Instance Selection Using Multi-objective CHC Evolutionary Algorithm Seema Rathee, Saroj Ratnoo and Jyoti Ahuja Abstract Data reduction has always been an important field of research to enhance the performance of data mining algorithms. Instance selection, a data reduction technique, relates to selecting a subset of informative and non-redundant examples from data. This paper deals with the problem of instance selection in a multi-objective perspective and, hence, proposes a multi-objective cross-generational elitist selec- tion, heterogeneous recombination, and cataclysmic mutation (CHC) for discovering a set of Pareto-optimal solutions. The suggested MOCHC algorithm integrates the concept of non-dominating sorting with CHC. The algorithm has been employed to eight datasets available from UCI machine learning repository. The MOCHC has been successful in finding a range of multiple optimal solutions instead of yielding a single solution. These solutions provide a user with several choices of reduced datasets. Further, the solutions may be combined into a single instance subset by exploiting the promising characteristics across the potentially good solutions based on some user-defined criteria. Keywords Multi-objective optimization ⋅ CHC algorithm ⋅ Instance selection KNN S. Rathee ( ✉ ) ⋅ S. Ratnoo Guru Jambheshwar University of Science and Technology, Hisar, India e-mail: seema27rathee@gmail.com S. Ratnoo e-mail: ratnoo.saroj@gmail.com J. Ahuja Government Post Graduate College for Women, Rohtak, India e-mail: kwatra.jyoti@gmail.com © Springer Nature Singapore Pte Ltd. 2019 S. Fong et al. (eds.), Information and Communication Technology for Competitive Strategies, Lecture Notes in Networks and Systems 40, https://doi.org/10.1007/978-981-13-0586-3_48 475