Does preference always help? A holistic study on preference-based evolutionary multiobjective optimization using reference points
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 …
solution (s) of interest (SOI) achieving satisfactory tradeoffs among multiple conflicting …
DeepSQLi: Deep semantic learning for testing SQL injection
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 …
injection (SQLi) attack is one of the most devastating attacks. Automatically testing SQLi …
BiLO-CPDP: Bi-level programming for automated model discovery in cross-project defect prediction
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 …
combining a transfer learner with a classifier, have emerged as a promising way to predict …
Batched data-driven evolutionary multiobjective optimization based on manifold interpolation
Multiobjective optimization problems are ubiquitous in real-world science, engineering, and
design optimization problems. It is not uncommon that the objective functions are as a black …
design optimization problems. It is not uncommon that the objective functions are as a black …
A data-driven evolutionary transfer optimization for expensive problems in dynamic environments
Many real-world problems are computationally costly and the objective functions evolve over
time. Data-driven, aka surrogate-assisted, evolutionary optimization has been recognized as …
time. Data-driven, aka surrogate-assisted, evolutionary optimization has been recognized as …
Empirical studies on the role of the decision maker in interactive evolutionary multi-objective optimization
G Lai, M Liao, K Li - 2021 IEEE Congress on Evolutionary …, 2021 - ieeexplore.ieee.org
The interactive evolutionary multi-objective optimization (IEMO) algorithms aim to learn and
utilize the preference information from the decision maker (DM) during the optimization …
utilize the preference information from the decision maker (DM) during the optimization …
Interactive evolutionary multiobjective optimization via learning to rank
K Li, G Lai, X Yao - IEEE Transactions on Evolutionary …, 2023 - ieeexplore.ieee.org
In practical multicriterion decision making, it is cumbersome if a decision maker (DM) is
asked to choose among a set of tradeoff alternatives covering the whole Pareto-optimal …
asked to choose among a set of tradeoff alternatives covering the whole Pareto-optimal …
Decomposition multi-objective evolutionary optimization: From state-of-the-art to future opportunities
K Li - arxiv preprint arxiv:2108.09588, 2021 - arxiv.org
Decomposition has been the mainstream approach in the classic mathematical
programming for multi-objective optimization and multi-criterion decision-making. However …
programming for multi-objective optimization and multi-criterion decision-making. However …
An improved two-archive evolutionary algorithm for constrained multi-objective optimization
X Shan, K Li - International Conference on Evolutionary Multi …, 2021 - Springer
Constrained multi-objective optimization problems (CMOPs) are ubiquitous in real-world
engineering optimization scenarios. A key issue in constrained multi-objective optimization …
engineering optimization scenarios. A key issue in constrained multi-objective optimization …
Do we really need to use constraint violation in constrained evolutionary multi-objective optimization?
Constraint violation has been a building block to design evolutionary multi-objective
optimization algorithms for solving constrained multi-objective optimization problems …
optimization algorithms for solving constrained multi-objective optimization problems …