{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,24]],"date-time":"2025-06-24T04:04:30Z","timestamp":1750737870082,"version":"3.41.0"},"publisher-location":"Singapore","reference-count":38,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819665846","type":"print"},{"value":"9789819665853","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-981-96-6585-3_8","type":"book-chapter","created":{"date-parts":[[2025,6,23]],"date-time":"2025-06-23T14:41:20Z","timestamp":1750689680000},"page":"104-118","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Novel Elitism-Based Genetic Algorithm with Gradient-Based Local Search for Seeking Local Nash Equilibrium in Non-Cooperative Game"],"prefix":"10.1007","author":[{"given":"Bo-Ying","family":"Lai","sequence":"first","affiliation":[]},{"given":"Chun-Hua","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Xin-Xin","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Yi","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"Wenwu","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Jun","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Zhi-Hui","family":"Zhan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,6,24]]},"reference":[{"key":"8_CR1","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1073\/pnas.36.1.48","volume":"36","author":"JF Nash","year":"1950","unstructured":"Nash, J.F.: Equilibrium points in n -person games. Proc. Natl. Acad. Sci. 36, 48\u201349 (1950)","journal-title":"Proc. Natl. Acad. Sci."},{"key":"8_CR2","doi-asserted-by":"publisher","first-page":"286","DOI":"10.2307\/1969529","volume":"54","author":"J Nash","year":"1951","unstructured":"Nash, J.: Non-cooperative games. Ann. Math. 54, 286 (1951)","journal-title":"Ann. Math."},{"key":"8_CR3","doi-asserted-by":"publisher","first-page":"1767","DOI":"10.1037\/xge0000531","volume":"148","author":"CKW De Dreu","year":"2019","unstructured":"De Dreu, C.K.W., Giacomantonio, M., Giffin, M.R., Vecchiato, G.: Psychological constraints on aggressive predation in economic contests. J. Exp. Psychol. Gen. 148, 1767\u20131781 (2019)","journal-title":"J. Exp. Psychol. Gen."},{"key":"8_CR4","doi-asserted-by":"publisher","first-page":"1067","DOI":"10.1257\/jel.37.3.1067","volume":"37","author":"RB Myerson","year":"1999","unstructured":"Myerson, R.B.: Nash equilibrium and the history of economic theory. J. Econ. Lit. 37, 1067\u20131082 (1999)","journal-title":"J. Econ. Lit."},{"key":"8_CR5","doi-asserted-by":"crossref","unstructured":"Alkheliwi, T., Jim, C., Lateef, K., Penn, S., Salem, A.: Applying game theory rules to enhance decision support systems in credit and financial applications. In: Computer Games: AI, Animation, Mobile, Multimedia, Educational and Serious Games, Louisville, KY, pp. 1\u201310. IEEE (2014)","DOI":"10.1109\/CGames.2014.6934138"},{"key":"8_CR6","doi-asserted-by":"crossref","unstructured":"Madani, K., Hooshyar, M., Khatami, S., Alaeipour, A., Moeini, A.: Nash-reinforcement learning (N-RL) for developing coordination strategies in non-transferable utility games. In: IEEE International Conference on Systems, Man, and Cybernetics, San Diego, CA, USA, pp. 2705\u20132710. IEEE (2014)","DOI":"10.1109\/SMC.2014.6974336"},{"key":"8_CR7","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1561\/2200000050","volume":"8","author":"S Bubeck","year":"2015","unstructured":"Bubeck, S.: Convex optimization: algorithms and complexity. Found. Trends Mach. Learn. 8, 231\u2013357 (2015)","journal-title":"Found. Trends Mach. Learn."},{"key":"8_CR8","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1109\/TEVC.2004.832862","volume":"8","author":"YS Son","year":"2004","unstructured":"Son, Y.S., Baldick, R.: Hybrid coevolutionary programming for Nash equilibrium search in games with local optima. IEEE Trans. Evol. Comput. 