When solving constrained multiobjective optimization problems, an important issue is how to
balance convergence, diversity, and feasibility simultaneously. To address this issue, this …

In contrast to the traditional single-tasking evolutionary algorithms, evolutionary multitasking
(EMT) travels in the search space of multiple optimization tasks simultaneously. Through …

Existing studies on dynamic multiobjective optimization (DMO) focus on problems with time-
dependent objective functions, while the ones with a changing number of objectives have …

Multitasking optimization can achieve better performance than traditional single-tasking
optimization by leveraging knowledge transfer between tasks. However, the current …

The decomposition-based evolutionary multiobjective optimization (EMO) algorithm has
become an increasingly popular choice for a posteriori multiobjective optimization. However …

Measuring the performance of an algorithm for solving multiobjective optimization problem
has always been challenging simply due to two conflicting goals, ie, convergence and …

The decomposition-based evolutionary algorithm has become an increasingly popular
choice for posterior multiobjective optimization. Facing the challenges of an increasing …

The ultimate goal of multiobjective optimization is to help a decision maker (DM) identify
solution (s) of interest (SOI) achieving satisfactory tradeoffs among multiple conflicting …

Security is unarguably the most serious concern for Web applications, to which SQL
injection (SQLi) attack is one of the most devastating attacks. Automatically testing SQLi …

Cross-Project Defect Prediction (CPDP), which borrows data from similar projects by
combining a transfer learner with a classifier, have emerged as a promising way to predict …