Papers Evolutionary Algorithms
“Evolutionary Algorithms” 태그가 달린 논문 1,107편 · 필터 해제
Natural Variational Annealing for Multimodal Optimization
We introduce a new multimodal optimization approach called Natural Variational Annealing (NVA) that combines the strengths of three foundational concepts to simultaneously search for multiple global and local modes of bl…
Evolutionary AlgorithmsParetoLens: A Visual Analytics Framework for Exploring Solution Sets of Multi-objective Evolutionary Algorithms
In the domain of multi-objective optimization, evolutionary algorithms are distinguished by their capability to generate a diverse population of solutions that navigate the trade-offs inherent among competing objectives.…
Evolutionary AlgorithmsNavigateLearning Evolution via Optimization Knowledge Adaptation
Evolutionary algorithms (EAs) maintain populations through evolutionary operators to discover diverse solutions for complex tasks while gathering valuable knowledge, such as historical population data and fitness evaluat…
Evolutionary AlgorithmsLanguage ModelingLanguage ModellingTransfer LearningResolving the Exploration-Exploitation Dilemma in Evolutionary Algorithms: A Novel Human-Centered Framework
Evolutionary Algorithms (EAs) are widely employed tools for complex search and optimization tasks; however, the absence of an overarching operational framework that permits a systematic regulation of the exploration-expl…
Evolutionary AlgorithmsAn LLM-Empowered Adaptive Evolutionary Algorithm For Multi-Component Deep Learning Systems
Multi-objective evolutionary algorithms (MOEAs) are widely used for searching optimal solutions in complex multi-component applications. Traditional MOEAs for multi-component deep learning (MCDL) systems face challenges …
DiversityEvolutionary AlgorithmsNavigateQUBE: Enhancing Automatic Heuristic Design via Quality-Uncertainty Balanced Evolution
Solving NP-hard problems traditionally relies on heuristics, yet manually designing effective heuristics for complex problems remains a significant challenge. While recent advancements like FunSearch have shown that larg…
DiversityEvolutionary AlgorithmsGraph Neural Networks Are Evolutionary Algorithms
In this paper, we reveal the intrinsic duality between graph neural networks (GNNs) and evolutionary algorithms (EAs), bridging two traditionally distinct fields. Building on this insight, we propose Graph Neural Evoluti…
Evolutionary AlgorithmsNeural Architecture SearchQuantum Time-Series Learning with Evolutionary Algorithms
Variational quantum circuits have arisen as an important method in quantum computing. A crucial step of it is parameter optimization, which is typically tackled through gradient-descent techniques. We advantageously expl…
Evolutionary AlgorithmsTime SeriesTime Series ForecastingTwo Layer Walk: A Community-Aware Graph Embedding
Community structures are critical for understanding the mesoscopic organization of networks, bridging local and global patterns. While methods such as DeepWalk and node2vec capture local positional information through ra…
Evolutionary AlgorithmsGraph EmbeddingLink PredictionArtificial Intelligence in Traffic Systems
Existing research on AI-based traffic management systems, utilizing techniques such as fuzzy logic, reinforcement learning, deep neural networks, and evolutionary algorithms, demonstrates the potential of AI to transform…
Autonomous VehiclesEvolutionary AlgorithmsManagementTraffic Signal ControlRuntime Analysis for Multi-Objective Evolutionary Algorithms in Unbounded Integer Spaces
Randomized search heuristics have been applied successfully to a plethora of problems. This success is complemented by a large body of theoretical results. Unfortunately, the vast majority of these results regard problem…
Evolutionary AlgorithmsEVOS: Efficient Implicit Neural Training via EVOlutionary Selector
We propose EVOlutionary Selector (EVOS), an efficient training paradigm for accelerating Implicit Neural Representation (INR). Unlike conventional INR training that feeds all samples through the neural network in each it…
Evolutionary AlgorithmsSelection biasTowards Automated Algebraic Multigrid Preconditioner Design Using Genetic Programming for Large-Scale Laser Beam Welding Simulations
Multigrid methods are asymptotically optimal algorithms ideal for large-scale simulations. But, they require making numerous algorithmic choices that significantly influence their efficiency. Unlike recent approaches tha…
Evolutionary AlgorithmsConfigX: Modular Configuration for Evolutionary Algorithms via Multitask Reinforcement Learning
Recent advances in Meta-learning for Black-Box Optimization (MetaBBO) have shown the potential of using neural networks to dynamically configure evolutionary algorithms (EAs), enhancing their performance and adaptability…
Evolutionary AlgorithmsLifelong learningMeta-Learningreinforcement-learning+2Learnable Evolutionary Multi-Objective Combinatorial Optimization via Sequence-to-Sequence Model
Recent advances in learnable evolutionary algorithms have demonstrated the importance of leveraging population distribution information and historical evolutionary trajectories. While significant progress has been made i…
Combinatorial OptimizationDiversityEvolutionary AlgorithmsUsing Cooperative Co-evolutionary Search to Generate Metamorphic Test Cases for Autonomous Driving Systems
Autonomous Driving Systems (ADSs) rely on Deep Neural Networks, allowing vehicles to navigate complex, open environments. However, the unpredictability of these scenarios highlights the need for rigorous system-level tes…
Autonomous DrivingEvolutionary AlgorithmsNavigateUn-evaluated Solutions May Be Valuable in Expensive Optimization
Expensive optimization problems (EOPs) are prevalent in real-world applications, where the evaluation of a single solution requires a significant amount of resources. In our study of surrogate-assisted evolutionary algor…
Bayesian OptimizationEvolutionary AlgorithmsAutomated Test-Case Generation for REST APIs Using Model Inference Search Heuristic
The rising popularity of the microservice architectural style has led to a growing demand for automated testing approaches tailored to these systems. EvoMaster is a state-of-the-art tool that uses Evolutionary Algorithms…
Evolutionary AlgorithmsParetoFlow: Guided Flows in Multi-Objective Optimization
In offline multi-objective optimization (MOO), we leverage an offline dataset of designs and their associated labels to simultaneously minimize multiple objectives. This setting more closely mirrors complex real-world pr…
Bayesian OptimizationEvolutionary AlgorithmsOptimizing Genetic Algorithms Using the Binomial Distribution
Evolutionary algorithms rely very heavily on randomized behavior. Execution speed, therefore, depends strongly on how we implement randomness, such as our choice of pseudorandom number generator, or the algorithms used t…
Evolutionary Algorithms