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A survey on evolutionary multiobjective feature selection in classification: approaches, applications, and challenges
Maximizing the classification accuracy and minimizing the number of selected features are
two primary objectives in feature selection (FS), which is inherently a multiobjective task …
two primary objectives in feature selection (FS), which is inherently a multiobjective task …
Quality evaluation of solution sets in multiobjective optimisation: A survey
M Li, X Yao - ACM Computing Surveys (CSUR), 2019 - dl.acm.org
Complexity and variety of modern multiobjective optimisation problems result in the
emergence of numerous search techniques, from traditional mathematical programming to …
emergence of numerous search techniques, from traditional mathematical programming to …
An evolutionary multitasking optimization framework for constrained multiobjective optimization problems
When addressing constrained multiobjective optimization problems (CMOPs) via
evolutionary algorithms, various constraints and multiple objectives need to be satisfied and …
evolutionary algorithms, various constraints and multiple objectives need to be satisfied and …
Utilizing the relationship between unconstrained and constrained pareto fronts for constrained multiobjective optimization
Constrained multiobjective optimization problems (CMOPs) involve multiple objectives to be
optimized and various constraints to be satisfied, which challenges the evolutionary …
optimized and various constraints to be satisfied, which challenges the evolutionary …
A kriging-assisted two-archive evolutionary algorithm for expensive many-objective optimization
Only a small number of function evaluations can be afforded in many real-world
multiobjective optimization problems (MOPs) where the function evaluations are …
multiobjective optimization problems (MOPs) where the function evaluations are …
A dual-population-based evolutionary algorithm for constrained multiobjective optimization
The main challenge in constrained multiobjective optimization problems (CMOPs) is to
appropriately balance convergence, diversity and feasibility. Their imbalance can easily …
appropriately balance convergence, diversity and feasibility. Their imbalance can easily …
Hybrid microgrid many-objective sizing optimization with fuzzy decision
B Cao, W Dong, Z Lv, Y Gu, S Singh… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
The economics, reliability, and carbon efficiency of hybrid microgrid systems (HMSs) are
often in conflict; hence, a reasonable design for the sizing of the initial microgrid is important …
often in conflict; hence, a reasonable design for the sizing of the initial microgrid is important …
Balancing objective optimization and constraint satisfaction in constrained evolutionary multiobjective optimization
Both objective optimization and constraint satisfaction are crucial for solving constrained
multiobjective optimization problems, but the existing evolutionary algorithms encounter …
multiobjective optimization problems, but the existing evolutionary algorithms encounter …
Two-archive evolutionary algorithm for constrained multiobjective optimization
When solving constrained multiobjective optimization problems, an important issue is how to
balance convergence, diversity, and feasibility simultaneously. To address this issue, this …
balance convergence, diversity, and feasibility simultaneously. To address this issue, this …
Benchmark problems for large-scale constrained multi-objective optimization with baseline results
The interests in evolutionary constrained multiobjective optimization are rapidly increasing
during the past two decades. However, most related studies are limited to small-scale …
during the past two decades. However, most related studies are limited to small-scale …