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🏛️ The Neural & Evolutionary Crypt
cs.NE: Where Neural & Evolutionary papers rest without their code.
6354
Total Papers
5604
No Code
153
Twilight
597
Has Code
9.4%
Survival Rate
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Optimal Design of Electric Machine with Efficient Handling of Constraints and Surrogate Assistance
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Cycle Mutation: Evolving Permutations via Cycle Induction
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A collaborative decomposition-based evolutionary algorithm integrating normal and penalty-based boundary intersection for many-objective optimization
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Population Diversity Leads to Short Running Times of Lexicase Selection
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Particle Swarm Optimization Based Demand Response Using Artificial Neural Network Based Load Prediction
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Fashion Style Generation: Evolutionary Search with Gaussian Mixture Models in the Latent Space
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Deep Learning in Spiking Phasor Neural Networks
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Oscillatory Neural Network as Hetero-Associative Memory for Image Edge Detection
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New hard benchmark functions for global optimization
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B2EA: An Evolutionary Algorithm Assisted by Two Bayesian Optimization Modules for Neural Architecture Search
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Search Trajectories Networks of Multiobjective Evolutionary Algorithms
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Beyond Rate Coding: Surrogate Gradients Enable Spike Timing Learning in Spiking Neural Networks
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Neuromorphic Sequential Arena: A Benchmark for Neuromorphic Temporal Processing
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Sigma-Delta Neural Network Conversion on Loihi 2
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Three-Factor Learning in Spiking Neural Networks: An Overview of Methods and Trends from a Machine Learning Perspective
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LLM-Guided Evolution: An Autonomous Model Optimization for Object Detection
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A Hybrid Swarm Intelligence Approach for Optimizing Multimodal Large Language Models Deployment in Edge-Cloud-based Federated Learning Environments
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Can Large Language Models Be Trusted as Evolutionary Optimizers for Network-Structured Combinatorial Problems?
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A Runtime Analysis of the Multi-Valued Compact Genetic Algorithm on Generalized LeadingOnes
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Evolutionary Optimization of Physics-Informed Neural Networks: Evo-PINN Frontiers and Opportunities
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Death by README
QUBE: Enhancing Automatic Heuristic Design via Quality-Uncertainty Balanced Evolution
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Combining Aggregated Attention and Transformer Architecture for Accurate and Efficient Performance of Spiking Neural Networks
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