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The six individual classifiers have an obvious effect on a particular type of ground, but the overall performance is poor. Therefore, the paper proposes a \u201cNovel Hybrid Evolutionary Learning\u201d method (NHEL) which combines every single classifier by means of weighted voting and adopts an improved genetic algorithm (GA) to obtain the optimal weight. According to the fitness function and evolution times, this paper designs the adaptively changing crossover and mutation rate and applies the conjugate gradient (CG) to enhance GA. By making full use of the global search capabilities of GA and the fast local search ability of CG, the convergence speed is accelerated and the search precision is upgraded. The experimental results show that the performance of the proposed model is significantly better than individual machine learning and ensemble classifiers.<\/jats:p>","DOI":"10.3233\/jifs-202940","type":"journal-article","created":{"date-parts":[[2021,3,23]],"date-time":"2021-03-23T13:47:47Z","timestamp":1616507267000},"page":"10129-10143","source":"Crossref","is-referenced-by-count":1,"title":["A hybrid evolutionary learning classification for robot ground pattern recognition"],"prefix":"10.1177","volume":"40","author":[{"given":"Jiankai","family":"Zuo","sequence":"first","affiliation":[{"name":"Department of Computer Science and Technology, and Key Laboratory of Embedded System and Service Computing Ministry of Education, Tongji University, Shanghai"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yaying","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, and Key Laboratory of Embedded System and Service 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