All Outputs (100)

Deep Reinforcement Learning Assisted Genetic Programming Ensemble Hyper-Heuristics for Dynamic Scheduling of Container Port Trucks (2024)
Journal Article

Efficient truck dispatching is crucial for optimizing container terminal operations within dynamic and complex scenarios. Despite good progress being made recently with more advanced uncertainty-handling techniques, existing approaches still have gen... Read More about Deep Reinforcement Learning Assisted Genetic Programming Ensemble Hyper-Heuristics for Dynamic Scheduling of Container Port Trucks.

DTCM: Deep Transformer Capsule Mutual Distillation for Multivariate Time Series Classification (2024)
Journal Article

This paper proposes a dual-network-based feature extractor, perceptive capsule network (PCapN), for multivariate time series classification (MTSC), including a local feature network (LFN) and a global relation network (GRN). The LFN has two heads (i.... Read More about DTCM: Deep Transformer Capsule Mutual Distillation for Multivariate Time Series Classification.

A pattern-based algorithm with fuzzy logic bin selector for online bin packing problem (2024)
Journal Article

The online bin packing problem is a well-known optimization challenge that finds application in a wide range of real-world scenarios. In the paper, we propose a novel algorithm called FuzzyPatternPack(FPP), which leverages fuzzy inference and pattern... Read More about A pattern-based algorithm with fuzzy logic bin selector for online bin packing problem.

Self-Bidirectional Decoupled Distillation for Time Series Classification (2024)
Journal Article

Over the years, many deep learning algorithms have been developed for time series classification (TSC). A learning model’s performance usually depends on the quality of the semantic information extracted from lower and higher levels within the repres... Read More about Self-Bidirectional Decoupled Distillation for Time Series Classification.

Discrete-Time Survival Models with Neural Networks for Age–Period–Cohort Analysis of Credit Risk (2024)
Journal Article

Survival models have become popular for credit risk estimation. Most current credit risk survival models use an underlying linear model. This is beneficial in terms of interpretability but is restrictive for real-life applications since it cannot dis... Read More about Discrete-Time Survival Models with Neural Networks for Age–Period–Cohort Analysis of Credit Risk.

Densely Knowledge-Aware Network for Multivariate Time Series Classification (2024)
Journal Article

Multivariate time series classification (MTSC) based on deep learning (DL) has attracted increasingly more research attention. The performance of a DL-based MTSC algorithm is heavily dependent on the quality of the learned representations providing s... Read More about Densely Knowledge-Aware Network for Multivariate Time Series Classification.

CapMatch: Semi-Supervised Contrastive Transformer Capsule With Feature-Based Knowledge Distillation for Human Activity Recognition (2023)
Journal Article

This article proposes a semi-supervised contrastive capsule transformer method with feature-based knowledge distillation (KD) that simplifies the existing semisupervised learning (SSL) techniques for wearable human activity recognition (HAR), called... Read More about CapMatch: Semi-Supervised Contrastive Transformer Capsule With Feature-Based Knowledge Distillation for Human Activity Recognition.

Automated design of local search algorithms: Predicting algorithmic components with LSTM (2023)
Journal Article

With a recently defined AutoGCOP framework, the design of local search algorithms has been defined as the composition of elementary algorithmic components. The effective compositions of the best algorithms thus retain useful knowledge of effective al... Read More about Automated design of local search algorithms: Predicting algorithmic components with LSTM.

Automated design of search algorithms based on reinforcement learning (2023)
Journal Article

Automated algorithm design has attracted increasing research attention recently in the evolutionary computation community. The main design decisions include selection heuristics and evolution operators in the search algorithms. Most existing studies,... Read More about Automated design of search algorithms based on reinforcement learning.

Deep Contrastive Representation Learning With Self-Distillation (2023)
Journal Article

Recently, contrastive learning (CL) is a promising way of learning discriminative representations from time series data. In the representation hierarchy, semantic information extracted from lower levels is the basis of that captured from higher level... Read More about Deep Contrastive Representation Learning With Self-Distillation.

