Papers Evolutionary Algorithms
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Open Source Evolutionary Computation with Chips-n-Salsa
When it was first introduced, the Chips-n-Salsa Java library provided stochastic local search and related algorithms, with a focus on self-adaptation and parallel execution. For the past four years, we expanded its scope…
Evolutionary Algorithmstest driven developmentSTAR: Synthesis of Tailored Architectures
Iterative improvement of model architectures is fundamental to deep learning: Transformers first enabled scaling, and recent advances in model hybridization have pushed the quality-efficiency frontier. However, optimizin…
Evolutionary AlgorithmsLanguage ModelingLanguage ModellingAdaptiveMDL-GenClust: A Robust Clustering Framework Integrating Normalized Mutual Information and Evolutionary Algorithms
Clustering algorithms are pivotal in data analysis, enabling the organization of data into meaningful groups. However, individual clustering methods often exhibit inherent limitations and biases, preventing the developme…
ClusteringEvolutionary AlgorithmsOptimal MPPT Control of a Photovoltaic System Under Non-uniform Irradiation
Under Partial shading conditions (PSC), traditional MPPT methods such as, P&O, IncCon and SMC, cannot track down the Global MPP. Thus, the energy conversion of the PV modules will decrease. To overcome this drawback, two…
Evolutionary AlgorithmsEvolutionary Automata and Deep Evolutionary Computation
Evolution by natural selection, which is one of the most compelling themes of modern science, brought forth evolutionary algorithms and evolutionary computation, applying mechanisms of evolution in nature to various prob…
Evolutionary AlgorithmsHeuristically Adaptive Diffusion-Model Evolutionary Strategy
Diffusion Models represent a significant advancement in generative modeling, employing a dual-phase process that first degrades domain-specific information via Gaussian noise and restores it through a trainable model. Th…
Evolutionary AlgorithmsmodelLanguage Model Evolutionary Algorithms for Recommender Systems: Benchmarks and Algorithm Comparisons
In the evolutionary computing community, the remarkable language-handling capabilities and reasoning power of large language models (LLMs) have significantly enhanced the functionality of evolutionary algorithms (EAs), e…
Evolutionary AlgorithmsLanguage ModelingLanguage ModellingRecommendation Systems+1Multi-Objective Algorithms for Learning Open-Ended Robotic Problems
Quadrupedal locomotion is a complex, open-ended problem vital to expanding autonomous vehicle reach. Traditional reinforcement learning approaches often fall short due to training instability and sample inefficiency. We …
DiversityEvolutionary AlgorithmsMuJoCoAre Neuromorphic Architectures Inherently Privacy-preserving? An Exploratory Study
While machine learning (ML) models are becoming mainstream, especially in sensitive application areas, the risk of data leakage has become a growing concern. Attacks like membership inference (MIA) have shown that traine…
Evolutionary AlgorithmsPrivacy PreservingMOANA: Multi-Objective Ant Nesting Algorithm for Optimization Problems
This paper presents the Multi-Objective Ant Nesting Algorithm (MOANA), a novel extension of the Ant Nesting Algorithm (ANA), specifically designed to address multi-objective optimization problems (MOPs). MOANA incorporat…
DiversityEvolutionary AlgorithmsHarnessing the Power of Gradient-Based Simulations for Multi-Objective Optimization in Particle Accelerators
Particle accelerator operation requires simultaneous optimization of multiple objectives. Multi-Objective Optimization (MOO) is particularly challenging due to trade-offs between the objectives. Evolutionary algorithms, …
Bayesian OptimizationEvolutionary Algorithmsreinforcement-learningReinforcement LearningAn Efficient Dynamic Resource Allocation Framework for Evolutionary Bilevel Optimization
Bilevel optimization problems are characterized by an interactive hierarchical structure, where the upper level seeks to optimize its strategy while simultaneously considering the response of the lower level. Evolutionar…
Bilevel OptimizationEvolutionary AlgorithmsAutomatic programming via large language models with population self-evolution for dynamic job shop scheduling problem
Heuristic dispatching rules (HDRs) are widely regarded as effective methods for solving dynamic job shop scheduling problems (DJSSP) in real-world production environments. However, their performance is highly scenario-de…
Deep Reinforcement LearningEvolutionary AlgorithmsJob Shop SchedulingPrompt Engineering+1Deep Insights into Automated Optimization with Large Language Models and Evolutionary Algorithms
Designing optimization approaches, whether heuristic or meta-heuristic, usually demands extensive manual intervention and has difficulty generalizing across diverse problem domains. The combination of Large Language Mode…
Evolutionary AlgorithmsOptimizing Hearthstone Agents using an Evolutionary Algorithm
Digital collectible card games are not only a growing part of the video game industry, but also an interesting research area for the field of computational intelligence. This game genre allows researchers to deal with hi…
Card GamesDecision MakingEvolutionary AlgorithmsSelf-LearningComparative Analysis of Indicators for Multiobjective Diversity Optimization
Indicator-based (multiobjective) diversity optimization aims at finding a set of near (Pareto-)optimal solutions that maximizes a diversity indicator, where diversity is typically interpreted as the number of essentially…
DiversityEvolutionary AlgorithmsMultiobjective OptimizationAn Inverse Modeling Constrained Multi-Objective Evolutionary Algorithm Based on Decomposition
This paper introduces the inverse modeling constrained multi-objective evolutionary algorithm based on decomposition (IM-C-MOEA/D) for addressing constrained real-world optimization problems. Our research builds upon the…
Evolutionary AlgorithmsAutoRNet: Automatically Optimizing Heuristics for Robust Network Design via Large Language Models
Achieving robust networks is a challenging problem due to its NP-hard nature and complex solution space. Current methods, from handcrafted feature extraction to deep learning, have made progress but remain rigid, requiri…
DiversityEvolutionary AlgorithmsSpiking Neural Networks as a Controller for Emergent Swarm Agents
Drones which can swarm and loiter in a certain area cost hundreds of dollars, but mosquitos can do the same and are essentially worthless. To control swarms of low-cost robots, researchers may end up spending countless h…
Evolutionary AlgorithmsSelection of Filters for Photonic Crystal Spectrometer Using Domain-Aware Evolutionary Algorithms
This work addresses the critical challenge of optimal filter selection for a novel trace gas measurement device. This device uses photonic crystal filters to retrieve trace gas concentrations prone to photon and read noi…
Combinatorial OptimizationEvolutionary AlgorithmsRetrieval