Evolutionary Algorithms and Applications
346 papers on this topic
AI Research Agents for Machine Learning: Search, Exploration, and Generalization in MLE-bench
AI research agents are demonstrating great potential to accelerate scientific progress by automating the design, implementation, and training of machine learning models. We focus on methods for...
Symbolic Discovery of Optimization Algorithms
We present a method to formulate algorithm discovery as program search, and apply it to discover optimization algorithms for deep neural network training. We leverage efficient search techniques...
Group-Evolving Agents: Open-Ended Self-Improvement via Experience Sharing
Open-ended self-improving agents can autonomously modify their own structural designs to advance their capabilities and overcome the limits of pre-defined architectures, thus reducing reliance on...
CoMAS: Co-Evolving Multi-Agent Systems via Interaction Rewards
Self-evolution is a central research topic in enabling large language model (LLM)-based agents to continually improve their capabilities after pretraining. Recent research has witnessed a...
The FM Agent
Large language models (LLMs) are catalyzing the development of autonomous AI research agents for scientific and engineering discovery. We present FM Agent, a novel and general-purpose multi-agent...
Large Language Models As Evolution Strategies
Large Transformer models are capable of implementing a plethora of so-called in-context learning algorithms. These include gradient descent, classification, sequence completion, transformation,...
HiFo-Prompt: Prompting with Hindsight and Foresight for LLM-based Automatic Heuristic Design
LLM-based Automatic Heuristic Design (AHD) within Evolutionary Computation (EC) frameworks has shown promising results. However, its effectiveness is hindered by the use of static operators and...
Algorithm Evolution Using Large Language Model
Optimization can be found in many real-life applications. Designing an effective algorithm for a specific optimization problem typically requires a tedious amount of effort from human experts with...
LLMatic: Neural Architecture Search via Large Language Models and Quality Diversity Optimization
Large Language Models (LLMs) have emerged as powerful tools capable of accomplishing a broad spectrum of tasks. Their abilities span numerous areas, and one area where they have made a significant...
Fast Genetic Algorithm for feature selection -- A qualitative approximation approach
Evolutionary Algorithms (EAs) are often challenging to apply in real-world settings since evolutionary computations involve a large number of evaluations of a typically expensive fitness function....
REvolve: Reward Evolution with Large Language Models using Human Feedback
Designing effective reward functions is crucial to training reinforcement learning (RL) algorithms. However, this design is non-trivial, even for domain experts, due to the subjective nature of...
Instance Generation for Meta-Black-Box Optimization through Latent Space Reverse Engineering
To relieve intensive human-expertise required to design optimization algorithms, recent Meta-Black-Box Optimization (MetaBBO) researches leverage generalization strength of meta-learning to train...
The Procedural Content Generation Benchmark: An Open-source Testbed for Generative Challenges in Games
This paper introduces the Procedural Content Generation Benchmark for evaluating generative algorithms on different game content creation tasks. The benchmark comes with 12 game-related problems...
ReGAL: Refactoring Programs to Discover Generalizable Abstractions
While large language models (LLMs) are increasingly being used for program synthesis, they lack the global view needed to develop useful abstractions; they generally predict programs one at a...
OMNI: Open-endedness via Models of human Notions of Interestingness
Open-ended algorithms aim to learn new, interesting behaviors forever. That requires a vast environment search space, but there are thus infinitely many possible tasks. Even after filtering for...
TIDE: Tuning-Integrated Dynamic Evolution for LLM-Based Automated Heuristic Design
Although Large Language Models have advanced Automated Heuristic Design, treating algorithm evolution as a monolithic text generation task overlooks the coupling between discrete algorithmic...
An Evolutionary Multitasking Algorithm with Multiple Filtering for High-Dimensional Feature Selection
Recently, evolutionary multitasking (EMT) has been successfully used in the field of high-dimensional classification. However, the generation of multiple tasks in the existing EMT-based feature...
Evaluating LLMs in Open-Source Games
Large Language Models' (LLMs) programming capabilities enable their participation in open-source games: a game-theoretic setting in which players submit computer programs in lieu of actions. These...