Papers Evolutionary Algorithms
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Evolutionary Large Language Model for Automated Feature Transformation
Feature transformation aims to reconstruct the feature space of raw features to enhance the performance of downstream models. However, the exponential growth in the combinations of features and operations poses a challen…
Efficient ExplorationEvolutionary AlgorithmsLanguage ModelingLanguage Modelling+1DendroPy 5: a mature Python library for phylogenetic computing
Contemporary bioinformatics has seen in profound new visibility into the composition, structure, and history of the natural world around us. Arguably, the central pillar of bioinformatics is phylogenetics -- the study of…
Evolutionary AlgorithmsAn Efficient Approach for Solving Expensive Constrained Multiobjective Optimization Problems
To solve real-world expensive constrained multi-objective optimization problems (ECMOPs), surrogate/approximation models are commonly incorporated in evolutionary algorithms to pre-select promising candidate solutions fo…
Evolutionary AlgorithmsMultiobjective OptimizationInfluence Maximization in Hypergraphs using Multi-Objective Evolutionary Algorithms
The Influence Maximization (IM) problem is a well-known NP-hard combinatorial problem over graphs whose goal is to find the set of nodes in a network that spreads influence at most. Among the various methods for solving …
DiversityEvolutionary AlgorithmsEnhancing Decision-Making in Optimization through LLM-Assisted Inference: A Neural Networks Perspective
This paper explores the seamless integration of Generative AI (GenAI) and Evolutionary Algorithms (EAs) within the domain of large-scale multi-objective optimization. Focusing on the transformative role of Large Language…
Decision MakingEvolutionary AlgorithmsLarge Language Model-Aided Evolutionary Search for Constrained Multiobjective Optimization
Evolutionary algorithms excel in solving complex optimization problems, especially those with multiple objectives. However, their stochastic nature can sometimes hinder rapid convergence to the global optima, particularl…
Evolutionary AlgorithmsLanguage ModelingLanguage ModellingLarge Language Model+2A Newton Method for Hausdorff Approximations of the Pareto Front within Multi-objective Evolutionary Algorithms
A common goal in evolutionary multi-objective optimization is to find suitable finite-size approximations of the Pareto front of a given multi-objective optimization problem. While many multi-objective evolutionary algor…
Evolutionary AlgorithmsSMARTINI3: Systematic Parametrization of Realistic Multi-Scale Membrane Models via Unsupervised Learning and Multi-Objective Evolutionary Algorithms
In this study, we utilize genetic algorithms to develop a realistic implicit solvent ultra-coarse-grained (PC) membrane model comprising only three interaction sites. The key philosophy of the ultra-CG membrane model SMA…
Evolutionary AlgorithmsPhilosophyGenetic Drift Regularization: on preventing Actor Injection from breaking Evolution Strategies
Evolutionary Algorithms (EA) have been successfully used for the optimization of neural networks for policy search, but they still remain sample inefficient and underperforming in some cases compared to gradient-based re…
Evolutionary AlgorithmsReinforcement Learning (RL)Pretrained Optimization Model for Zero-Shot Black Box Optimization
Zero-shot optimization involves optimizing a target task that was not seen during training, aiming to provide the optimal solution without or with minimal adjustments to the optimizer. It is crucial to ensure reliable an…
Evolutionary AlgorithmsCommon pitfalls to avoid while using multiobjective optimization in machine learning
Recently, there has been an increasing interest in the application of multiobjective optimization (MOO) in machine learning (ML). This interest is driven by the numerous real-life situations where multiple objectives mus…
Evolutionary AlgorithmsMisconceptionsMultiobjective OptimizationOptimized Drug Design using Multi-Objective Evolutionary Algorithms with SELFIES
Computer aided drug design is a promising approach to reduce the tremendous costs, i.e. time and resources, for developing new medicinal drugs. It finds application in aiding the traversal of the vast chemical space of p…
Drug DesignEvolutionary AlgorithmsGEEvo: Game Economy Generation and Balancing with Evolutionary Algorithms
Game economy design significantly shapes the player experience and progression speed. Modern game economies are becoming increasingly complex and can be very sensitive to even minor numerical adjustments, which may have …
Evolutionary AlgorithmsRevolutionizing Traffic Sign Recognition: Unveiling the Potential of Vision Transformers
This research introduces an innovative method for Traffic Sign Recognition (TSR) by leveraging deep learning techniques, with a particular emphasis on Vision Transformers. TSR holds a vital role in advancing driver assis…
Autonomous VehiclesEvolutionary AlgorithmsTraffic Sign RecognitionA Multi-objective Optimization Benchmark Test Suite for Real-time Semantic Segmentation
As one of the emerging challenges in Automated Machine Learning, the Hardware-aware Neural Architecture Search (HW-NAS) tasks can be treated as black-box multi-objective optimization problems (MOPs). An important applica…
Autonomous DrivingEvolutionary AlgorithmsHardware Aware Neural Architecture SearchNeural Architecture Search+3LVNS-RAVE: Diversified audio generation with RAVE and Latent Vector Novelty Search
Evolutionary Algorithms and Generative Deep Learning have been two of the most powerful tools for sound generation tasks. However, they have limitations: Evolutionary Algorithms require complicated designs, posing challe…
Audio GenerationDeep LearningEvolutionary AlgorithmsA Survey of Decomposition-Based Evolutionary Multi-Objective Optimization: Part II -- A Data Science Perspective
This paper presents the second part of the two-part survey series on decomposition-based evolutionary multi-objective optimization where we mainly focus on discussing the literature related to multi-objective evolutionar…
AnatomyDescriptiveEvolutionary AlgorithmsNear-Tight Runtime Guarantees for Many-Objective Evolutionary Algorithms
Despite significant progress in the field of mathematical runtime analysis of multi-objective evolutionary algorithms (MOEAs), the performance of MOEAs on discrete many-objective problems is little understood. In particu…
Evolutionary AlgorithmsSelf-Adjusting Evolutionary Algorithms Are Slow on Multimodal Landscapes
The one-fifth rule and its generalizations are a classical parameter control mechanism in discrete domains. They have also been transferred to control the offspring population size of the $(1, \lambda)$-EA. This has been…
Evolutionary AlgorithmsRuntime Analysis of Evolutionary Diversity Optimization on the Multi-objective (LeadingOnes, TrailingZeros) Problem
The diversity optimization is the class of optimization problems, in which we aim at finding a diverse set of good solutions. One of the frequently used approaches to solve such problems is to use evolutionary algorithms…
DiversityEvolutionary Algorithms