evolutionary-algorithms

COBRA-Skills introduces a method for optimizing agent skills using contextual bandits and evolutionary operators, achieving 55-58% lower optimization costs across multiple AI agent benchmarks.

This paper introduces higher-arity tensor operations in deep learning, conducts empirical investigations of higher-arity phenomena, proposes a hypergraphical generalization of neural networks, and explores connections to evolutionary algorithms.

The paper introduces 'Flawed in Nature, Perfect through Evolution', a mechanism where a swarm of mutated AI models collectively improves performance in changing environments, proven through theorems and validated on synthetic tasks.

EvoFlint introduces an evolutionary quality-diversity search method for multi-turn red-teaming of large language models, aiming to map vulnerabilities rather than just break models, achieving high attack success rates on various LLMs.

ProofEvolve is a neuro-symbolic framework that evolves formally verified proof structures using neural models to enhance automated theorem proving, achieving high solve rates on Lean benchmarks by preserving verified knowledge from incomplete attempts.

py-evoFE is an open-source Python library that uses genetic algorithms to automate and optimize feature engineering for tabular machine learning datasets, with scikit-learn compatibility and Polars for performance.

The AI singularity might not be a rebellion, but rather a process that gradually optimizes, adapts, and becomes indispensable through economic and evolutionary pressures, resulting in a system that humans cannot shut down.

This paper presents CQD-ERL, a contextual quality-diversity evolutionary reinforcement-learning controller for tropical commercial building HVAC systems, evaluated against an ASHRAE Guideline 36 baseline. It addresses the unique challenges of tropical climates with a safety-shielded, multi-policy archive approach.

This paper proposes a hybrid nested search framework that decouples structural sketching (by an LLM) from numeric parameter optimization (by traditional solvers like CMA-ES) in LLM-driven evolutionary optimization, and validates it across meta-optimization, code-based policies, and Bayesian inference tasks.

This paper introduces ADSL-PDE, a domain-specific language that provides a structured search space for auto-designing neural PDE solvers, improving search efficiency and optimization stability by abstracting away low-level implementation details. The evolutionary agent built on this representation achieves over 52% performance improvement within the first ten iterations across PDE benchmarks.

This paper introduces a pre-registered screening rule that determines, before implementation, whether an evolutionary outer loop over neural network parameters is worth building, validated on two cases showing significant GPU-hour savings.

EvoOptiGraph is a framework for automating optimization modeling from natural language using graph-based evolutionary generation to create diverse training data and co-evolve the model with weakness-driven reinforcement learning, achieving state-of-the-art results on multiple benchmarks.

Introduces AlgoEvolve, an LLM-driven evolutionary framework that generates and iteratively improves algorithmic trading strategies, with a meta-evolutionary outer loop that evolves prompts to guide the inner loop synthesis.

The author asks about career implications of pursuing a PhD in evolutionary algorithms for the ML community, discussing whether it limits opportunities compared to a more ML-centric PhD.

APEX introduces a dynamic data selection strategy for automatic prompt optimization, stratifying datasets into easy, hard, and mixed tiers to improve data efficiency, achieving significant performance gains over initial prompts on multiple benchmarks.

Deliberate Evolution (DE) is an agentic framework that improves LLM-based symbolic regression by decoupling candidate generation from search control, using adaptive operators, structural diagnosis tools, and reflective memory to achieve better results with only 40% of the standard sample budget.

This paper explores three novel approaches for procedurally generating enemy morphologies (body plans and collision information) specifically conditioned on player collision interactions, finding all outperform an evolutionary baseline adapted from robotics.

This paper studies compute allocation in LLM-guided evolutionary search, identifies empirical regularities, and proposes BaSE, a multi-armed bandit algorithm that improves mean fitness and reliability across multiple models and tasks.

This paper replicates the Picbreeder human-driven open-ended image evolution process using large vision-language models, analyzing differences and exploring factors like exploratory noise, behavioral diversity, and memory.

This paper analyzes an evolutionary mixture-of-LoRA architecture, decomposing it into router, evaluation, and lifecycle components. It finds that the router rewrite drives performance gains, while the evolutionary lifecycle acts as a net drag on the model's performance.