8, 305\u2013315 (2004)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"8_CR9","unstructured":"Daskalakis, C., Panageas, I.: The limit points of (Optimistic) gradient descent in min-max optimization. In: Advances in Neural Information Processing Systems (2018)"},{"key":"8_CR10","unstructured":"Adolphs, L., Daneshmand, H., Lucchi, A., Hofmann, T.: Local saddle point optimization: a curvature exploitation approach. In: International Conference on Artificial Intelligence and Statistics, vol. 89, pp. 486\u2013495 (2019)"},{"key":"8_CR11","unstructured":"Hensel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: GANs trained by a two time-scale update rule converge to a local Nash equilibrium. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"8_CR12","doi-asserted-by":"publisher","first-page":"144361","DOI":"10.1109\/ACCESS.2021.3120128","volume":"9","author":"R Adnan","year":"2021","unstructured":"Adnan, R., Saputra, M.A., Fadlil, J., Ezerman, M.F., Iqbal, M., Basaruddin, T.: Learning GANs in simultaneous game using sinkhorn with positive features. IEEE Access. 9, 144361\u2013144374 (2021)","journal-title":"IEEE Access."},{"key":"8_CR13","doi-asserted-by":"publisher","first-page":"847","DOI":"10.1109\/59.962436","volume":"16","author":"JB Park","year":"2001","unstructured":"Park, J.B., Kim, B.H., Kim, J.H., Jung, M.H., Park, J.K.: A continuous strategy game for power transactions analysis in competitive electricity markets. IEEE Trans. Power Syst. 16, 847\u2013855 (2001)","journal-title":"IEEE Trans. Power Syst."},{"key":"8_CR14","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/S0378-7796(03)00042-7","volume":"67","author":"Y Song","year":"2003","unstructured":"Song, Y., Ni, Y., Wen, F., Hou, Z., Wu, F.F.: Conjectural variation based bidding strategy in spot markets: fundamentals and comparison with classical game theoretical bidding strategies. Electric Power Syst. Res. 67, 45\u201351 (2003)","journal-title":"Electric Power Syst. Res."},{"key":"8_CR15","doi-asserted-by":"crossref","unstructured":"Weber, J.D., Overbye, T.J.: A two-level optimization problem for analysis of market bidding strategies. In: IEEE Power Engineering Society Summer Meeting. Conference Proceedings, Edmonton, Alta, Canada, pp. 682\u2013687. IEEE (1999)","DOI":"10.1109\/PESS.1999.787399"},{"key":"8_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.epsr.2020.106806","volume":"189","author":"B Fanzeres","year":"2020","unstructured":"Fanzeres, B., Street, A., Pozo, D.: A column-and-constraint generation algorithm to find Nash equilibrium in pool-based electricity Markets. Electric Power Syst. Res. 189, 106806 (2020)","journal-title":"Electric Power Syst. Res."},{"key":"8_CR17","doi-asserted-by":"publisher","first-page":"830","DOI":"10.1109\/TPWRS.2002.807067","volume":"18","author":"KH Lee","year":"2003","unstructured":"Lee, K.H., Baldick, R.: Tuning of discretization in bimatrix game approach to power system market analysis. IEEE Trans. Power Syst. 18, 830\u2013836 (2003)","journal-title":"IEEE Trans. Power Syst."},{"key":"8_CR18","doi-asserted-by":"crossref","unstructured":"Xu, X.-X., Jiang, Y., Zhang, L., Liu, X., Ding, X.-Q., Zhan, Z.-H.: Evolutionary computation for berth allocation problems: a survey. Lecture Notes in Computer Science, pp. 40\u201351 (2023)","DOI":"10.1007\/978-981-99-8067-3_4"},{"key":"8_CR19","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1109\/MCI.2022.3155330","volume":"17","author":"ZG Chen","year":"2022","unstructured":"Chen, Z.G., Zhan, Z.-H., Kwong, S., Zhang, J.: Evolutionary computation for intelligent transportation in smart cities: a survey. IEEE Comput. Intell. Mag. 17, 83\u2013102 (2022)","journal-title":"IEEE Comput. Intell. Mag."},{"key":"8_CR20","doi-asserted-by":"publisher","first-page":"558","DOI":"10.1109\/TCYB.2023.3273625","volume":"54","author":"Y Jiang","year":"2024","unstructured":"Jiang, Y., Zhan, Z.