Papers Evolutionary Algorithms
“Evolutionary Algorithms” 태그가 달린 논문 1,107편 · 필터 해제
Evolution as a Service: A Privacy-Preserving Genetic Algorithm for Combinatorial Optimization
Evolutionary algorithms (EAs), such as the genetic algorithm (GA), offer an elegant way to handle combinatorial optimization problems (COPs). However, limited by expertise and resources, most users do not have enough cap…
Combinatorial OptimizationEvolutionary AlgorithmsPrivacy PreservingTraveling Salesman ProblemComparing the Digital Annealer with Classical Evolutionary Algorithm
In more recent years, there has been increasing research interest in exploiting the use of application specific hardware for solving optimisation problems. Examples of solvers that use specialised hardware are IBM's Quan…
Evolutionary AlgorithmsBalancing Exploration and Exploitation for Solving Large-scale Multiobjective Optimization via Attention Mechanism
Large-scale multiobjective optimization problems (LSMOPs) refer to optimization problems with multiple conflicting optimization objectives and hundreds or even thousands of decision variables. A key point in solving LSMO…
Evolutionary AlgorithmsMultiobjective OptimizationA Hardware-Aware Framework for Accelerating Neural Architecture Search Across Modalities
Recent advances in Neural Architecture Search (NAS) such as one-shot NAS offer the ability to extract specialized hardware-aware sub-network configurations from a task-specific super-network. While considerable effort ha…
Evolutionary Algorithmsimage-classificationImage ClassificationMachine Translation+1Evolutionary optimization of the Verlet closure relation for the hard-sphere and square-well fluids
The Ornstein-Zernike equation is solved for the hard-sphere and square-well fluids using a diverse selection of closure relations; the attraction range of the square-well is chosen to be $\lambda=1.5.$ In particular, for…
Evolutionary AlgorithmsRelationNeuroevolutionary Multi-objective approaches to Trajectory Prediction in Autonomous Vehicles
The incentive for using Evolutionary Algorithms (EAs) for the automated optimization and training of deep neural networks (DNNs), a process referred to as neuroevolution, has gained momentum in recent years. The configur…
Autonomous VehiclesEvolutionary AlgorithmsTrajectory PredictionSeeding Diversity into AI Art
This paper argues that generative art driven by conformance to a visual and/or semantic corpus lacks the necessary criteria to be considered creative. Among several issues identified in the literature, we focus on the fa…
DiversityEvolutionary AlgorithmsImage GenerationCombining Lipschitz and RBF Surrogate Models for High-dimensional Computationally Expensive Problems
Standard evolutionary optimization algorithms assume that the evaluation of the objective and constraint functions is straightforward and computationally cheap. However, in many real-world optimization problems, these ev…
Evolutionary AlgorithmsA suite of diagnostic metrics for characterizing selection schemes
Benchmark suites are crucial for assessing the performance of evolutionary algorithms, but the constituent problems are often too complex to provide clear intuition about an algorithm's strengths and weaknesses. To addre…
DiagnosticEvolutionary AlgorithmsRun Time Analysis for Random Local Search on Generalized Majority Functions
Run time analysis of evolutionary algorithms recently makes significant progress in linking algorithm performance to algorithm parameters. However, settings that study the impact of problem parameters are rare. The recen…
Evolutionary AlgorithmsEvolutionary Multi-Armed Bandits with Genetic Thompson Sampling
As two popular schools of machine learning, online learning and evolutionary computations have become two important driving forces behind real-world decision making engines for applications in biomedicine, economics, and…
Decision MakingEvolutionary AlgorithmsMulti-Armed BanditsSequential Decision Making+1Reference Vector Adaptation and Mating Selection Strategy via Adaptive Resonance Theory-based Clustering for Many-objective Optimization
Decomposition-based multiobjective evolutionary algorithms (MOEAs) with clustering-based reference vector adaptation show good optimization performance for many-objective optimization problems (MaOPs). Especially, algori…
ClusteringEvolutionary AlgorithmsAnalyzing the Impact of Undersampling on the Benchmarking and Configuration of Evolutionary Algorithms
The stochastic nature of iterative optimization heuristics leads to inherently noisy performance measurements. Since these measurements are often gathered once and then used repeatedly, the number of collected samples wi…
BenchmarkingEvolutionary AlgorithmsTowards a Stronger Theory for Permutation-based Evolutionary Algorithms
While the theoretical analysis of evolutionary algorithms (EAs) has made significant progress for pseudo-Boolean optimization problems in the last 25 years, only sporadic theoretical results exist on how EAs solve permut…
Evolutionary AlgorithmsHigh-performance Evolutionary Algorithms for Online Neuron Control
Recently, optimization has become an emerging tool for neuroscientists to study neural code. In the visual system, neurons respond to images with graded and noisy responses. Image patterns eliciting highest responses are…
DiagnosticEvolutionary AlgorithmsVocal Bursts Intensity PredictionA collaborative decomposition-based evolutionary algorithm integrating normal and penalty-based boundary intersection for many-objective optimization
Decomposition-based evolutionary algorithms have become fairly popular for many-objective optimization in recent years. However, the existing decomposition methods still are quite sensitive to the various shapes of front…
DiversityEvolutionary AlgorithmsHard Problems are Easier for Success-based Parameter Control
Recent works showed that simple success-based rules for self-adjusting parameters in evolutionary algorithms (EAs) can match or outperform the best fixed parameters on discrete problems. Non-elitism in a (1,$\lambda$) EA…
Evolutionary AlgorithmsNeural Network Pruning by Cooperative Coevolution
Neural network pruning is a popular model compression method which can significantly reduce the computing cost with negligible loss of accuracy. Recently, filters are often pruned directly by designing proper criteria or…
Evolutionary AlgorithmsModel CompressionNetwork PruningEvolutionary Algorithms for Limiting the Effect of Uncertainty for the Knapsack Problem with Stochastic Profits
Evolutionary algorithms have been widely used for a range of stochastic optimization problems in order to address complex real-world optimization problems. We consider the knapsack problem where the profits involve uncer…
Evolutionary AlgorithmsStochastic OptimizationEffective Mutation Rate Adaptation through Group Elite Selection
Evolutionary algorithms are sensitive to the mutation rate (MR); no single value of this parameter works well across domains. Self-adaptive MR approaches have been proposed but they tend to be brittle: Sometimes they dec…
Evolutionary Algorithmsimage-classificationImage Classification