Systems, Man and Cybernetics, Orlando, FL, USA, pp. 4104\u20108.","DOI":"10.1109\/ICSMC.1997.637339"},{"key":"key2022020420122867000_b31","doi-asserted-by":"crossref","unstructured":"Ku, K.W.C. (2006), \u201cEnhance the Baldwin effect by strengthening the correlation between genetic operators and learning methods\u201d, Proceedings of 2006 IEEE International Conference on Evolutionary Computing, Vancouver, Canada, pp. 3302\u20108.","DOI":"10.1109\/CEC.2006.1688729"},{"key":"key2022020420122867000_b25","unstructured":"Ku, K.W.C. and Mak, M.W. (1997), \u201cExploring the effects of Lamarckian and Baldwinian learning in evolving recurrent neural networks\u201d, Proceedings of 1997 IEEE International Conference on Evolutionary Computing, Indianapolis, IN, USA, pp. 617\u201021."},{"key":"key2022020420122867000_b40","doi-asserted-by":"crossref","unstructured":"Lee, S., Soak, S., Oh, S., Pedrycz, O. and Jeon, M. (2008), \u201cModified binary particle swarm optimization\u201d, Progress in Natural Science, Vol. 18 No. 9, pp. 1161\u20106.","DOI":"10.1016\/j.pnsc.2008.03.018"},{"key":"key2022020420122867000_b13","unstructured":"Lis, J. and Eiben, A.E. (1997), \u201cA multi\u2010sexual genetic algorithm for multiobjective optimization\u201d, Proceedings of 1997 IEEE International Conference on Evolutionary Computing, Indianapolis, IN, USA, pp. 59\u201064."},{"key":"key2022020420122867000_b32","doi-asserted-by":"crossref","unstructured":"Nolfi, S., Elman, J.L. and Parisi, D. 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(2006), \u201cGenetic algorithm with species and sexual selection\u201d, Proceedings of 2006 IEEE Conference on Cybernetics and Intelligent System, Bangkok, Thailand, pp. 1\u20108.","DOI":"10.1109\/ICCIS.2006.252229"},{"key":"key2022020420122867000_b14","doi-asserted-by":"crossref","unstructured":"Rejeb, J. and AbuElhaija, M. (2000), \u201cNew gender genetic algorithm for solving graph partitioning problems\u201d, Proceedings of 43rd IEEE Midwest Symposium on Circuits and Systems, Lansing, MI, USA, pp. 444\u20106.","DOI":"10.1109\/MWSCAS.2000.951679"},{"key":"key2022020420122867000_b16","unstructured":"Sanchez\u2010Velazco, J. and Bullinaria, J.A. 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(2004), \u201cBaldwin effect based self\u2010adaptive generalized genetic algorithm and its application\u201d, Proceedings of 8th International Conference on Control, Automation, Robotics and Vision, Kunming, China, pp. 242\u20107."},{"key":"key2022020420122867000_b28","doi-asserted-by":"crossref","unstructured":"Suzuki, R. and Arita, T. (2004), \u201cInteractions between learning and evolution: outstanding strategy generated by the Baldwin effect\u201d, BioSystems, Vol. 77 Nos 1\/3, pp. 57\u201071.","DOI":"10.1016\/j.biosystems.2004.04.002"},{"key":"key2022020420122867000_b29","doi-asserted-by":"crossref","unstructured":"Suzuki, R. and Arita, T. (2007), \u201cThe dynamic changes in roles of learning through the Baldwin effect\u201d, Artificial Life, Vol. 13 No. 1, pp. 31\u201043.","DOI":"10.1162\/artl.2007.13.1.31"},{"key":"key2022020420122867000_b26","unstructured":"Tcholakian, A.B., Martins, A., Pacheco, R.C.S. and Barcia, R.M. 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