Papers Evolutionary Algorithms
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A multiplicity-preserving crossover operator on graphs. Extended version
Evolutionary algorithms usually explore a search space of solutions by means of crossover and mutation. While a mutation consists of a small, local modification of a solution, crossover mixes the genetic information of t…
Evolutionary AlgorithmsRuntime Analysis of the (1+1) EA on Weighted Sums of Transformed Linear Functions
Linear functions play a key role in the runtime analysis of evolutionary algorithms and studies have provided a wide range of new insights and techniques for analyzing evolutionary computation methods. Motivated by studi…
Evolutionary AlgorithmsEvolutionary bagging for ensemble learning
Ensemble learning has gained success in machine learning with major advantages over other learning methods. Bagging is a prominent ensemble learning method that creates subgroups of data, known as bags, that are trained …
BIG-bench Machine LearningDiversityEnsemble LearningEvolutionary AlgorithmsA New Kind of Adversarial Example
Almost all adversarial attacks are formulated to add an imperceptible perturbation to an image in order to fool a model. Here, we consider the opposite which is adversarial examples that can fool a human but not a model.…
Evolutionary AlgorithmsDesigning Deep Convolutional Neural Networks using a Genetic Algorithm for Image-based Malware Classification
In recent years, deep Convolutional Neural Networks (CNNs) have shown great potential in malware classification. CNNs, which are originally designed for image processing, identify malware binaries visualised as images. D…
Evolutionary AlgorithmsMalware ClassificationAssessing Ranking and Effectiveness of Evolutionary Algorithm Hyperparameters Using Global Sensitivity Analysis Methodologies
We present a comprehensive global sensitivity analysis of two single-objective and two multi-objective state-of-the-art global optimization evolutionary algorithms as an algorithm configuration problem. That is, we inves…
Evolutionary Algorithmsglobal-optimizationSensitivityMultimodal Multi-objective Optimization: Comparative Study of the State-of-the-Art
Multimodal multi-objective problems (MMOPs) commonly arise in real-world problems where distant solutions in decision space correspond to very similar objective values. To obtain all solutions for MMOPs, many multimodal …
DiversityEvolutionary AlgorithmsQuantum compression with classically simulatable circuits
As we continue to find applications where the currently available noisy devices exhibit an advantage over their classical counterparts, the efficient use of quantum resources is highly desirable. The notion of quantum au…
Evolutionary AlgorithmsRuntime Analysis 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 AlgorithmsRuntime Analysis of Competitive co-Evolutionary Algorithms for Maximin Optimisation of a Bilinear Function
Co-evolutionary algorithms have a wide range of applications, such as in hardware design, evolution of strategies for board games, and patching software bugs. However, these algorithms are poorly understood and applicati…
Board GamesEvolutionary AlgorithmsCentralized and Decentralized Control in Modular Robots and Their Effect on Morphology
In Evolutionary Robotics, evolutionary algorithms are used to co-optimize morphology and control. However, co-optimizing leads to different challenges: How do you optimize a controller for a body that often changes its n…
Evolutionary AlgorithmsEmergence of Novelty in Evolutionary Algorithms
One of the main problems of evolutionary algorithms is the convergence of the population to local minima. In this paper, we explore techniques that can avoid this problem by encouraging a diverse behavior of the agents t…
Atari GamesEvolutionary AlgorithmsA Survey on Learnable Evolutionary Algorithms for Scalable Multiobjective Optimization
Recent decades have witnessed great advancements in multiobjective evolutionary algorithms (MOEAs) for multiobjective optimization problems (MOPs). However, these progressively improved MOEAs have not necessarily been eq…
Evolutionary AlgorithmsMultiobjective OptimizationSurveyEvolutionary Time-Use Optimization for Improving Children's Health Outcomes
How someone allocates their time is important to their health and well-being. In this paper, we show how evolutionary algorithms can be used to promote health and well-being by optimizing time usage. Based on data from a…
Evolutionary AlgorithmsQuantum-Enhanced Selection Operators for Evolutionary Algorithms
Genetic algorithms have unique properties which are useful when applied to black box optimization. Using selection, crossover, and mutation operators, candidate solutions may be obtained without the need to calculate a g…
Evolutionary AlgorithmsMulti-Objective Hyperparameter Optimization in Machine Learning -- An Overview
Hyperparameter optimization constitutes a large part of typical modern machine learning workflows. This arises from the fact that machine learning methods and corresponding preprocessing steps often only yield optimal pe…
Bayesian OptimizationBIG-bench Machine LearningEvolutionary AlgorithmsFairness+1Automated Circuit Sizing with Multi-objective Optimization based on Differential Evolution and Bayesian Inference
With the ever increasing complexity of specifications, manual sizing for analog circuits recently became very challenging. Especially for innovative, large-scale circuits designs, with tens of design variables, operating…
Bayesian InferenceBayesian OptimizationDiversityEvolutionary Algorithms+1Bandit Theory and Thompson Sampling-Guided Directed Evolution for Sequence Optimization
Directed Evolution (DE), a landmark wet-lab method originated in 1960s, enables discovery of novel protein designs via evolving a population of candidate sequences. Recent advances in biotechnology has made it possible t…
BIG-bench Machine LearningEvolutionary AlgorithmsLearning TheoryThompson SamplingAn Effective and Efficient Evolutionary Algorithm for Many-Objective Optimization
In evolutionary multiobjective optimization, effectiveness refers to how an evolutionary algorithm performs in terms of converging its solutions into the Pareto front and also diversifying them over the front. This is no…
Density EstimationEvolutionary AlgorithmsMultiobjective OptimizationCycle Mutation: Evolving Permutations via Cycle Induction
Evolutionary algorithms solve problems by simulating the evolution of a population of candidate solutions. We focus on evolving permutations for ordering problems like the traveling salesperson problem (TSP), as well as …
Evolutionary Algorithms