A hybrid approach to testing for nonfunctional faults in embedded systems using genetic algorithms

2018 ◽  
Vol 28 (7) ◽  
pp. e1686 ◽  
Author(s):  
Tingting Yu ◽  
Witawas Srisa-an ◽  
Myra B. Cohen ◽  
Gregg Rothermel
Keyword(s):  
Genetic Algorithms ◽  
Embedded Systems ◽  
Hybrid Approach

Genetic Algorithms

2014 ◽  
pp. 239-255 ◽  
Author(s):  
Pabitra Mohan Khilar
Keyword(s):  
Genetic Algorithms ◽  
Fault Diagnosis ◽  
Distributed Computing ◽  
Embedded Systems ◽  
Distributed Embedded Systems ◽  
Distributed Computing Systems ◽  
Computing Systems ◽  
Satellite Systems ◽  
Complete Problems ◽  
Diagnosis Algorithm

Genetic Algorithms are important techniques to solve many NP-Complete problems related to distributed computing and its application domains. Genetic algorithm-based fault diagnoses in distributed computing systems have been a feasible methodology to solve diagnosis problems recently. Distributed embedded systems consisting of sensors, actuators, processors/microcontrollers, and interconnection networks are one class of distributed computing systems that have long been used, staring from small-scale home appliances to large-scale satellite systems. Some of their applications are in safety-critical systems where occurrence of faults can result in catastrophic situations for which fault diagnosis in such systems are very important. In this chapter, different types of faults, which are likely to occur in distributed embedded systems and a GA-based methodology to solve these problems along with the performance analysis of fault diagnosis algorithm have been presented. Nevertheless, the diagnosis algorithm presented here is well suitable for general purpose distributed computing systems with appropriate modification over system and fault model. In fact, this book chapter will enable the reader not only to study various aspects of fault diagnosis techniques but will also provide insight to build robust systems to allow for continued normal service despite the occurrence of failures.

A Hybrid Approach for Shape Retrieval Using Genetic Algorithms and Approximate Distance

2021 ◽  
pp. 205-222
Author(s):  
Saliha Mezzoudj ◽  
Kamal Eddine Melkemi
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Hybrid Approach ◽  
Shape Context ◽  
Similar Shape ◽  
Complex Shapes ◽  
Ga Algorithm ◽  
Approximate Distance ◽  
Multi Agent ◽  
Part Structure

This article describes how the classical algorithm of shape context (SC) is still unable to capture the part structure of some complex shapes. To overcome this insufficiency, the authors propose a novel shape-based retrieval approach that is called HybMAS-GA using a multi-agent system (MAS) and a genetic algorithm (GA). They define a new distance called approximate distance (AD) to define a SC method by AD, which called approximate distance shape context (ADSC) descriptor. Furthermore, the authors' proposed HybMAS-GA is a star architecture where all shape context agents, N, are directly linked to a coordinator agent. Each retrieval agent must perform either a SC or an ADSC method to obtain a similar shape, started from its own initial configuration of sample points. This combination increases the efficiency of the proposed HybMAS-GA algorithm and ensures its convergence to an optimal images retrieval as it is shown through experimental results.

scholarly journals Optimization methodology of dynamic data structures based on genetic algorithms for multimedia embedded systems

2009 ◽  
Vol 82 (4) ◽  
pp. 590-602 ◽  
Author(s):  
Christos Baloukas ◽  
Jose L. Risco-Martin ◽  
David Atienza ◽  
Christophe Poucet ◽  
Lazaros Papadopoulos ◽  
...  
Keyword(s):  
Genetic Algorithms ◽  
Embedded Systems ◽  
Data Structures ◽  
Dynamic Data Structures ◽  
Dynamic Data ◽  
Optimization Methodology
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