Papers Evolutionary Algorithms
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Crossover Can Guarantee Exponential Speed-Ups in Evolutionary Multi-Objective Optimisation
Evolutionary algorithms are popular algorithms for multiobjective optimisation (also called Pareto optimisation) as they use a population to store trade-offs between different objectives. Despite their popularity, the th…
Evolutionary AlgorithmsEnabling surrogate-assisted evolutionary reinforcement learning via policy embedding
Evolutionary Reinforcement Learning (ERL) that applying Evolutionary Algorithms (EAs) to optimize the weight parameters of Deep Neural Network (DNN) based policies has been widely regarded as an alternative to traditiona…
Atari GamesEvolutionary Algorithmsreinforcement-learningReinforcement Learning+1NASiam: Efficient Representation Learning using Neural Architecture Search for Siamese Networks
Siamese networks are one of the most trending methods to achieve self-supervised visual representation learning (SSL). Since hand labeling is costly, SSL can play a crucial part by allowing deep learning to train on larg…
Contrastive LearningEvolutionary AlgorithmsGPUimage-classification+3ATM-R: An Adaptive Tradeoff Model with Reference Points for Constrained Multiobjective Evolutionary Optimization
The goal of constrained multiobjective evolutionary optimization is to obtain a set of well-converged and welldistributed feasible solutions. To complete this goal, there should be a tradeoff among feasibility, diversity…
DiversityEvolutionary AlgorithmsMultiobjective OptimizationRuntime Performance of Evolutionary Algorithms for the Chance-constrained Makespan Scheduling Problem
The Makespan Scheduling problem is an extensively studied NP-hard problem, and its simplest version looks for an allocation approach for a set of jobs with deterministic processing times to two identical machines such th…
Evolutionary AlgorithmsSchedulingPredicting the Score of Atomic Candidate OWL Class Axioms
Candidate axiom scoring is the task of assessing the acceptability of a candidate axiom against the evidence provided by known facts or data. The ability to score candidate axioms reliably is required for automated schem…
Evolutionary AlgorithmsSemantic SimilaritySemantic Textual SimilarityNeuroevolution of Physics-Informed Neural Nets: Benchmark Problems and Comparative Results
The potential of learned models for fundamental scientific research and discovery is drawing increasing attention worldwide. Physics-informed neural networks (PINNs), where the loss function directly embeds governing equ…
Evolutionary AlgorithmsPyPop7: A Pure-Python Library for Population-Based Black-Box Optimization
In this paper, we present an open-source pure-Python library called PyPop7 for black-box optimization (BBO). As population-based methods (e.g., evolutionary algorithms, swarm intelligence, and pattern search) become incr…
BenchmarkingEvolutionary AlgorithmsMetric LearningThe Hybridization of Branch and Bound with Metaheuristics for Nonconvex Multiobjective Optimization
A hybrid framework combining the branch and bound method with multiobjective evolutionary algorithms is proposed for nonconvex multiobjective optimization. The hybridization exploits the complementary character of the tw…
Evolutionary AlgorithmsMultiobjective OptimizationCan Evolutionary Clustering Have Theoretical Guarantees?
Clustering is a fundamental problem in many areas, which aims to partition a given data set into groups based on some distance measure, such that the data points in the same group are similar while that in different grou…
ClusteringEvolutionary AlgorithmsFairnessCoevolutionary Framework for Generalized Multimodal Multi-objective Optimization
Most multimodal multi-objective evolutionary algorithms (MMEAs) aim to find all global Pareto optimal sets (PSs) for a multimodal multi-objective optimization problem (MMOP). However, in real-world problems, decision mak…
DiversityEvolutionary AlgorithmsTransfer LearningThe Effect of Epigenetic Blocking on Dynamic Multi-Objective Optimisation Problems
Hundreds of Evolutionary Computation approaches have been reported. From an evolutionary perspective they focus on two fundamental mechanisms: cultural inheritance in Swarm Intelligence and genetic inheritance in Evoluti…
BlockingEvolutionary AlgorithmsAssessing Quality-Diversity Neuro-Evolution Algorithms Performance in Hard Exploration Problems
A fascinating aspect of nature lies in its ability to produce a collection of organisms that are all high-performing in their niche. Quality-Diversity (QD) methods are evolutionary algorithms inspired by this observation…
DiversityEvolutionary AlgorithmsOn Narrative Information and the Distillation of Stories
The act of telling stories is a fundamental part of what it means to be human. This work introduces the concept of narrative information, which we define to be the overlap in information space between a story and the ite…
Contrastive LearningEvolutionary AlgorithmsEvolutionary Strategies for the Design of Binary Linear Codes
The design of binary error-correcting codes is a challenging optimization problem with several applications in telecommunications and storage, which has also been addressed with metaheuristic techniques and evolutionary …
DiversityEvolutionary AlgorithmsRe-visiting Reservoir Computing architectures optimized by Evolutionary Algorithms
For many years, Evolutionary Algorithms (EAs) have been applied to improve Neural Networks (NNs) architectures. They have been used for solving different problems, such as training the networks (adjusting the weights), d…
Evolutionary AlgorithmsMulti-objective optimization via evolutionary algorithm (MOVEA) for high-definition transcranial electrical stimulation of the human brain
Designing a transcranial electrical stimulation (TES) strategy requires considering multiple objectives, such as intensity in the target area, focality, stimulation depth, and avoidance zone, which are often mutually exc…
Evolutionary AlgorithmsCollaborative Multiobjective Evolutionary Algorithms in search of better Pareto Fronts. An application to trading systems
Technical indicators use graphic representations of data sets by applying various mathematical formulas to financial time series of prices. These formulas comprise a set of rules and parameters whose values are not neces…
Evolutionary AlgorithmsTime SeriesTime Series AnalysisLearning Adaptive Evolutionary Computation for Solving Multi-Objective Optimization Problems
Multi-objective evolutionary algorithms (MOEAs) are widely used to solve multi-objective optimization problems. The algorithms rely on setting appropriate parameters to find good solutions. However, this parameter tuning…
Combinatorial OptimizationDeep Reinforcement LearningEvolutionary AlgorithmsExploring the effectiveness of surrogate-assisted evolutionary algorithms on the batch processing problem
Real-world optimisation problems typically have objective functions which cannot be expressed analytically. These optimisation problems are evaluated through expensive physical experiments or simulations. Cheap approxima…
Evolutionary Algorithms