Papers Evolutionary Algorithms
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Fast and scalable neuroevolution deep learning architecture search for multivariate anomaly detection
Neuroevolution is one of the methodologies that can be used for learning optimal architecture during training. It uses evolutionary algorithms to generate the topology of artificial neural networks and its parameters. Th…
Anomaly DetectionEvolutionary AlgorithmsHybrid Self-Attention NEAT: A novel evolutionary approach to improve the NEAT algorithm
This article presents a "Hybrid Self-Attention NEAT" method to improve the original NeuroEvolution of Augmenting Topologies (NEAT) algorithm in high-dimensional inputs. Although the NEAT algorithm has shown a significant…
Atari GamesEvolutionary AlgorithmsMulti-Task Learning on Networks
The multi-task learning (MTL) paradigm can be traced back to an early paper of Caruana (1997) in which it was argued that data from multiple tasks can be used with the aim to obtain a better performance over learning eac…
Bayesian OptimizationEvolutionary AlgorithmsMulti-Task LearningOn the Difficulty of Evolving Permutation Codes
Combinatorial designs provide an interesting source of optimization problems. Among them, permutation codes are particularly interesting given their applications in powerline communications, flash memories, and block cip…
Evolutionary AlgorithmsSatNet: A Benchmark for Satellite Scheduling Optimization
Satellites provide essential services such as networking and weather tracking, and the number of satellites are expected to grow rapidly in the coming years. Communications with terrestrial ground stations is one of the …
Combinatorial OptimizationEvolutionary AlgorithmsReinforcement Learning (RL)SchedulingQuality and Computation Time in Optimization Problems
Optimization problems are crucial in artificial intelligence. Optimization algorithms are generally used to adjust the performance of artificial intelligence models to minimize the error of mapping inputs to outputs. Cur…
Bayesian OptimizationEvolutionary AlgorithmsEvoLearner: Learning Description Logics with Evolutionary Algorithms
Classifying nodes in knowledge graphs is an important task, e.g., for predicting missing types of entities, predicting which molecules cause cancer, or predicting which drugs are promising treatment candidates. While bla…
BenchmarkingEvolutionary AlgorithmsKnowledge GraphsA novel multiobjective evolutionary algorithm based on decomposition and multi-reference points strategy
Many real-world optimization problems such as engineering design can be eventually modeled as the corresponding multiobjective optimization problems (MOPs) which must be solved to obtain approximate Pareto optimal fronts…
DiversityEvolutionary AlgorithmsMultiobjective OptimizationDebiasing Credit Scoring using Evolutionary Algorithms
This paper investigates the application of machine learning when training a credit decision model over real, publicly available data whilst accounting for "bias objectives". We use the term "bias objective" to describe t…
Decision MakingEvolutionary AlgorithmsResult Diversification by Multi-objective Evolutionary Algorithms with Theoretical Guarantees
Given a ground set of items, the result diversification problem aims to select a subset with high "quality" and "diversity" while satisfying some constraints. It arises in various real-world artificial intelligence appli…
DiversityDocument SummarizationEvolutionary Algorithmsfeature selection+1Improving the sample-efficiency of neural architecture search with reinforcement learning
Designing complex architectures has been an essential cogwheel in the revolution deep learning has brought about in the past decade. When solving difficult problems in a datadriven manner, a well-tried approach is to tak…
AutoMLDeep LearningEvolutionary AlgorithmsNeural Architecture Search+2Across-Task Neural Architecture Search via Meta Learning
Adequate labeled data and expensive compute resources are the prerequisites for the success of neural architecture search(NAS). It is challenging to apply NAS in meta-learning scenarios with limited compute resources and…
Evolutionary AlgorithmsGPUMeta-LearningNeural Architecture SearchTowards Explainable Real Estate Valuation via Evolutionary Algorithms
Human lives are increasingly influenced by algorithms, which therefore need to meet higher standards not only in accuracy but also with respect to explainability. This is especially true for high-stakes areas such as rea…
Decision MakingEvolutionary AlgorithmsTime Complexity Analysis of Evolutionary Algorithms for 2-Hop (1,2)-Minimum Spanning Tree Problem
The Minimum Spanning Tree problem (abbr. MSTP) is a well-known combinatorial optimization problem that has been extensively studied by the researchers in the field of evolutionary computing to theoretically analyze the o…
Combinatorial OptimizationEvolutionary AlgorithmsEvolving Evolutionary Algorithms with Patterns
A new model for evolving Evolutionary Algorithms (EAs) is proposed in this paper. The model is based on the Multi Expression Programming (MEP) technique. Each MEP chromosome encodes an evolutionary pattern that is repeat…
BenchmarkingEvolutionary AlgorithmsSelf-adaptive Multi-task Particle Swarm Optimization
Multi-task optimization (MTO) studies how to simultaneously solve multiple optimization problems for the purpose of obtaining better performance on each problem. Over the past few years, evolutionary MTO (EMTO) was propo…
Evolutionary AlgorithmsTransfer LearningA guided journey through non-interactive automatic story generation
We present a literature survey on non-interactive computational story generation. The article starts with the presentation of requirements for creative systems, three types of models of creativity (computational, socio-c…
Evolutionary AlgorithmsStory GenerationEvolutionary Algorithms for Solving Unconstrained, Constrained and Multi-objective Noisy Combinatorial Optimisation Problems
We present an empirical study of a range of evolutionary algorithms applied to various noisy combinatorial optimisation problems. There are three sets of experiments. The first looks at several toy problems, such as OneM…
Evolutionary AlgorithmsTransfer Learning Based Multi-Objective Genetic Algorithm for Dynamic Community Detection
Dynamic community detection is the hotspot and basic problem of complex network and artificial intelligence research in recent years. It is necessary to maximize the accuracy of clustering as the network structure change…
ClusteringCommunity DetectionDynamic Community DetectionEvolutionary Algorithms+1Influence of Binomial Crossover on Approximation Error of Evolutionary Algorithms
Although differential evolution (DE) algorithms perform well on a large variety of complicated optimization problems, only a few theoretical studies are focused on the working principle of DE algorithms. To make the firs…
Evolutionary Algorithms