MIP neighborhood synthesis through semantic feature extraction and automatic algorithm configuration

2017 ◽  
Vol 83 ◽  
pp. 106-119 ◽  
Author(s):  
Tommaso Adamo ◽  
Gianpaolo Ghiani ◽  
Antonio Grieco ◽  
Emanuela Guerriero ◽  
Emanuele Manni
Keyword(s):  
Feature Extraction ◽  
Semantic Feature ◽  
Automatic Algorithm Configuration ◽  
Algorithm Configuration ◽  
Automatic Algorithm ◽  
Semantic Feature Extraction

Image Recognition Technology Based on Artificial Intelligence

2013 ◽  
Vol 347-350 ◽  
pp. 3537-3540
Author(s):  
Hai Yun Lin ◽  
Yu Jiao Wang ◽  
Jian Chun Cai
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Feature Extraction ◽  
Image Retrieval ◽  
Semantic Feature ◽  
Current Image ◽  
Semantic Feature Extraction

In respect of the classification of current image retrieval technology and the existing issues, the paper put forward a method designed for image semantic feature extraction based on artificial intelligence. The new method has solved the tough problem of image semantic feature extraction, by fusing fuzzy logic, genetic algorithm and artificial neural network altogether, which greatly improved the efficiency and accuracy of image retrieval.

scholarly journals MO-ParamILS: A Multi-objective Automatic Algorithm Configuration Framework

2016 ◽  
pp. 32-47 ◽  
Author(s):  
Aymeric Blot ◽  
Holger H. Hoos ◽  
Laetitia Jourdan ◽  
Marie-Éléonore Kessaci-Marmion ◽  
Heike Trautmann
Keyword(s):  
Multi Objective ◽  
Automatic Algorithm Configuration ◽  
Algorithm Configuration ◽  
Automatic Algorithm

scholarly journals ParamILS: An Automatic Algorithm Configuration Framework

2009 ◽  
Vol 36 ◽  
pp. 267-306 ◽  
Author(s):  
F. Hutter ◽  
H. H. Hoos ◽  
K. Leyton-Brown ◽  
T. Stuetzle
Keyword(s):  
Mixed Integer ◽  
Considerable Effort ◽  
Performance Improvements ◽  
Automatic Algorithm Configuration ◽  
Automated Algorithm ◽  
Algorithm Configuration ◽  
Consistent Performance ◽  
Problem Instances ◽  
Automatic Algorithm ◽  
Configuration Problem

The identification of performance-optimizing parameter settings is an important part of the development and application of algorithms. We describe an automatic framework for this algorithm configuration problem. More formally, we provide methods for optimizing a target algorithm’s performance on a given class of problem instances by varying a set of ordinal and/or categorical parameters. We review a family of local-search-based algorithm configuration procedures and present novel techniques for accelerating them by adaptively limiting the time spent for evaluating individual configurations. We describe the results of a comprehensive experimental evaluation of our methods, based on the configuration of prominent complete and incomplete algorithms for SAT. We also present what is, to our knowledge, the first published work on automatically configuring the CPLEX mixed integer programming solver. All the algorithms we considered had default parameter settings that were manually identified with considerable effort. Nevertheless, using our automated algorithm configuration procedures, we achieved substantial and consistent performance improvements.

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