Papers Evolutionary Algorithms
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BlackDAN: A Black-Box Multi-Objective Approach for Effective and Contextual Jailbreaking of Large Language Models
While large language models (LLMs) exhibit remarkable capabilities across various tasks, they encounter potential security risks such as jailbreak attacks, which exploit vulnerabilities to bypass security measures and ge…
Evolutionary AlgorithmsSynthesizing Interpretable Control Policies through Large Language Model Guided Search
The combination of Large Language Models (LLMs), systematic evaluation, and evolutionary algorithms has enabled breakthroughs in combinatorial optimization and scientific discovery. We propose to extend this powerful com…
Combinatorial OptimizationEvolutionary AlgorithmsLanguage ModelingLanguage Modelling+2Diffusion Models are Evolutionary Algorithms
In a convergence of machine learning and biology, we reveal that diffusion models are evolutionary algorithms. By considering evolution as a denoising process and reversed evolution as diffusion, we mathematically demons…
DenoisingEvolutionary AlgorithmsLarge Language Model Aided Multi-objective Evolutionary Algorithm: a Low-cost Adaptive Approach
Multi-objective optimization is a common problem in practical applications, and multi-objective evolutionary algorithm (MOEA) is considered as one of the effective methods to solve these problems. However, their randomne…
Evolutionary Algorithmsglobal-optimizationLanguage ModelingLanguage Modelling+1Cross-Modality Attack Boosted by Gradient-Evolutionary Multiform Optimization
In recent years, despite significant advancements in adversarial attack research, the security challenges in cross-modal scenarios, such as the transferability of adversarial attacks between infrared, thermal, and RGB im…
Adversarial AttackEvolutionary AlgorithmsA Multi-operator Ensemble LSHADE with Restart and Local Search Mechanisms for Single-objective Optimization
In recent years, multi-operator and multi-method algorithms have succeeded, encouraging their combination within single frameworks. Despite promising results, there remains room for improvement as only some evolutionary …
Evolutionary AlgorithmsSymbolic Regression with a Learned Concept Library
We present a novel method for symbolic regression (SR), the task of searching for compact programmatic hypotheses that best explain a dataset. The problem is commonly solved using genetic algorithms; we show that we can …
Evolutionary AlgorithmsLanguage ModelingLanguage ModellingLarge Language Model+2Optimizing Feature Selection with Genetic Algorithms: A Review of Methods and Applications
Analyzing large datasets to select optimal features is one of the most important research areas in machine learning and data mining. This feature selection procedure involves dimensionality reduction which is crucial in …
AttributeDimensionality ReductionEvolutionary Algorithmsfeature selectionEvolutionary Algorithms for One-Sided Bipartite Crossing Minimisation
Evolutionary algorithms (EAs) are universal solvers inspired by principles of natural evolution. In many applications, EAs produce astonishingly good solutions. As they are able to deal with complex optimisation problems…
Evolutionary AlgorithmsLibMOON: A Gradient-based MultiObjective OptimizatioN Library in PyTorch
Multiobjective optimization problems (MOPs) are prevalent in machine learning, with applications in multi-task learning, learning under fairness or robustness constraints, etc. Instead of reducing multiple objective func…
Evolutionary AlgorithmsFairnessMultiobjective OptimizationMulti-Task LearningEvolutionary Algorithms Are Significantly More Robust to Noise When They Ignore It
Randomized search heuristics (RSHs) are known to have a certain robustness to noise. Mathematical analyses trying to quantify rigorously how robust RSHs are to a noisy access to the objective function typically assume th…
Evolutionary AlgorithmsMathematical ProofsAn Adaptive Latent Factorization of Tensors Model for Embedding Dynamic Communication Network
The Dynamic Communication Network (DCN) describes the interactions over time among various communication nodes, and it is widely used in Big-data applications as a data source. As the number of communication nodes increa…
Evolutionary AlgorithmsProcedural Synthesis of Synthesizable Molecules
Designing synthetically accessible molecules and recommending analogs to unsynthesizable molecules are important problems for accelerating molecular discovery. We reconceptualize both problems using ideas from program sy…
Evolutionary AlgorithmsProgram SynthesisA Safe Self-evolution Algorithm for Autonomous Driving Based on Data-Driven Risk Quantification Model
Autonomous driving systems with self-evolution capabilities have the potential to independently evolve in complex and open environments, allowing to handle more unknown scenarios. However, as a result of the safety-perfo…
Autonomous DrivingEvolutionary AlgorithmsQuantizationSafe ExplorationEvaluation Framework for AI-driven Molecular Design of Multi-target Drugs: Brain Diseases as a Case Study
The widespread application of Artificial Intelligence (AI) techniques has significantly influenced the development of new therapeutic agents. These computational methods can be used to design and predict the properties o…
Drug DiscoveryEvolutionary AlgorithmsSurrogate-Assisted Search with Competitive Knowledge Transfer for Expensive Optimization
Expensive optimization problems (EOPs) have attracted increasing research attention over the decades due to their ubiquity in a variety of practical applications. Despite many sophisticated surrogate-assisted evolutionar…
Evolutionary AlgorithmsTransfer LearningOnline Optimization of Curriculum Learning Schedules using Evolutionary Optimization
We propose RHEA CL, which combines Curriculum Learning (CL) with Rolling Horizon Evolutionary Algorithms (RHEA) to automatically produce effective curricula during the training of a reinforcement learning agent. RHEA CL …
Evolutionary Algorithmsreinforcement-learningReinforcement LearningReinforcement Learning (RL)Evolutionary Neural Architecture Search for 3D Point Cloud Analysis
Neural architecture search (NAS) automates neural network design by using optimization algorithms to navigate architecture spaces, reducing the burden of manual architecture design. While NAS has achieved success, applyi…
Evolutionary AlgorithmsNavigateNeural Architecture SearchParetoTracker: Understanding Population Dynamics in Multi-objective Evolutionary Algorithms through Visual Analytics
Multi-objective evolutionary algorithms (MOEAs) have emerged as powerful tools for solving complex optimization problems characterized by multiple, often conflicting, objectives. While advancements have been made in comp…
Computational EfficiencyEvolutionary AlgorithmsTheoretical Advantage of Multiobjective Evolutionary Algorithms for Problems with Different Degrees of Conflict
The field of multiobjective evolutionary algorithms (MOEAs) often emphasizes its popularity for optimization problems with conflicting objectives. However, it is still theoretically unknown how MOEAs perform for differen…
Evolutionary Algorithms