Guest Editorial Special Issue on Multiobjective Evolutionary Optimization in Machine Learning (2023)
Journal Article

We are very pleased to introduce this special issue on multiobjective evolutionary optimization for machine learning (MOML). Optimization is at the heart of many machine-learning techniques. However, there is still room to exploit optimization in mac... Read More about Guest Editorial Special Issue on Multiobjective Evolutionary Optimization in Machine Learning.

Sequential Rule Mining for Automated Design of Meta-heuristics (2023)
Conference Proceeding

With a recently defined AutoGCOP framework, the design of local search algorithms can be defined as the composition of the basic elementary algorithmic components. These compositions into the best algorithms thus retain useful knowledge of effective... Read More about Sequential Rule Mining for Automated Design of Meta-heuristics.

Models of Representation in Computational Intelligence [Guest Editorial] (2023)
Journal Article

Computational Intelligence (CI) provides a set of powerful tools to effectively tackle complex computational tasks: global optimization methods (e.g., evolutionary computation, swarm intelligence), machine learning (e.g., neural networks), fuzzy reas... Read More about Models of Representation in Computational Intelligence [Guest Editorial].

Automated algorithm design using proximal policy optimisation with identified features (2022)
Journal Article

Automated algorithm design is attracting considerable recent research attention in solving complex combinatorial optimisation problems, due to that most metaheuristics may be particularly effective at certain problems or certain instances of the same... Read More about Automated algorithm design using proximal policy optimisation with identified features.

Cooperative Double-Layer Genetic Programming Hyper-Heuristic for Online Container Terminal Truck Dispatching (2022)
Journal Article

In a marine container terminal, truck dispatching is a crucial problem that impacts on the operation efficiency of the whole port. Traditionally, this problem is formulated as an offline optimisation problem, whose solutions are, however, impractical... Read More about Cooperative Double-Layer Genetic Programming Hyper-Heuristic for Online Container Terminal Truck Dispatching.

An Efficient Federated Distillation Learning System for Multitask Time Series Classification (2022)
Journal Article

This paper proposes an efficient federated distillation learning system (EFDLS) for multi-task time series classification (TSC). EFDLS consists of a central server and multiple mobile users, where different users may run different TSC tasks. EFDLS ha... Read More about An Efficient Federated Distillation Learning System for Multitask Time Series Classification.

Automated Design of Metaheuristics Using Reinforcement Learning within a Novel General Search Framework (2022)
Journal Article

Metaheuristic algorithms have been investigated intensively to address highly complex combinatorial optimisation problems. However, most metaheuristic algorithms have been designed manually by researchers of different expertise without a consistent f... Read More about Automated Design of Metaheuristics Using Reinforcement Learning within a Novel General Search Framework.

An Improved Ant Colony Approach for the Competitive Traveling Salesmen Problem (2022)
Conference Proceeding

A competitive traveling salesmen problem is a variant of traveling salesman problem in that multiple agents compete with each other in visiting a number of cities. The agent who is the first one to visit a city will receive a reward. Each agent aims... Read More about An Improved Ant Colony Approach for the Competitive Traveling Salesmen Problem.

A Collaborative Learning Tracking Network for Remote Sensing Videos (2022)
Journal Article

With the increasing accessibility of remote sensing videos, remote sensing tracking is gradually becoming a hot issue. However, accurately detecting and tracking in complex remote sensing scenes is still a challenge. In this article, we propose a col... Read More about A Collaborative Learning Tracking Network for Remote Sensing Videos.

Adaptive Fuzzy Learning Superpixel Representation for PolSAR Image Classification (2021)
Journal Article

The increasing applications of polarimetric synthetic aperture radar (PolSAR) image classification demand for effective superpixels’ algorithms. Fuzzy superpixels’ algorithms reduce the misclassification rate by dividing pixels into superpixels, whic... Read More about Adaptive Fuzzy Learning Superpixel Representation for PolSAR Image Classification.