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However, There are some shortcomings in the algorithm, such as local optima and low convergence speed, which make its practical effects not ideal. Culture algorithm(CA) is driven by knowledge, which can significantly improve the evolutionary efficiency. Chaos mechanism can make the algorithm have better problem space coverage ability. Therefore, a culture&amp;chaos-inspired CSA(CC-CSA) is proposed in this paper to deal with the problems mentioned before. CC-CSA adopts the double-layer evolutionary framework of CA to extract knowledge and guide the crossover and chaotic mutation operation to complete the evolution process. The implicit knowledge is used to adaptively control the chaotic mutation scale, guide the individuals to jump out of the local optima, and realize the accurate search in the latter evolution cycle to gradually approach the optimal solution. It can be seen from the mathematical model analysis that CC-CSA can converge to the global optimal solution. Compared with the experimental results of the original CSA and its representative, up-to-date improved methods, CC-CSA has the fastest convergence speed and the best detection performances. It is also proved that CC-CSA can solve the problems of local optima and slow convergence speed by using the knowledge guidance of CA\u2019s double-layer framework and good coverage ability of chaos mechanism to the problem space.<\/jats:p>","DOI":"10.3233\/jifs-192188","type":"journal-article","created":{"date-parts":[[2020,6,2]],"date-time":"2020-06-02T13:16:48Z","timestamp":1591103808000},"page":"1289-1301","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["CC-CSA: A culture&amp;chaos-inspired clonal selection algorithm for abnormal detection"],"prefix":"10.1177","volume":"39","author":[{"given":"Liang","family":"Xi","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruidong","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fengbin","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuezhongyi","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2020,5,30]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10472-014-9441-7"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2015.04.026"},{"issue":"3","key":"e_1_3_2_4_2","first-page":"239","article-title":"Learning and optimization using the clonal selection principle[J]","volume":"6","author":"de Castro L.N.","year":"2002","unstructured":"de CastroL.N. and Von ZubenF.J., Learning and optimization using the clonal selection principle[J], IEEE Trans on Evolutionary Computation Special Issue on Artificial Immune Systems 6(3) (2002), 239\u2013251.","journal-title":"IEEE Trans on Evolutionary Computation Special Issue on Artificial Immune Systems"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1177\/1687814017721857"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-015-3117-0"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2017.2657797"},{"key":"e_1_3_2_8_2","doi-asserted-by":"crossref","unstructured":"ZhangW.W. 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