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May 1, 2026 · Artificial Intelligence

LLMs Write and Evolve Code to Redefine Quantitative Factor Mining – The CogAlpha ACL Paper

The CogAlpha framework upgrades Alpha discovery from static formulas to executable Python code, organizes a 7‑layer, 21‑agent research hierarchy, iteratively evolves factor candidates, and on CSI300 10‑day prediction outperforms 21 baselines with a 16.39% annual excess return and an IR of 1.8999, demonstrating that large models can actively participate in the discovery process.

ACL 2026Alpha MiningCode Generation

0 likes · 9 min read

LLMs Write and Evolve Code to Redefine Quantitative Factor Mining – The CogAlpha ACL Paper

SuanNi

Apr 28, 2026 · Artificial Intelligence

ASI‑EVOLVE: AI Designs AI and Beats Human SOTA by Almost Three‑Fold

The open‑source ASI‑EVOLVE framework lets AI autonomously design AI across model architecture, data curation, and reinforcement‑learning algorithms, achieving up to three times the human‑level state‑of‑the‑art performance and demonstrating cross‑domain gains in drug‑target prediction.

AI-driven AIASI-EVOLVECross-domain AI

0 likes · 12 min read

ASI‑EVOLVE: AI Designs AI and Beats Human SOTA by Almost Three‑Fold

SuanNi

Apr 2, 2026 · Artificial Intelligence

EvoSkill: Turning AI Failures into 12% Accuracy Gains with Automated Skill Evolution

The EvoSkill framework introduced by Sentient and Virginia Tech researchers equips large language models with a text‑feedback loop that automatically discovers, refines, and validates reusable agent Skills, boosting task‑specific accuracy by 12.1% and enabling cross‑domain transfer without altering the underlying model parameters.

AIAutomated LearningEvolutionary Algorithms

0 likes · 11 min read

EvoSkill: Turning AI Failures into 12% Accuracy Gains with Automated Skill Evolution

AI Explorer

Mar 2, 2026 · Operations

Huawei Team’s LLM‑Enhanced Algorithm Wins CVRP Challenge, Redefining Optimization Design

A joint Huawei and City University of Hong Kong team combined large language models with evolutionary computation to solve the capacity‑constrained vehicle routing problem, winning the CVRPLib BKS Global Challenge and demonstrating how AI can automate and transform algorithm design, heralding a new paradigm for operations optimization.

AI for ScienceCVRPEvolutionary Algorithms

0 likes · 7 min read

Huawei Team’s LLM‑Enhanced Algorithm Wins CVRP Challenge, Redefining Optimization Design

Baidu Intelligent Cloud Tech Hub

Jan 20, 2026 · Artificial Intelligence

How LoongFlow Enables Expert‑Level AI Agents to Outperform Human Mathematicians

LoongFlow is an open‑source AI agent framework that combines a Plan‑Execute‑Summarize (PES) paradigm with a Hybrid Evolutionary Memory system, allowing agents to perform directed, iterative problem solving and achieve state‑of‑the‑art results on mathematical challenges, Kaggle‑style benchmarks, and real‑world tasks with dramatically higher efficiency.

BenchmarkingEvolutionary AlgorithmsLoongFlow

0 likes · 15 min read

How LoongFlow Enables Expert‑Level AI Agents to Outperform Human Mathematicians

SF Technology Team

Nov 10, 2025 · Artificial Intelligence

Deep RL Powers Multi‑Population Evolution for Better Many‑Objective Optimization

This study introduces DQNMaOEA, a deep reinforcement learning‑guided multi‑population coevolutionary algorithm that adaptively selects sub‑populations and allocates computational resources, achieving significantly higher solution quality and up to 25% faster runtimes on benchmark and large‑scale logistics many‑objective problems compared with state‑of‑the‑art methods.

Evolutionary AlgorithmsLogisticsdeep reinforcement learning

0 likes · 3 min read

Deep RL Powers Multi‑Population Evolution for Better Many‑Objective Optimization

Bighead's Algorithm Notes

Sep 9, 2025 · Artificial Intelligence

How EFS Leverages Large Language Models for Sparse Portfolio Optimization

The paper introduces the Evolutionary Factor Search (EFS) framework, which uses large language models to automatically generate and evolve alpha factors, turning sparse portfolio selection into an LLM‑guided top‑m ranking task, and demonstrates superior performance on multiple Fama‑French benchmarks and real‑world market datasets.

