A Survey of Multiobjective Evolutionary Algorithms for Data Mining: Part I

By Anirban Mukhopadhyay, Ujjwal Maulik, Sanghamitra Bandyopadhyay, Carlos Artemio Coello Coello. IEEE Transactions on Evolutionary Computation, 2014

419 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/1b0f4514ef7958db27c3eabe371b3a218f575746

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Citations in the corpus

Genetic algorithms: theory, genetic operators, solutions, and applications (2023, neutral): “However, multiple objectives must be optimized simultaneously to solve real-world problems [19].”
Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches (2015, methodology): “It is fortunate that the EC paradigm has proved to be one of the most signiﬁcant and promising tools for multiobjective optimization [Zhan et al. 2013; Mukhopadhyay et al. 2014].”
A multi-disciplinary review of knowledge acquisition methods: From human to autonomous eliciting agents (2016, methodology): “KD data preprocessing data mining knowledge interpretation [29], [152], [148]”
Data Mining for the Internet of Things: Literature Review and Challenges (2015, background): “The objective of any data mining process is to build an efficient predictive or descriptive model of a large amount of data that not only best fits or explains it, but is also able to generalize to new data [4].”
Big IoT Data Analytics: Architecture, Opportunities, and Open Research Challenges (2017, methodology): “Data mining methods provide efﬁcient and best-ﬁtting predictive or descriptive solutions for big data that can also be generalized for new data [45].”
PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization [Educational Forum] (2017, background): “Multi-objective optimization problems (MOPs) widely exist in computer science such as data mining [1], pattern recognition [2], image processing [3] and neural network [4], as well as many other application ﬁelds [5]– [8].”
Data-Driven Surrogate-Assisted Multiobjective Evolutionary Optimization of a Trauma System (2016, background): “Lack of training data will it impossible to build accurate surrogates when the dimension of the decision space is high [5], while large amount of training may result in increasing computational cost [52], [53].”
Many-Objective Evolutionary Algorithms (2015, background): “That is what we have done in our survey, including the real-world problems listed in the appendix.”