A Self-Adaptive Mutation Neural Architecture Search Algorithm Based on Blocks

2021 ◽  
Vol 16 (3) ◽  
pp. 67-78
Author(s):  
Yu Xue ◽  
Yankang Wang ◽  
Jiayu Liang ◽  
Adam Slowik
Keyword(s):  
Search Algorithm ◽  
Adaptive Mutation ◽  
Neural Architecture ◽  
Self Adaptive

scholarly journals Research on the inverse kinematics of manipulator using an improved self-adaptive mutation differential evolution algorithm

2021 ◽  
Vol 18 (3) ◽  
pp. 172988142110144
Author(s):  
Qianqian Zhang ◽  
Daqing Wang ◽  
Lifu Gao
Keyword(s):  
Differential Evolution ◽  
Objective Function ◽  
Inverse Kinematics ◽  
Differential Evolution Algorithm ◽  
Weight Coefficient ◽  
Adaptive Mutation ◽  
Feasible Region ◽  
Serial Manipulators ◽  
De Algorithm ◽  
Self Adaptive

To assess the inverse kinematics (IK) of multiple degree-of-freedom (DOF) serial manipulators, this article proposes a method for solving the IK of manipulators using an improved self-adaptive mutation differential evolution (DE) algorithm. First, based on the self-adaptive DE algorithm, a new adaptive mutation operator and adaptive scaling factor are proposed to change the control parameters and differential strategy of the DE algorithm. Then, an error-related weight coefficient of the objective function is proposed to balance the weight of the position error and orientation error in the objective function. Finally, the proposed method is verified by the benchmark function, the 6-DOF and 7-DOF serial manipulator model. Experimental results show that the improvement of the algorithm and improved objective function can significantly improve the accuracy of the IK. For the specified points and random points in the feasible region, the proportion of accuracy meeting the specified requirements is increased by 22.5% and 28.7%, respectively.

SELF-ADAPTIVE LEARNING CLASSIFIER SYSTEM

2010 ◽  
Vol 19 (01) ◽  
pp. 275-296 ◽  
Author(s):  
OLGIERD UNOLD
Keyword(s):  
Mutation Rate ◽  
Adaptive Learning ◽  
Adaptive Mutation ◽  
Learning Classifier System ◽  
Classifier System ◽  
Learning Classifier ◽  
Adaptive Parameters ◽  
Proposed Model ◽  
Fixed Parameter ◽  
Self Adaptive

This article introduces a new kind of self-adaptation in discovery mechanism of learning classifier system XCS. Unlike the previous approaches, which incorporate self-adaptive parameters in the representation of an individual, proposed model evolves competitive population of the reduced XCSs, which are able to adapt both classifiers and genetic parameters. The experimental comparisons of self-adaptive mutation rate XCS and standard XCS interacting with 11-bit, 20-bit, and 37-bit multiplexer environment were provided. It has been shown that adapting the mutation rate can give an equivalent or better performance to known good fixed parameter settings, especially for computationally complex tasks. Moreover, the self-adaptive XCS is able to solve the problem of inappropriate for a standard XCS parameters.

Semi-self-adaptive harmony search algorithm

2017 ◽  
Vol 16 (4) ◽  
pp. 619-636 ◽  
Author(s):  
Xinchao Zhao ◽  
Zhaohua Liu ◽  
Junling Hao ◽  
Rui Li ◽  
Xingquan Zuo
Keyword(s):  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Self Adaptive

scholarly journals HARNAS: Human Activity Recognition Based on Automatic Neural Architecture Search Using Evolutionary Algorithms

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6927
Author(s):  
Xiaojuan Wang ◽  
Xinlei Wang ◽  
Tianqi Lv ◽  
Lei Jin ◽  
Mingshu He
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Search Algorithm ◽  
Wearable Sensors ◽  
Research Direction ◽  
Human Activity Recognition ◽  
Nsga Ii ◽  
Neural Architecture ◽  
Access Cost ◽  
Computation Speed

Human activity recognition (HAR) based on wearable sensors is a promising research direction. The resources of handheld terminals and wearable devices limit the performance of recognition and require lightweight architectures. With the development of deep learning, the neural architecture search (NAS) has emerged in an attempt to minimize human intervention. We propose an approach for using NAS to search for models suitable for HAR tasks, namely, HARNAS. The multi-objective search algorithm NSGA-II is used as the search strategy of HARNAS. To make a trade-off between the performance and computation speed of a model, the F1 score and the number of floating-point operations (FLOPs) are selected, resulting in a bi-objective problem. However, the computation speed of a model not only depends on the complexity, but is also related to the memory access cost (MAC). Therefore, we expand the bi-objective search to a tri-objective strategy. We use the Opportunity dataset as the basis for most experiments and also evaluate the portability of the model on the UniMiB-SHAR dataset. The experimental results show that HARNAS designed without manual adjustments can achieve better performance than the best model tweaked by humans. HARNAS obtained an F1 score of 92.16% and parameters of 0.32 MB on the Opportunity dataset.

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