[HTML][HTML] Large-scale evolutionary optimization: A review and comparative study
Large-scale global optimization (LSGO) problems have widely appeared in various real-
world applications. However, their inherent complexity, coupled with the curse of …
world applications. However, their inherent complexity, coupled with the curse of …
Learning to accelerate evolutionary search for large-scale multiobjective optimization
Most existing evolutionary search strategies are not so efficient when directly handling the
decision space of large-scale multiobjective optimization problems (LMOPs). To enhance …
decision space of large-scale multiobjective optimization problems (LMOPs). To enhance …
A survey on learnable evolutionary algorithms for scalable multiobjective optimization
Recent decades have witnessed great advancements in multiobjective evolutionary
algorithms (MOEAs) for multiobjective optimization problems (MOPs). However, these …
algorithms (MOEAs) for multiobjective optimization problems (MOPs). However, these …
Neural net-enhanced competitive swarm optimizer for large-scale multiobjective optimization
The competitive swarm optimizer (CSO) classifies swarm particles into loser and winner
particles and then uses the winner particles to efficiently guide the search of the loser …
particles and then uses the winner particles to efficiently guide the search of the loser …
Regularity evolution for multiobjective optimization
Recent years have witnessed the repaid progress in developing and applying multiobjective
evolutionary algorithms (MOEAs). However, as a major component of an MOEA, the …
evolutionary algorithms (MOEAs). However, as a major component of an MOEA, the …
Generating diagnostic and actionable explanations for fair graph neural networks
A plethora of fair graph neural networks (GNNs) have been proposed to promote fairness in
models for high-stake real-life contexts. Meanwhile, explainability is generally proposed to …
models for high-stake real-life contexts. Meanwhile, explainability is generally proposed to …
Generative adversarial networks-based dynamic multi-objective task allocation algorithm for crowdsensing
J Ji, Y Guo, X Yang, R Wang, D Gong - Information Sciences, 2023 - Elsevier
Task allocation of large-scale and widely-distributed mobile users in a crowdsensing system
is a challenging issue, especially on time-varying sensing requirements and available users …
is a challenging issue, especially on time-varying sensing requirements and available users …
[HTML][HTML] The application of evolutionary computation in generative adversarial networks (GANs): a systematic literature survey
Y Wang, Q Zhang, GG Wang, H Cheng - Artificial Intelligence Review, 2024 - Springer
As a subfield of deep learning (DL), generative adversarial networks (GANs) have produced
impressive generative results by applying deep generative models to create synthetic data …
impressive generative results by applying deep generative models to create synthetic data …
A regularity augmented evolutionary algorithm with dual-space search for multiobjective optimization
The well-known regularity property allows the Pareto optimal solutions of a multiobjective
optimization problem (MOP) to be embedded in some latent spaces by the manifold …
optimization problem (MOP) to be embedded in some latent spaces by the manifold …
Learning regularity for evolutionary multiobjective search: A generative model-based approach
The prior domain knowledge, ie, the regularity property of continuous multiobjective
optimization problems (MOPs), could be learned to guide the search for evolutionary …
optimization problems (MOPs), could be learned to guide the search for evolutionary …