-H., Tan, K.C., Zhang, J.: Block-level knowledge transfer for evolutionary multitask optimization. IEEE Trans. Cybernetics. 54, 558\u2013571 (2024)","journal-title":"IEEE Trans. Cybernetics."},{"key":"8_CR21","doi-asserted-by":"publisher","first-page":"1794","DOI":"10.1109\/TEVC.2022.3232776","volume":"27","author":"Z-H Zhan","year":"2023","unstructured":"Zhan, Z.-H., Li, J.Y., Kwong, S., Zhang, J.: Learning-aid evolution for optimization. IEEE Trans. Evol. Comput. 27, 1794\u20131808 (2023)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"8_CR22","doi-asserted-by":"crossref","unstructured":"Jiang, Y., Xu, X.-X., Zheng, M.-Y., Zhan, Z.-H.: Evolutionary computation for unmanned aerial vehicle path planning: a survey. Artif. Intell. Rev. 57 (2024)","DOI":"10.1007\/s10462-024-10913-0"},{"key":"8_CR23","doi-asserted-by":"publisher","first-page":"1460","DOI":"10.1109\/TCYB.2021.3102642","volume":"53","author":"SC Liu","year":"2023","unstructured":"Liu, S.C., Chen, Z.G., Zhan, Z.-H., Jeon, S.W., Kwong, S., Zhang, J.: Many-objective job shop scheduling: a multiple populations for multiple objectives-based genetic algorithm approach. IEEE Trans. Cybernet. 53, 1460\u20131474 (2023)","journal-title":"IEEE Trans. Cybernet."},{"key":"8_CR24","doi-asserted-by":"publisher","first-page":"478","DOI":"10.1109\/TEVC.2021.3051608","volume":"25","author":"SH Wu","year":"2021","unstructured":"Wu, S.H., Zhan, Z.-H., Zhang, J.: SAFE: Scale-adaptive fitness evaluation method for expensive optimization problems. IEEE Trans. Evol. Comput. 25, 478\u2013491 (2021)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"8_CR25","doi-asserted-by":"publisher","first-page":"587","DOI":"10.1109\/TEVC.2018.2875430","volume":"23","author":"XF Liu","year":"2019","unstructured":"Liu, X.F., Zhan, Z.-H., Gao, Y., Zhang, J., Kwong, S., Zhang, J.: Coevolutionary particle swarm optimization with bottleneck objective learning strategy for many-objective optimization. IEEE Trans. Evol. Comput. 23, 587\u2013602 (2019)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"8_CR26","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2023.3340678","author":"QT Yang","year":"2023","unstructured":"Yang, Q.T., Zhan, Z.-H., Liu, X.F., Li, J.Y., Zhang, J.: Grid classification-based surrogate-assisted particle swarm optimization for expensive multiobjective optimization. IEEE Trans. Evol. Comput. (2023). https:\/\/doi.org\/10.1109\/TEVC.2023.3340678","journal-title":"IEEE Trans. Evol. Comput."},{"key":"8_CR27","doi-asserted-by":"publisher","first-page":"512","DOI":"10.1109\/TEVC.2021.3097339","volume":"26","author":"X Zhang","year":"2022","unstructured":"Zhang, X., Zhan, Z.-H., Fang, W., Qian, P., Zhang, J.: Multipopulation ant colony system with knowledge-based local searches for multiobjective supply chain configuration. IEEE Trans. Evol. Comput. 26, 512\u2013526 (2022)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"8_CR28","first-page":"1209","volume":"135","author":"C Wang","year":"2023","unstructured":"Wang, C., et al.: A scheme library-based ant colony optimization with 2-opt local search for dynamic traveling salesman problem. Comput. Model. Eng. Sci. 135, 1209\u20131228 (2023)","journal-title":"Comput. Model. Eng. Sci."},{"key":"8_CR29","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2024.3365814","author":"J Hong","year":"2024","unstructured":"Hong, J., Zhan, Z.-H., He, L., Xu, Z., Zhang, J.: Protein structure prediction using a new optimization-based evolutionary and explainable artificial intelligence approach. IEEE Trans. Evol. Comput. (2024). https:\/\/doi.org\/10.1109\/TEVC.2024.3365814","journal-title":"IEEE Trans. Evol. Comput."},{"key":"8_CR30","doi-asserted-by":"crossref","unstructured":"Jie, S.