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M. Ö. Efe

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Review Open access Jul 2026

Evolutionary neural architecture search: a survey

Deep Neural Networks (DNNs) have achieved remarkable success across diverse machine learning applications, yet designing effective architectures remains a laborious, expert-driven process. Neural Architecture Search (NAS) was introduced to automate this process, with Evolutionary NAS (ENAS) emerging as one of the most effective and widely adopted NAS paradigms. This survey provides a comprehensive and systematic review of 164 ENAS studies published between 2020 and 2024, categorized according to the specific evolutionary algorithm employed as the search strategy. Unlike prior surveys—which either treat evolutionary methods at a high level or focus on general NAS pipelines—this study is, to the best of our knowledge, the first to perform an in-depth, algorithm-level breakdown of ENAS research, covering evolutionary strategies (ES), genetic algorithms (GA) and their derivatives, Particle Swarm Optimization (PSO), Differential Evolution (DE), Quantum-Inspired Evolutionary Algorithms (QIEA), and other heuristics. Our quantitative analysis reveals that ES is the dominant paradigm (45.7% of studies), followed by GA (29.9%), with annual publication counts growing 6.6-fold from 2020 to 2024—reflecting a rapidly expanding field. Key findings include: (i) surrogate-assisted methods have grown substantially in 2023--2024, significantly reducing search cost; (ii) GA-based multiobjective approaches (NSGA-II, NSGA-III) dominate Pareto-optimal architecture search; (iii) hybridization of evolutionary search with gradient descent and predictor networks is an accelerating trend; and (iv) image classification on CIFAR-10/100 and NAS-Bench-201 benchmarks accounts for the majority of evaluations, highlighting opportunities in underexplored domains. This survey also provides a critical comparison of encoding strategies (fixed-length vs. variable-length) and an analysis of search space complexity across reviewed methods. We believe this work serves as a practical guide for researchers seeking to understand, select, and advance evolutionary strategies for automated neural architecture design.

Ferda Özçelik, M. Ö. Efe · 1 citation
Conference Jul 2026

Double Deep Reinforcement Learning–Based UAV Positioning for Throughput Optimization in Wireless Networks

This work investigates a reinforcement learning-based control framework for the autonomous movement and coordination of multiple Unmanned Aerial Vehicles (UAVs) in a wireless communication environment. The considered system includes UAVs performing sensing and relaying tasks, where mobility decisions directly affect the overall network performance. The main objective is to improve the communication quality of ground users by maximizing aggregate network throughput. To achieve this objective, a Double Deep Q-Network (DDQN) architecture is employed, where each UAV is assigned an individual learning agent. The agents learn role-specific movement policies while coordinating through interactions with the shared environment. Learning performance is further improved by using adaptive scaling and a custom reward function designed to capture variations in network utility. Simulation results show that the proposed approach outperforms baseline movement strategies in terms of utility. In addition, different task configurations, agent behaviors, and hyperparameter selections are examined to improve convergence speed and training stability. Overall, the results indicate that reinforcement learning is a promising method for cooperative UAV positioning in dynamic and interference-sensitive wireless communication scenarios.

Berke Kilinç, M. Ö. Efe · 0 citations