Enhancing Multi-objective Evolutionary Neural Architecture Search with Surrogate Models and Potential Point-Guided Local Searches. - Quan Minh Phan; Ngoc Hoang Luong (IEA/AIE (1), 2021)

TF-MOPNAS: Training-free Multi-objective Pruning-Based Neural Architecture Search. - Quan Minh Phan; Ngoc Hoang Luong (ICCCI, 2022)

Pareto Local Search is Competitive with Evolutionary Algorithms for Multi-Objective Neural Architecture Search. - Quan Minh Phan; Ngoc Hoang Luong (GECCO, 2023)

Enhancing Training-Free Multi-Objective Pruning-Based Neural Architecture Search with Low-Cost Local Search. - Quan Minh Phan; Ngoc Hoang Luong (RIVF, 2023)

Enhancing multi-objective evolutionary neural architecture search with training-free Pareto local search. - Quan Minh Phan; Ngoc Hoang Luong (Appl. Intell., 2023)

Efficient Multi-Fidelity Neural Architecture Search with Zero-Cost Proxy-Guided Local Search. - Quan Minh Phan; Ngoc Hoang Luong (GECCO, 2024)

Evaluating the Adversarial Robustness of Evolutionary Neural Architecture Search. - Quan Minh Phan; Hieu Trung Nguyen; Van Bich Nguyen; Ngoc Hoang Luong (MAPR, 2024)

Parameter-less Pareto local search for multi-objective neural architecture search with the Interleaved Multi-start Scheme. - Quan Minh Phan; Ngoc Hoang Luong (Swarm Evol. Comput., 2024)

Lightweight multi-objective evolutionary neural architecture search with low-cost proxy metrics. - Ngoc Hoang Luong; Quan Minh Phan; An Vo; Tan Ngoc Pham; Dzung Tri Bui (Inf. Sci., 2024)

From Hand-Crafted Metrics to Evolved Training-Free Performance Predictors for Neural Architecture Search via Genetic Programming. - Quan Minh Phan; Ngoc Hoang Luong (CoRR, 2025)