Papers Evolutionary Algorithms
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Reachability Analysis for Lexicase Selection via Community Assembly Graphs
Fitness landscapes have historically been a powerful tool for analyzing the search space explored by evolutionary algorithms. In particular, they facilitate understanding how easily reachable an optimal solution is from …
Evolutionary AlgorithmsEvoPrompt: Connecting LLMs with Evolutionary Algorithms Yields Powerful Prompt Optimizers
Large Language Models (LLMs) excel in various tasks, but they rely on carefully crafted prompts that often demand substantial human effort. To automate this process, in this paper, we propose a novel framework for discre…
Evolutionary AlgorithmsPOCKET: Pruning Random Convolution Kernels for Time Series Classification from a Feature Selection Perspective
In recent years, two competitive time series classification models, namely, ROCKET and MINIROCKET, have garnered considerable attention due to their low training cost and high accuracy. However, they rely on a large numb…
Evolutionary Algorithmsfeature selectionTime SeriesTime Series ClassificationUsing Evolutionary Algorithms to Find Cache-Friendly Generalized Morton Layouts for Arrays
The layout of multi-dimensional data can have a significant impact on the efficacy of hardware caches and, by extension, the performance of applications. Common multi-dimensional layouts include the canonical row-major a…
Evolutionary AlgorithmsDrift Analysis with Fitness Levels for Elitist Evolutionary Algorithms
The fitness level method is a popular tool for analyzing the hitting time of elitist evolutionary algorithms. Its idea is to divide the search space into multiple fitness levels and estimate lower and upper bounds on the…
Evolutionary AlgorithmsReinforcement Learning-assisted Evolutionary Algorithm: A Survey and Research Opportunities
Evolutionary algorithms (EA), a class of stochastic search methods based on the principles of natural evolution, have received widespread acclaim for their exceptional performance in various real-world optimization probl…
AttributeEvolutionary Algorithmsreinforcement-learningReinforcement Learning+1FedPop: Federated Population-based Hyperparameter Tuning
Federated Learning (FL) is a distributed machine learning (ML) paradigm, in which multiple clients collaboratively train ML models without centralizing their local data. Similar to conventional ML pipelines, the client l…
Computational EfficiencyEvolutionary AlgorithmsFederated LearningFastAiAlloc: A real-time multi-resources allocation framework proposal based on predictive model and multiple optimization strategies
In cloud platforms, a common task is to allocate computational resources, e.g., memory and CPU, requested by applications and users. The allocation of these resources, which is an optimal load-balancing task, is consider…
Cloud ComputingCPUEvolutionary AlgorithmsSelf-Organized ClusteringAsynchronous Evolution of Deep Neural Network Architectures
Many evolutionary algorithms (EAs) take advantage of parallel evaluation of candidates. However, if evaluation times vary significantly, many worker nodes (i.e.,\ compute clients) are idle much of the time, waiting for t…
Evolutionary AlgorithmsImage CaptioningNeural Architecture SearchEvolutionary Multi-objective Optimisation in Neurotrajectory Prediction
Machine learning has rapidly evolved during the last decade, achieving expert human performance on notoriously challenging problems such as image classification. This success is partly due to the re-emergence of bio-insp…
Evolutionary Algorithmsimage-classificationImage ClassificationPrediction+2Cognitive Evolutionary Search to Select Feature Interactions for Click-Through Rate Prediction
Click-Through Rate (CTR) prediction of intelligent marketing systems is of great importance, in which feature interaction selection plays a key role. Most approaches model interactions of features by the same pre-defined…
Click-Through Rate PredictionEvolutionary AlgorithmsFeature Engineeringfeature selection+2Multiobjective Evolutionary Component Effect on Algorithm behavior
The performance of multiobjective evolutionary algorithms (MOEAs) varies across problems, making it hard to develop new algorithms or apply existing ones to new problems. To simplify the development and application of ne…
Evolutionary AlgorithmsImproved Solution Search Performance of Constrained MOEA/D Hybridizing Directional Mating and Local Mating
In this study, we propose an improvement to the direct mating method, a constraint handling approach for multi-objective evolutionary algorithms, by hybridizing it with local mating. Local mating selects another parent f…
DiversityEvolutionary AlgorithmsMany-objective Optimization via Voting for Elites
Real-world problems are often comprised of many objectives and require solutions that carefully trade-off between them. Current approaches to many-objective optimization often require challenging assumptions, like knowle…
Evolutionary AlgorithmsImproving Time and Memory Efficiency of Genetic Algorithms by Storing Populations as Minimum Spanning Trees of Patches
In many applications of evolutionary algorithms the computational cost of applying operators and storing populations is comparable to the cost of fitness evaluation. Furthermore, by knowing what exactly has changed in an…
Evolutionary AlgorithmsEvolutionary Strategy Guided Reinforcement Learning via MultiBuffer Communication
Evolutionary Algorithms and Deep Reinforcement Learning have both successfully solved control problems across a variety of domains. Recently, algorithms have been proposed which combine these two methods, aiming to lever…
Deep Reinforcement LearningEvolutionary AlgorithmsMuJoCoreinforcement-learning+1A Melting Pot of Evolution and Learning
We survey eight recent works by our group, involving the successful blending of evolutionary algorithms with machine learning and deep learning: 1. Binary and Multinomial Classification through Evolutionary Symbolic Regr…
ClassificationDeep LearningEvolutionary Algorithmsimage-classification+3Analysing the Robustness of NSGA-II under Noise
Runtime analysis has produced many results on the efficiency of simple evolutionary algorithms like the (1+1) EA, and its analogue called GSEMO in evolutionary multiobjective optimisation (EMO). Recently, the first runti…
Evolutionary AlgorithmsExploring the effects of robotic design on learning and neural control
The ongoing deep learning revolution has allowed computers to outclass humans in various games and perceive features imperceptible to humans during classification tasks. Current machine learning techniques have clearly d…
Computational EfficiencyEvolutionary AlgorithmsPhylogeny-informed fitness estimation
Phylogenies (ancestry trees) depict the evolutionary history of an evolving population. In evolutionary computing, a phylogeny can reveal how an evolutionary algorithm steers a population through a search space, illumina…
DiagnosticDiversityEvolutionary Algorithms