Papers Evolutionary Algorithms
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Mergenetic: a Simple Evolutionary Model Merging Library
Model merging allows combining the capabilities of existing models into a new one - post hoc, without additional training. This has made it increasingly popular thanks to its low cost and the availability of libraries th…
Evolutionary AlgorithmsmodelQuantum-Evolutionary Neural Networks for Multi-Agent Federated Learning
As artificial intelligence continues to drive innovation in complex, decentralized environments, the need for scalable, adaptive, and privacy-preserving decision-making systems has become critical. This paper introduces …
Decision MakingEvolutionary AlgorithmsFederated LearningPrivacy PreservingVisual Evolutionary Optimization on Combinatorial Problems with Multimodal Large Language Models: A Case Study of Influence Maximization
Graph-structured combinatorial problems in complex networks are prevalent in many domains, and are computationally demanding due to their complexity and non-linear nature. Traditional evolutionary algorithms (EAs), while…
Evolutionary AlgorithmsEvolutionary thoughts: integration of large language models and evolutionary algorithms
Large Language Models (LLMs) have unveiled remarkable capabilities in understanding and generating both natural language and code, but LLM reasoning is prone to hallucination and struggle with complex, novel scenarios, o…
Evolutionary AlgorithmsHallucinationvalidScalable Speed-ups for the SMS-EMOA from a Simple Aging Strategy
Different from single-objective evolutionary algorithms, where non-elitism is an established concept, multi-objective evolutionary algorithms almost always select the next population in a greedy fashion. In the only nota…
Evolutionary AlgorithmsProven Approximation Guarantees in Multi-Objective Optimization: SPEA2 Beats NSGA-II
Together with the NSGA-II and SMS-EMOA, the strength Pareto evolutionary algorithm 2 (SPEA2) is one of the most prominent dominance-based multi-objective evolutionary algorithms (MOEAs). Different from the NSGA-II, it do…
Evolutionary AlgorithmsWeighted-Scenario Optimisation for the Chance Constrained Travelling Thief Problem
The chance constrained travelling thief problem (chance constrained TTP) has been introduced as a stochastic variation of the classical travelling thief problem (TTP) in an attempt to embody the effect of uncertainty in …
Evolutionary AlgorithmsA Memetic Algorithm based on Variational Autoencoder for Black-Box Discrete Optimization with Epistasis among Parameters
Black-box discrete optimization (BB-DO) problems arise in many real-world applications, such as neural architecture search and mathematical model estimation. A key challenge in BB-DO is epistasis among parameters where m…
Evolutionary AlgorithmsNeural Architecture SearchGenerate more than one child in your co-evolutionary semi-supervised learning GAN
Generative Adversarial Networks (GANs) are very useful methods to address semi-supervised learning (SSL) datasets, thanks to their ability to generate samples similar to real data. This approach, called SSL-GAN has attra…
Evolutionary AlgorithmsIntelligent4DSE: Optimizing High-Level Synthesis Design Space Exploration with Graph Neural Networks and Large Language Models
High-level synthesis (HLS) design space exploration (DSE) is an optimization process in electronic design automation (EDA) that systematically explores high-level design configurations to achieve Pareto-optimal hardware …
Evolutionary AlgorithmsGraph Neural NetworkHigh-Level SynthesisLarge Language Model+1A Comparison-Relationship-Surrogate Evolutionary Algorithm for Multi-Objective Optimization
Evolutionary algorithms often struggle to find well converged (e.g small inverted generational distance on test problems) solutions to multi-objective optimization problems on a limited budget of function evaluations (he…
Evolutionary AlgorithmsA Systematic Study on the Design of Odd-Sized Highly Nonlinear Boolean Functions via Evolutionary Algorithms
This paper focuses on the problem of evolving Boolean functions of odd sizes with high nonlinearity, a property of cryptographic relevance. Despite its simple formulation, this problem turns out to be remarkably difficul…
Evolutionary AlgorithmsEvolution Meets Diffusion: Efficient Neural Architecture Generation
Neural Architecture Search (NAS) has gained widespread attention for its transformative potential in deep learning model design. However, the vast and complex search space of NAS leads to significant computational and ti…
DenoisingEvolutionary AlgorithmsNeural Architecture SearchEfficient Design of Compliant Mechanisms Using Multi-Objective Optimization
Compliant mechanisms achieve motion through elastic deformation. In this work, we address the synthesis of a compliant cross-hinge mechanism capable of large angular strokes while approximating the behavior of an ideal r…
Evolutionary AlgorithmsNeuro-Evolutionary Approach to Physics-Aware Symbolic Regression
Symbolic regression is a technique that can automatically derive analytic models from data. Traditionally, symbolic regression has been implemented primarily through genetic programming that evolves populations of candid…
Evolutionary AlgorithmsregressionSymbolic RegressionComparative Analysis of Evolutionary Algorithms for Energy-Aware Production Scheduling
The energy transition is driving rapid growth in renewable energy generation, creating the need to balance energy supply and demand with energy price awareness. One such approach for manufacturers to balance their energy…
Evolutionary AlgorithmsSchedulingRainbowPlus: Enhancing Adversarial Prompt Generation via Evolutionary Quality-Diversity Search
Large Language Models (LLMs) exhibit remarkable capabilities but are susceptible to adversarial prompts that exploit vulnerabilities to produce unsafe or biased outputs. Existing red-teaming methods often face scalabilit…
DiversityEvolutionary AlgorithmsRed TeamingA Balanced Approach of Rapid Genetic Exploration and Surrogate Exploitation for Hyperparameter Optimization
This paper proposes a new method for hyperparameter optimization (HPO) that balances exploration and exploitation. While evolutionary algorithms (EAs) show promise in HPO, they often struggle with effective exploitation.…
Evolutionary AlgorithmsHyperparameter OptimizationController Distillation Reduces Fragile Brain-Body Co-Adaptation and Enables Migrations in MAP-Elites
Brain-body co-optimization suffers from fragile co-adaptation where brains become over-specialized for particular bodies, hindering their ability to transfer well to others. Evolutionary algorithms tend to discard such l…
Evolutionary AlgorithmsEvolutionary algorithms meet self-supervised learning: a comprehensive survey
The number of studies that combine Evolutionary Machine Learning and self-supervised learning has been growing steadily in recent years. Evolutionary Machine Learning has been shown to help automate the design of machine…
Evolutionary AlgorithmsSelf-Supervised LearningSurvey