Papers Evolutionary Algorithms
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A Hardware-Aware System for Accelerating Deep Neural Network Optimization
Recent advances in Neural Architecture Search (NAS) which extract specialized hardware-aware configurations (a.k.a. "sub-networks") from a hardware-agnostic "super-network" have become increasingly popular. While conside…
Bayesian OptimizationEvolutionary AlgorithmsNeural Architecture SearchAccelerating Neural Architecture Exploration Across Modalities Using Genetic Algorithms
Neural architecture search (NAS), the study of automating the discovery of optimal deep neural network architectures for tasks in domains such as computer vision and natural language processing, has seen rapid growth in …
Evolutionary Algorithmsimage-classificationImage ClassificationMachine Translation+1Novel Metric based on Walsh Coefficients for measuring problem difficulty in Estimation of Distribution Algorithms
Estimation of distribution algorithms are evolutionary algorithms that use extracted information from the population instead of traditional genetic operators to generate new solutions. This information is represented as …
Evolutionary AlgorithmsSimple Genetic Operators are Universal Approximators of Probability Distributions (and other Advantages of Expressive Encodings)
This paper characterizes the inherent power of evolutionary algorithms. This power depends on the computational properties of the genetic encoding. With some encodings, two parents recombined with a simple crossover oper…
Evolutionary AlgorithmsEvolving Constructions for Balanced, Highly Nonlinear Boolean Functions
Finding balanced, highly nonlinear Boolean functions is a difficult problem where it is not known what nonlinearity values are possible to be reached in general. At the same time, evolutionary computation is successfully…
Evolutionary AlgorithmsModeling Strong Physically Unclonable Functions with Metaheuristics
Evolutionary algorithms have been successfully applied to attacking Physically Unclonable Functions (PUFs). CMA-ES is recognized as the most powerful option for a type of attack called the reliability attack. While there…
Evolutionary AlgorithmsInterpretable pipelines with evolutionarily optimized modules for RL tasks with visual inputs
The importance of explainability in AI has become a pressing concern, for which several explainable AI (XAI) approaches have been recently proposed. However, most of the available XAI techniques are post-hoc methods, whi…
Decision MakingEvolutionary AlgorithmsExplainable Artificial Intelligence (XAI)reinforcement-learning+2Theory-inspired Parameter Control Benchmarks for Dynamic Algorithm Configuration
It has long been observed that the performance of evolutionary algorithms and other randomized search heuristics can benefit from a non-static choice of the parameters that steer their optimization behavior. Mechanisms t…
BenchmarkingEvolutionary AlgorithmsHeed the Noise in Performance Evaluations in Neural Architecture Search
Neural Architecture Search (NAS) has recently become a topic of great interest. However, there is a potentially impactful issue within NAS that remains largely unrecognized: noise. Due to stochastic factors in neural net…
Combinatorial OptimizationEvolutionary AlgorithmsImage SegmentationMedical Image Segmentation+2COIL: Constrained Optimization in Learned Latent Space: Learning Representations for Valid Solutions
Constrained optimization problems can be difficult because their search spaces have properties not conducive to search, e.g., multimodality, discontinuities, or deception. To address such difficulties, considerable resea…
Evolutionary AlgorithmsvalidOptimizing LLVM Pass Sequences with Shackleton: A Linear Genetic Programming Framework
In this paper we introduce Shackleton as a generalized framework enabling the application of linear genetic programming -- a technique under the umbrella of evolutionary algorithms -- to a variety of use cases. We also e…
Evolutionary AlgorithmsZeroth-Order Actor-Critic: An Evolutionary Framework for Sequential Decision Problems
Evolutionary algorithms (EAs) have shown promise in solving sequential decision problems (SDPs) by simplifying them to static optimization problems and searching for the optimal policy parameters in a zeroth-order way. W…
continuous-controlContinuous ControlEvolutionary AlgorithmsReinforcement Learning (RL)Search Trajectories Networks of Multiobjective Evolutionary Algorithms
Understanding the search dynamics of multiobjective evolutionary algorithms (MOEAs) is still an open problem. This paper extends a recent network-based tool, search trajectory networks (STNs), to model the behavior of MO…
Evolutionary AlgorithmsDiversity Enhancement via Magnitude
Promoting and maintaining diversity of candidate solutions is a key requirement of evolutionary algorithms in general and multi-objective evolutionary algorithms in particular. In this paper, we use the recently develope…
DiversityEvolutionary AlgorithmsSurrogate-assisted distributed swarm optimisation for computationally expensive geoscientific models
Evolutionary algorithms provide gradient-free optimisation which is beneficial for models that have difficulty in obtaining gradients; for instance, geoscientific landscape evolution models. However, such models are at t…
Distributed ComputingEvolutionary AlgorithmsEvolutionary Action Selection for Gradient-based Policy Learning
Evolutionary Algorithms (EAs) and Deep Reinforcement Learning (DRL) have recently been integrated to take the advantage of the both methods for better exploration and exploitation.The evolutionary part in these hybrid me…
continuous-controlContinuous ControlDeep Reinforcement LearningEvolutionary AlgorithmsA Surrogate-Assisted Controller for Expensive Evolutionary Reinforcement Learning
The integration of Reinforcement Learning (RL) and Evolutionary Algorithms (EAs) aims at simultaneously exploiting the sample efficiency as well as the diversity and robustness of the two paradigms. Recently, hybrid lear…
continuous-controlContinuous ControlDiversityEvolutionary Algorithms+4Training Quantized Deep Neural Networks via Cooperative Coevolution
This work considers a challenging Deep Neural Network(DNN) quantization task that seeks to train quantized DNNs without involving any full-precision operations. Most previous quantization approaches are not applicable to…
Evolutionary AlgorithmsQuantizationOn the Evolution of the MCTS Upper Confidence Bounds for Trees by Means of Evolutionary Algorithms in the Game of Carcassonne
Monte Carlo Tree Search (MCTS) is a sampling best-first method to search for optimal decisions. The MCTS's popularity is based on its extraordinary results in the challenging two-player based game Go, a game considered m…
Evolutionary AlgorithmsTowards automated optimisation of residual convolutional neural networks for electrocardiogram classification
The interpretation of the electrocardiogram (ECG) gives clinical information and helps in assessing heart function. There are distinct ECG patterns associated with a specific class of arrythmia. The convolutional neural …
Bayesian OptimisationEvolutionary Algorithms