-J., Jiang, Y., Xu, X.-X., Kwong, S., Zhan, Z.-H., Zhang, J.: Optimal peaks detected-based differential evolution for multimodal optimization problems. In: IEEE International Conference on Systems, Man, and Cybernetics, pp. 1176\u20131181 (2023)","DOI":"10.1109\/SMC53992.2023.10394311"},{"key":"8_CR31","doi-asserted-by":"publisher","first-page":"2544","DOI":"10.1109\/TCYB.2021.3125362","volume":"53","author":"Y Jiang","year":"2023","unstructured":"Jiang, Y., Zhan, Z.-H., Tan, K.C., Zhang, J.: Optimizing niche center for multimodal optimization problems. IEEE Trans. Cybernet. 53, 2544\u20132557 (2023)","journal-title":"IEEE Trans. Cybernet."},{"key":"8_CR32","doi-asserted-by":"publisher","first-page":"21675","DOI":"10.1109\/TITS.2022.3172719","volume":"23","author":"R Wang","year":"2022","unstructured":"Wang, R., et al.: An adaptive ant colony system based on variable range receding horizon control for berth allocation problem. IEEE Trans. Intell. Transp. Syst. 23, 21675\u201321686 (2022)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"8_CR33","doi-asserted-by":"publisher","first-page":"1514","DOI":"10.1109\/TEVC.2022.3210783","volume":"27","author":"Y Jiang","year":"2023","unstructured":"Jiang, Y., Zhan, Z.-H., Tan, K.C., Zhang, J.: A bi-objective knowledge transfer framework for evolutionary many-task optimization. IEEE Trans. Evol. Comput. 27, 1514\u20131528 (2023)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"8_CR34","doi-asserted-by":"publisher","first-page":"680","DOI":"10.1287\/opre.2016.1501","volume":"64","author":"J Koshal","year":"2016","unstructured":"Koshal, J., Nedi\u0107, A., Shanbhag, U.V.: Distributed algorithms for aggregative games on graphs. Oper. Res. 64, 680\u2013704 (2016)","journal-title":"Oper. Res."},{"key":"8_CR35","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2023.3278132","author":"Y Jiang","year":"2024","unstructured":"Jiang, Y., Zhan, Z.-H., Tan, K.C., Zhang, J.: Knowledge learning for evolutionary computation. IEEE Trans. Evol. Comput. (2024). https:\/\/doi.org\/10.1109\/TEVC.2023.3278132","journal-title":"IEEE Trans. Evol. Comput."},{"key":"8_CR36","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2024.3355781","author":"Y Jiang","year":"2024","unstructured":"Jiang, Y., Zhan, Z.-H., Tan, K.C., Kwong, S., Zhang, J.: Knowledge structure preserving-based evolutionary many-task optimization. IEEE Trans. Evol. Comput. (2024). https:\/\/doi.org\/10.1109\/TEVC.2024.3355781","journal-title":"IEEE Trans. Evol. Comput."},{"key":"8_CR37","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1109\/72.265964","volume":"5","author":"G Rudolph","year":"1994","unstructured":"Rudolph, G.: Convergence analysis of canonical genetic algorithms. IEEE Trans. Neural Networks 5, 96\u2013101 (1994)","journal-title":"IEEE Trans. Neural Networks"},{"key":"8_CR38","unstructured":"De Jong, D.A.: An analysis of the behavior of a class of genetic adaptive systems. Doctoral dissertation (1975)"}],"container-title":["Lecture Notes in Computer Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-6585-3_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,23]],"date-time":"2025-06-23T14:41:23Z","timestamp":1750689683000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-6585-3_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819665846","9789819665853"],"references-count":38,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-6585-3_8","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"24 June 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Auckland","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"New Zealand","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 December 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iconip2024.org","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}