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Found 12 papers, 4 papers with code
An Interactive Knowledge-based Multi-objective Evolutionary Algorithm Framework for Practical Optimization Problems
User knowledge can be formulated as inter-variable relationships to assist an optimization algorithm in finding good solutions faster.
Enhanced Innovized Repair Operator for Evolutionary Multi- and Many-objective Optimization
"Innovization" is a task of learning common relationships among some or all of the Pareto-optimal (PO) solutions in multi- and many-objective optimization problems.
NSGANetV2: Evolutionary Multi-Objective Surrogate-Assisted Neural Architecture Search
In this paper, we propose an efficient NAS algorithm for generating task-specific models that are competitive under multiple competing objectives.
Ranked #17 on
Neural Architecture Search
on ImageNet
Neural Architecture Transfer
At the same time, the architecture search and transfer is orders of magnitude more efficient than existing NAS methods.
Ranked #1 on
Neural Architecture Search
on STL-10
Fine-Grained Image Classification
Neural Architecture Search
+1
It is Time for New Perspectives on How to Fight Bloat in GP
This new perspective allows us to understand that new methods for bloat control can be derived, and the first of such a method is described and tested.
Multi-Objective Evolutionary Design of Deep Convolutional Neural Networks for Image Classification
While existing approaches have achieved competitive performance in image classification, they are not well suited to problems where the computational budget is limited for two reasons: (1) the obtained architectures are either solely optimized for classification performance, or only for one deployment scenario; (2) the search process requires vast computational resources in most approaches.
Ranked #1 on
Pneumonia Detection
on ChestX-ray14
Embedding Push and Pull Search in the Framework of Differential Evolution for Solving Constrained Single-objective Optimization Problems
In the top sub-population, the search process is divided into two different stages --- push and pull stages. An adaptive DE variant with three trial vector generation strategies is employed in the proposed PPS-DE.
NSGA-Net: Neural Architecture Search using Multi-Objective Genetic Algorithm
This paper introduces NSGA-Net -- an evolutionary approach for neural architecture search (NAS).
NSGA-Net: A Multi-Objective Genetic Algorithm for Neural Architecture Search
This paper introduces NSGA-Net, an evolutionary approach for neural architecture search (NAS).
MOEA/D with Angle-based Constrained Dominance Principle for Constrained Multi-objective Optimization Problems
The experimental results manifest that MOEA/D-ACDP is significantly better than the other four CMOEAs on these test instances and the real-world case, which indicates that ACDP is more effective for solving CMOPs.
An Improved Epsilon Constraint-handling Method in MOEA/D for CMOPs with Large Infeasible Regions
In order to evaluate the performance of MOEA/D-IEpsilon, a new set of CMOPs with two and three objectives is designed, having large infeasible regions (relative to the feasible regions), and they are called LIR-CMOPs.
A New Repair Operator for Multi-objective Evolutionary Algorithm in Constrained Optimization Problems
In this paper, we design a set of multi-objective constrained optimization problems (MCOPs) and propose a new repair operator to address them.
