:: Library Catalog

Copertina

Salvato in:

Dettagli Bibliografici
Autori principali:	Sen, Anuvab, Sen, Udayon, Roy, Subhabrata
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Neural and Evolutionary Computing Artificial Intelligence
Accesso online:	https://arxiv.org/abs/2401.09795
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Differential Evolution Algorithm based Hyper-Parameters Selection of Transformer Neural Network Model for Load Forecasting
di: Sen, Anuvab, et al.
Pubblicazione: (2023)

LLaMEA: A Large Language Model Evolutionary Algorithm for Automatically Generating Metaheuristics
di: van Stein, Niki, et al.
Pubblicazione: (2024)

A Tutorial on the Design, Experimentation and Application of Metaheuristic Algorithms to Real-World Optimization Problems
di: Osaba, Eneko, et al.
Pubblicazione: (2024)

AutoOpt: A General Framework for Automatically Designing Metaheuristic Optimization Algorithms with Diverse Structures
di: Zhao, Qi, et al.
Pubblicazione: (2022)

HRA: A Multi-Criteria Framework for Ranking Metaheuristic Optimization Algorithms
di: Goula, Evgenia-Maria K., et al.
Pubblicazione: (2024)

ClustOpt: A Clustering-based Approach for Representing and Visualizing the Search Dynamics of Numerical Metaheuristic Optimization Algorithms
di: Cenikj, Gjorgjina, et al.
Pubblicazione: (2025)

Memory-Driven Metaheuristics: Improving Optimization Performance
di: Farahmand-Tabar, Salar
Pubblicazione: (2024)

RAG/LLM Augmented Switching Driven Polymorphic Metaheuristic Framework
di: Esfahani, Faramarz Safi, et al.
Pubblicazione: (2025)

Comparing Optimization Algorithms Through the Lens of Search Behavior Analysis
di: Cenikj, Gjorgjina, et al.
Pubblicazione: (2025)

METAFOR: A Hybrid Metaheuristics Software Framework for Single-Objective Continuous Optimization Problems
di: Camacho-Villalón, Christian, et al.
Pubblicazione: (2025)

Applications of Nature-Inspired Metaheuristic Algorithms for Tackling Optimization Problems Across Disciplines
di: Cui, Elvis Han, et al.
Pubblicazione: (2023)

A Study of Hybrid and Evolutionary Metaheuristics for Single Hidden Layer Feedforward Neural Network Architecture
di: Kashyap, Gautam Siddharth, et al.
Pubblicazione: (2025)

Yukthi Opus: A Multi-Chain Hybrid Metaheuristic for Large-Scale NP-Hard Optimization
di: Vikraman, SB Danush, et al.
Pubblicazione: (2026)

Constrained Hybrid Metaheuristic Algorithm for Probabilistic Neural Networks Learning
di: Kowalski, Piotr A., et al.
Pubblicazione: (2025)

A Prescription of Methodological Guidelines for Comparing Bio-inspired Optimization Algorithms
di: LaTorre, Antonio, et al.
Pubblicazione: (2020)

Predicting Confinement Effect of Carbon Fiber Reinforced Polymers on Strength of Concrete using Metaheuristics-based Artificial Neural Networks
di: Wahab, Sarmed, et al.
Pubblicazione: (2023)

From Performance to Understanding: A Vision for Explainable Automated Algorithm Design
di: van Stein, Niki, et al.
Pubblicazione: (2025)

A Novel Pareto-optimal Ranking Method for Comparing Multi-objective Optimization Algorithms
di: Ibrahim, Amin, et al.
Pubblicazione: (2024)

Evolving Benchmark Functions to Compare Evolutionary Algorithms via Genetic Programming
di: He, Yifan, et al.
Pubblicazione: (2024)

Learning Alzheimer's Disease Signatures by bridging EEG with Spiking Neural Networks and Biophysical Simulations
di: Mamoń, Szymon, et al.
Pubblicazione: (2026)

Boosting the Efficiency of Metaheuristics Through Opposition-Based Learning in Optimum Locating of Control Systems in Tall Buildings
di: Farahmand-Tabar, Salar, et al.
Pubblicazione: (2024)

Metaheuristics for (Variable-Size) Mixed Optimization Problems: A Unified Taxonomy and Survey
di: Talbi, El-Ghazali
Pubblicazione: (2024)

Controlled Self-Evolution for Algorithmic Code Optimization
di: Hu, Tu, et al.
Pubblicazione: (2026)

Runtime Analysis for Permutation-based Evolutionary Algorithms
di: Doerr, Benjamin, et al.
Pubblicazione: (2022)

Rank-Based Learning and Local Model Based Evolutionary Algorithm for High-Dimensional Expensive Multi-Objective Problems
di: Chen, Guodong, et al.
Pubblicazione: (2023)

A Block-Coordinate Descent EMO Algorithm: Theoretical and Empirical Analysis
di: Doerr, Benjamin, et al.
Pubblicazione: (2024)

Trimming Down Large Spiking Vision Transformers via Heterogeneous Quantization Search
di: Xu, Boxun, et al.
Pubblicazione: (2024)

BEExformer: A Fast Inferencing Binarized Transformer with Early Exits
di: Ansar, Wazib, et al.
Pubblicazione: (2024)

Concentration Tail-Bound Analysis of Coevolutionary and Bandit Learning Algorithms
di: Lehre, Per Kristian, et al.
Pubblicazione: (2024)

Parent Selection Mechanisms in Elitist Crossover-Based Algorithms
di: Opris, Andre, et al.
Pubblicazione: (2026)

A Hybrid Spiking-Convolutional Neural Network Approach for Advancing Machine Learning Models
di: Sanaullah, et al.
Pubblicazione: (2024)

A Novel Ranking Scheme for the Performance Analysis of Stochastic Optimization Algorithms using the Principles of Severity
di: Chandrasekaran, Sowmya, et al.
Pubblicazione: (2024)

Runtime Analysis of Evolutionary Algorithms for Multi-party Multi-objective Optimization
di: Sun, Yuetong, et al.
Pubblicazione: (2025)

Theoretical Analysis of Quality Diversity Algorithms for a Classical Path Planning Problem
di: Dang, Duc-Cuong, et al.
Pubblicazione: (2024)

When Switching Algorithms Helps: A Theoretical Study of Online Algorithm Selection
di: Antipov, Denis, et al.
Pubblicazione: (2026)

An Explainable Reconfiguration-Based Optimization Algorithm for Industrial and Reliability-Redundancy Allocation Problems
di: Chauhan, Dikshit, et al.
Pubblicazione: (2025)

Guiding Evolution of Artificial Life Using Vision-Language Models
di: Baid, Nikhil, et al.
Pubblicazione: (2025)

Reinforcement learning Based Automated Design of Differential Evolution Algorithm for Black-box Optimization
di: Yang, Xu, et al.
Pubblicazione: (2025)

Deep Insights into Automated Optimization with Large Language Models and Evolutionary Algorithms
di: Yu, He, et al.
Pubblicazione: (2024)

Evolution of Optimization Algorithms for Global Placement via Large Language Models
di: Yao, Xufeng, et al.
Pubblicazione: (2025)