Proposal of Surrogate Model for Genetic Programming Based on Program Structure Similarity

2020 ◽  
Author(s):  
Sohei Kino ◽  
Tomohiro Harada ◽  
Ruck Thawonmas
Keyword(s):  
Genetic Programming ◽  
Surrogate Model ◽  
Program Structure ◽  
Structure Similarity

scholarly journals Genetic Programming with Image-Related Operators and A Flexible Program Structure for Feature Learning in Image Classification

2020 ◽  
Author(s):  
Ying Bi ◽  
Bing Xue ◽  
Mengjie Zhang
Keyword(s):  
Feature Extraction ◽  
Genetic Programming ◽  
Image Classification ◽  
Domain Knowledge ◽  
Extraction Methods ◽  
Classification Performance ◽  
Model Complexity ◽  
Program Structure ◽  
New Approach ◽  
Classification Tasks

IEEE Feature extraction is essential for solving image classification by transforming low-level pixel values into high-level features. However, extracting effective features from images is challenging due to high variations across images in scale, rotation, illumination, and background. Existing methods often have a fixed model complexity and require domain expertise. Genetic programming with a flexible representation can find the best solution without the use of domain knowledge. This paper proposes a new genetic programming-based approach to automatically learning informative features for different image classification tasks. In the new approach, a number of image-related operators, including filters, pooling operators and feature extraction methods, are employed as functions. A flexible program structure is developed to integrate different functions and terminals into a single tree/solution. The new approach can evolve solutions of variable depths to extract various numbers and types of features from the images. The new approach is examined on 12 different image classification tasks of varying difficulty and compared with a large number of effective algorithms. The results show that the new approach achieves better classification performance than most benchmark methods. The analysis of the evolved programs/solutions and the visualisation of the learned features provide deep insights on the proposed approach.

scholarly journals An Automatic Heuristic Design Approach for Seru Scheduling Problem with Resource Conflicts

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Rongxin Zhan ◽  
Jinhui Zhang ◽  
Zihua Cui ◽  
Jin Peng ◽  
Dongni Li
Keyword(s):  
Genetic Programming ◽  
High Efficiency ◽  
Production Systems ◽  
Design Approach ◽  
Programming Algorithm ◽  
Scheduling Problem ◽  
Quick Response ◽  
Resource Conflicts ◽  
Structure Similarity ◽  
High Flexibility

In current environments, production systems need the ability of quick response to face the volatile markets. Seru production systems, as a new mode of the production system, have the advantages of quick response, high flexibility, and high efficiency. Seru scheduling, which refers to constructing serus with an exact sequence in limited workspace, is an important decision problem in the operational management of seru production systems and can reflect the reconfiguration nature of seru production systems. This study investigates a seru scheduling problem with resource conflicts, whose objective is to minimize the makespan. An automatic heuristic design approach that combines a genetic programming algorithm and structure similarity-based operators is proposed. Comparative experiments are conducted with human-made rules, basic genetic programming, genetic programming-based algorithm, and with some state-of-the-art scheduling algorithms. The results show the effectiveness and efficiency of the proposed algorithm.

scholarly journals Genetic Programming with Image-Related Operators and A Flexible Program Structure for Feature Learning in Image Classification

2020 ◽  
Author(s):  
Ying Bi ◽  
Bing Xue ◽  
Mengjie Zhang
Keyword(s):  
Feature Extraction ◽  
Genetic Programming ◽  
Image Classification ◽  
Domain Knowledge ◽  
Extraction Methods ◽  
Classification Performance ◽  
Model Complexity ◽  
Program Structure ◽  
New Approach ◽  
Classification Tasks

IEEE Feature extraction is essential for solving image classification by transforming low-level pixel values into high-level features. However, extracting effective features from images is challenging due to high variations across images in scale, rotation, illumination, and background. Existing methods often have a fixed model complexity and require domain expertise. Genetic programming with a flexible representation can find the best solution without the use of domain knowledge. This paper proposes a new genetic programming-based approach to automatically learning informative features for different image classification tasks. In the new approach, a number of image-related operators, including filters, pooling operators and feature extraction methods, are employed as functions. A flexible program structure is developed to integrate different functions and terminals into a single tree/solution. The new approach can evolve solutions of variable depths to extract various numbers and types of features from the images. The new approach is examined on 12 different image classification tasks of varying difficulty and compared with a large number of effective algorithms. The results show that the new approach achieves better classification performance than most benchmark methods. The analysis of the evolved programs/solutions and the visualisation of the learned features provide deep insights on the proposed approach.

scholarly journals An Evolutionary Deep Learning Approach Using Genetic Programming with Convolution Operators for Image Classification

2021 ◽  
Author(s):  
Ying Bi ◽  
Bing Xue ◽  
Mengjie Zhang
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Genetic Programming ◽  
Image Classification ◽  
Feature Learning ◽  
Learning Approach ◽  
Data Sets ◽  
Convolution Operators ◽  
Program Structure ◽  
Terminal Set

© 2019 IEEE. Evolutionary deep learning (EDL) as a hot topic in recent years aims at using evolutionary computation (EC) techniques to address existing issues in deep learning. Most existing work focuses on employing EC methods for evolving hyper-parameters, deep structures or weights for neural networks (NNs). Genetic programming (GP) as an EC method is able to achieve deep learning due to the characteristics of its representation. However, many current GP-based EDL methods are limited to binary image classification. This paper proposed a new GP-based EDL method with convolution operators (COGP) for feature learning on binary and multi-class image classification. A novel flexible program structure is developed to allow COGP to evolve solutions with deep or shallow structures. Associated with the program structure, a new function set and a new terminal set are developed in COGP. The experimental results on six different image classification data sets of varying difficulty demonstrated that COGP achieved significantly better performance in most comparisons with 11 effectively competitive methods. The visualisation of the best program further revealed the high interpretability of the solutions found by COGP.

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