Alpha FactorsEvolutionary AlgorithmsFactor Search

0 likes · 11 min read

How EFS Leverages Large Language Models for Sparse Portfolio Optimization

Data STUDIO

Sep 2, 2025 · Artificial Intelligence

Understanding NAS: Core Algorithms and Python Implementations

This article reviews Neural Architecture Search (NAS), explains its bi‑level optimization formulation, compares three major search strategies—reinforcement learning, evolutionary algorithms, and differentiable gradient‑based methods—provides complete Python code for each, and analyzes experimental results highlighting performance trade‑offs and remaining challenges.

Deep LearningDifferentiable Architecture SearchEvolutionary Algorithms

0 likes · 25 min read

Understanding NAS: Core Algorithms and Python Implementations

Data Party THU

Aug 10, 2025 · Artificial Intelligence

Can Evolutionary Algorithms Auto-Design Training-Free Vision-Language Model Adaptations?

This study introduces EvoVLMA, an evolutionary vision-language model adaptation framework that automatically searches training-free VLM adaptation algorithms using a two-stage LLM-guided evolution, demonstrating superior performance—such as a 1.91 % accuracy gain on 8-shot image classification—and releasing the code publicly.

Evolutionary AlgorithmsLLMModel Adaptation

0 likes · 5 min read

Can Evolutionary Algorithms Auto-Design Training-Free Vision-Language Model Adaptations?

Model Perspective

Jun 23, 2024 · Artificial Intelligence

Mastering Multi-Objective Optimization with NSGA-II: Theory and Python Example

This article introduces the fundamentals of multi‑objective optimization, explains the NSGA‑II algorithm’s non‑dominated sorting, crowding distance, and selection mechanisms, and demonstrates its application to a production‑line case study with a complete Python implementation and visualized Pareto front.

Evolutionary AlgorithmsNSGA-IIPareto Front

0 likes · 10 min read

Mastering Multi-Objective Optimization with NSGA-II: Theory and Python Example

Model Perspective

Nov 1, 2023 · Artificial Intelligence

Master Differential Evolution: Principles, Code Example, and GA Comparison

This article introduces the Differential Evolution (DE) algorithm, detailing its core concepts of mutation, crossover, and selection, outlines the step-by-step procedure, provides a Python implementation, compares DE with Genetic Algorithms, and highlights practical applications across engineering, machine learning, and image processing.

Differential EvolutionEvolutionary AlgorithmsPython

0 likes · 11 min read

Master Differential Evolution: Principles, Code Example, and GA Comparison

Model Perspective

Nov 7, 2022 · Artificial Intelligence

How Particle Swarm Optimization Finds Global Optima: Theory and Practice

Particle Swarm Optimization (PSO), inspired by bird flocking behavior, is a simple yet powerful evolutionary computation technique that updates particle positions and velocities using personal and global bests, offering fast convergence, parallelism, and wide applications across function optimization, neural networks, pattern recognition, and robotics.

Evolutionary Algorithmsswarm intelligence

0 likes · 9 min read

How Particle Swarm Optimization Finds Global Optima: Theory and Practice

Model Perspective

Aug 25, 2022 · Artificial Intelligence

How Particle Swarm Optimization Mimics Nature to Find Global Optima

Particle Swarm Optimization (PSO), inspired by bird flocking behavior, is a simple yet powerful population‑based metaheuristic that updates particle velocities and positions using individual and global bests, offering fast convergence, robustness, and wide applications across function optimization, neural networks, classification, and robotics.

Evolutionary Algorithmsglobal optimizationinertia weight

0 likes · 9 min read

How Particle Swarm Optimization Mimics Nature to Find Global Optima

Top Architect

Jan 16, 2020 · Artificial Intelligence

A Survey of Neural Architecture Search: Search Spaces, Optimization Strategies, and Recent Results

This article surveys neural architecture search, classifying existing methods, describing common search spaces—including global and cell‑based designs—detailing optimization strategies such as reinforcement learning, evolutionary algorithms, surrogate models, one‑shot and differentiable approaches, and highlighting recent results and trends in the field.

Evolutionary AlgorithmsNASNeural Architecture Search

0 likes · 13 min read