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Found 27 papers, 4 papers with code
Balancing Exploration and Exploitation for Solving Large-scale Multiobjective Optimization via Attention Mechanism
However, these algorithms ignore the significance of tackling this issue from the perspective of decision variables, which makes the algorithm lack the ability to search from different dimensions and limits the performance of the algorithm.
Benchmark Problems for CEC2021 Competition on Evolutionary Transfer Multiobjectve Optimization
Evolutionary transfer multiobjective optimization (ETMO) has been becoming a hot research topic in the field of evolutionary computation, which is based on the fact that knowledge learning and transfer across the related optimization exercises can improve the efficiency of others.
Solving Large-Scale Multi-Objective Optimization via Probabilistic Prediction Model
At the same time, due to the adoption of an individual-based evolution mechanism, the computational cost of the proposed method is independent of the number of decision variables, thus avoiding the problem of exponential growth of the search space.
Principled Design of Translation, Scale, and Rotation Invariant Variation Operators for Metaheuristics
In the past three decades, a large number of metaheuristics have been proposed and shown high performance in solving complex optimization problems.
Multi-Space Evolutionary Search for Large-Scale Optimization
This paper thus proposes a new search paradigm, namely the multi-space evolutionary search, to enhance the existing evolutionary search methods for solving large-scale optimization problems.
Concept drift-tolerant transfer learning in dynamic environments.
Then, a domain-wise weighted ensemble is introduced to combine the source and target models to select useful knowledge of each domain.
Manifold Interpolation for Large-Scale Multi-Objective Optimization via Generative Adversarial Networks
However, traditional evolutionary algorithms for solving LSMOPs have some deficiencies in dealing with this structural manifold, resulting in poor diversity, local optima, and inefficient searches.
Evolutionary Gait Transfer of Multi-Legged Robots in Complex Terrains
Given the high dimensions of control space, this problem is particularly challenging for multi-legged robots walking in complex and unknown environments.
A Survey on Evolutionary Neural Architecture Search
Deep Neural Networks (DNNs) have achieved great success in many applications.
Progressive Tandem Learning for Pattern Recognition with Deep Spiking Neural Networks
In this paper, we propose a novel ANN-to-SNN conversion and layer-wise learning framework for rapid and efficient pattern recognition, which is referred to as progressive tandem learning of deep SNNs.
Synaptic Learning with Augmented Spikes
Traditional neuron models use analog values for information representation and computation, while all-or-nothing spikes are employed in the spiking ones.
Constructing Accurate and Efficient Deep Spiking Neural Networks with Double-threshold and Augmented Schemes
We examine the performance of our methods based on MNIST, Fashion-MNIST and CIFAR10 datasets.
Towards Efficient Processing and Learning with Spikes: New Approaches for Multi-Spike Learning
They are also believed to play an essential role in low-power consumption of the biological systems, whose efficiency attracts increasing attentions to the field of neuromorphic computing.
Understanding the Automated Parameter Optimization on Transfer Learning for CPDP: An Empirical Study
Given a tight computational budget, it is more cost-effective to focus on optimizing the parameter configuration of transfer learning algorithms (3) The research on CPDP is far from mature where it is "not difficult" to find a better alternative by making a combination of existing transfer learning and classification techniques.
Deep Spiking Neural Networks for Large Vocabulary Automatic Speech Recognition
The brain-inspired spiking neural networks (SNN) closely mimic the biological neural networks and can operate on low-power neuromorphic hardware with spike-based computation.
Online Bagging for Anytime Transfer Learning
In practical applications, it is often necessary to face online learning problems in which the data samples are achieved sequentially.
Evolutionary Dynamic Multi-objective Optimization Via Regression Transfer Learning
In recent years, transfer learning has been proven to be a kind of effective approach in solving DMOPs.
Solving Dynamic Multi-objective Optimization Problems Using Incremental Support Vector Machine
The main feature of the Dynamic Multi-objective Optimization Problems (DMOPs) is that optimization objective functions will change with times or environments.
Solving dynamic multi-objective optimization problems via support vector machine
The algorithm uses the POS that has been obtained to train a SVM and then take the trained SVM to classify the solutions of the dynamic optimization problem at the next moment, and thus it is able to generate an initial population which consists of different individuals recognized by the trained SVM.
Evolutionary Multi-Objective Optimization Driven by Generative Adversarial Networks (GANs)
The proposed algorithm is tested on 10 benchmark problems with up to 200 decision variables.
Evolutionary Multi-Objective Optimization Driven by Generative Adversarial Networks
Recently, more and more works have proposed to drive evolutionary algorithms using machine learning models. Usually, the performance of such model based evolutionary algorithms is highly dependent on the training qualities of the adopted models. Since it usually requires a certain amount of data (i. e. the candidate solutions generated by the algorithms) for model training, the performance deteriorates rapidly with the increase of the problem scales, due to the curse of dimensionality. To address this issue, we propose a multi-objective evolutionary algorithm driven by the generative adversarial networks (GANs). At each generation of the proposed algorithm, the parent solutions are first classified into \emph{real} and \emph{fake} samples to train the GANs; then the offspring solutions are sampled by the trained GANs. Thanks to the powerful generative ability of the GANs, our proposed algorithm is capable of generating promising offspring solutions in high-dimensional decision space with limited training data. The proposed algorithm is tested on 10 benchmark problems with up to 200 decision variables. Experimental results on these test problems demonstrate the effectiveness of the proposed algorithm.
A Tandem Learning Rule for Effective Training and Rapid Inference of Deep Spiking Neural Networks
Spiking neural networks (SNNs) represent the most prominent biologically inspired computing model for neuromorphic computing (NC) architectures.
Robust Environmental Sound Recognition with Sparse Key-point Encoding and Efficient Multi-spike Learning
Our framework is a unifying system with a consistent integration of three major functional parts which are sparse encoding, efficient learning and robust readout.
Which Surrogate Works for Empirical Performance Modelling? A Case Study with Differential Evolution
Perhaps surprisingly, it is possible to build a cheap-to-evaluate surrogate that models the algorithm's empirical performance as a function of its parameters.
A Multi-State Diagnosis and Prognosis Framework with Feature Learning for Tool Condition Monitoring
For fault diagnosis, a cost-sensitive deep belief network (namely ECS-DBN) is applied to deal with the imbalanced data problem for tool state estimation.
A Cost-Sensitive Deep Belief Network for Imbalanced Classification
Adaptive differential evolution optimization is implemented as the optimization algorithm that automatically updates its corresponding parameters without the need of prior domain knowledge.
Evolutionary Multitasking for Multiobjective Continuous Optimization: Benchmark Problems, Performance Metrics and Baseline Results
In this report, we suggest nine test problems for multi-task multi-objective optimization (MTMOO), each of which consists of two multiobjective optimization tasks that need to be solved simultaneously.
