Papers Evolutionary Algorithms
“Evolutionary Algorithms” 태그가 달린 논문 1,107편 · 필터 해제
DASViT: Differentiable Architecture Search for Vision Transformer
Designing effective neural networks is a cornerstone of deep learning, and Neural Architecture Search (NAS) has emerged as a powerful tool for automating this process. Among the existing NAS approaches, Differentiable Ar…
Evolutionary AlgorithmsNeural Architecture SearchAdversarial attacks to image classification systems using evolutionary algorithms
Image classification currently faces significant security challenges due to adversarial attacks, which consist of intentional alterations designed to deceive classification models based on artificial intelligence. This a…
ClassificationDiversityEvolutionary AlgorithmsGenerative Adversarial Network+2Multi-population GAN Training: Analyzing Co-Evolutionary Algorithms
Generative adversarial networks (GANs) are powerful generative models but remain challenging to train due to pathologies suchas mode collapse and instability. Recent research has explored co-evolutionary approaches, in w…
DiversityEvolutionary AlgorithmsSensing Accuracy Optimization for Multi-UAV SAR Interferometry with Data Offloading
The integration of unmanned aerial vehicles (UAVs) with radar imaging sensors has revolutionized the monitoring of dynamic and local Earth surface processes by enabling high-resolution and cost-effective remote sensing. …
Deep Reinforcement LearningEvolutionary AlgorithmsEvolutionary and Coevolutionary Multi-Agent Design Choices and Dynamics
We investigate two representation alternatives for the controllers of teams of cyber agents. We combine these controller representations with different evolutionary algorithms, one of which introduces a novel LLM-support…
Evolutionary AlgorithmsLLM-guided Chemical Process Optimization with a Multi-Agent Approach
Chemical process optimization is crucial to maximize production efficiency and economic performance. Traditional methods, including gradient-based solvers, evolutionary algorithms, and parameter grid searches, become imp…
Chemical ProcessComputational EfficiencyEvolutionary AlgorithmsLarge Language ModelSurrogate-Assisted Evolution for Efficient Multi-branch Connection Design in Deep Neural Networks
State-of-the-art Deep Neural Networks (DNNs) often incorporate multi-branch connections, enabling multi-scale feature extraction and enhancing the capture of diverse features. This design improves network capacity and ge…
Evolutionary AlgorithmsSynergizing Reinforcement Learning and Genetic Algorithms for Neural Combinatorial Optimization
Combinatorial optimization problems are notoriously challenging due to their discrete structure and exponentially large solution space. Recent advances in deep reinforcement learning (DRL) have enabled the learning heuri…
Combinatorial OptimizationDeep Reinforcement LearningEvolutionary Algorithmsreinforcement-learning+1An Efficient Task-Oriented Dialogue Policy: Evolutionary Reinforcement Learning Injected by Elite Individuals
Deep Reinforcement Learning (DRL) is widely used in task-oriented dialogue systems to optimize dialogue policy, but it struggles to balance exploration and exploitation due to the high dimensionality of state and action …
Deep Reinforcement LearningEvolutionary AlgorithmsTask-Oriented Dialogue SystemsSurrogate-Assisted Evolutionary Reinforcement Learning Based on Autoencoder and Hyperbolic Neural Network
Evolutionary Reinforcement Learning (ERL), training the Reinforcement Learning (RL) policies with Evolutionary Algorithms (EAs), have demonstrated enhanced exploration capabilities and greater robustness than using tradi…
Evolutionary AlgorithmsMuJoCoMuJoCo GamesReinforcement Learning (RL)Integrating Dynamical Systems Learning with Foundational Models: A Meta-Evolutionary AI Framework for Clinical Trials
Artificial intelligence (AI) has evolved into an ecosystem of specialized "species," each with unique strengths. We analyze two: DeepSeek-V3, a 671-billion-parameter Mixture of Experts large language model (LLM) exemplif…
Evolutionary AlgorithmsLarge Language ModelMixture-of-ExpertsAdapting Novelty towards Generating Antigens for Antivirus systems
It is well known that anti-malware scanners depend on malware signatures to identify malware. However, even minor modifications to malware code structure results in a change in the malware signature thus enabling the var…
Evolutionary AlgorithmsMalware AnalysisMalware DetectionGraph-Supported Dynamic Algorithm Configuration for Multi-Objective Combinatorial Optimization
Deep reinforcement learning (DRL) has been widely used for dynamic algorithm configuration, particularly in evolutionary computation, which benefits from the adaptive update of parameters during the algorithmic execution…
Combinatorial OptimizationDeep Reinforcement LearningEvolutionary AlgorithmsGraph Neural NetworkCMA-ES with Radial Basis Function Surrogate for Black-Box Optimization
Evolutionary optimization algorithms often face defects and limitations that complicate the evolution processes or even prevent them from reaching the global optimum. A notable constraint pertains to the considerable qua…
Evolutionary AlgorithmsEvolutionary Computation and Large Language Models: A Survey of Methods, Synergies, and Applications
Integrating Large Language Models (LLMs) and Evolutionary Computation (EC) represents a promising avenue for advancing artificial intelligence by combining powerful natural language understanding with optimization and se…
Evolutionary AlgorithmsNatural Language UnderstandingPrompt EngineeringSurveyWeak Pareto Boundary: The Achilles' Heel of Evolutionary Multi-Objective Optimization
The weak Pareto boundary ($WPB$) refers to a boundary in the objective space of a multi-objective optimization problem, characterized by weak Pareto optimality rather than Pareto optimality. The $WPB$ brings severe chall…
AttributeEvolutionary AlgorithmsMMD-Newton Method for Multi-objective Optimization
Maximum mean discrepancy (MMD) has been widely employed to measure the distance between probability distributions. In this paper, we propose using MMD to solve continuous multi-objective optimization problems (MOPs). For…
Evolutionary AlgorithmsMulti-parameter Control for the (1+($λ$,$λ$))-GA on OneMax via Deep Reinforcement Learning
It is well known that evolutionary algorithms can benefit from dynamic choices of the key parameters that control their behavior, to adjust their search strategy to the different stages of the optimization process. A pro…
Deep Reinforcement LearningEvolutionary Algorithmsreinforcement-learningReinforcement LearningRandomised Optimism via Competitive Co-Evolution for Matrix Games with Bandit Feedback
Learning in games is a fundamental problem in machine learning and artificial intelligence, with numerous applications~\citep{silver2016mastering,schrittwieser2020mastering}. This work investigates two-player zero-sum ma…
Evolutionary AlgorithmsBenchmarking MOEAs for solving continuous multi-objective RL problems
Multi-objective reinforcement learning (MORL) addresses the challenge of simultaneously optimizing multiple, often conflicting, rewards, moving beyond the single-reward focus of conventional reinforcement learning (RL). …
BenchmarkingEvolutionary AlgorithmsMulti-Objective Reinforcement Learningreinforcement-learning+2