Self-Evolvable Protocol Design Using Genetic Algorithms

2010 ◽  
Vol 1 (1) ◽  
pp. 36-56 ◽  
Author(s):  
Wenbing Yao ◽  
Sheng-Uei Guan ◽  
Zhiqiang Jiang ◽  
Ilias Kiourktsidis
Keyword(s):  
Genetic Algorithms ◽  
Protocol Design ◽  
Design Approach ◽  
User Requirements ◽  
Network Simulator ◽  
Next Generation ◽  
Next Generation Internet ◽  
Apply Genetic ◽  
Run Time

Self-modifying protocols (SMP) are protocols that can be modified at run time by the computers using them. Such protocols can be modified at run time so that they can adapt to the changing communicating environment and user requirements on the fly. Evolvable protocols are SMP designed using Genetic Algorithms (GA). The purpose of this paper is to apply Genetic Algorithms (GA) to design an evolvable protocol in order to equip communication peers with more autonomy and intelligence. The next-generation Internet will benefit from the concept of evolvable protocols. In this paper, we design a Self Evolvable Transaction Protocol (SETP) with a GA executor embedded. We then use the Network Simulator (NS2) to evaluate this evolvable protocol module to demonstrate the feasibility of our new design approach.

Self-Evolvable Protocol Design Using Genetic Algorithms

2012 ◽  
pp. 36-56
Author(s):  
Wenbing Yao ◽  
Sheng-Uei Guan ◽  
Zhiqiang Jiang ◽  
Ilias Kiourktsidis
Keyword(s):  
Genetic Algorithms ◽  
Protocol Design ◽  
Design Approach ◽  
User Requirements ◽  
Network Simulator ◽  
Next Generation ◽  
Next Generation Internet ◽  
Apply Genetic ◽  
Run Time

Self-modifying protocols (SMP) are protocols that can be modified at run time by the computers using them. Such protocols can be modified at run time so that they can adapt to the changing communicating environment and user requirements on the fly. Evolvable protocols are SMP designed using Genetic Algorithms (GA). The purpose of this paper is to apply Genetic Algorithms (GA) to design an evolvable protocol in order to equip communication peers with more autonomy and intelligence. The next-generation Internet will benefit from the concept of evolvable protocols. In this paper, we design a Self Evolvable Transaction Protocol (SETP) with a GA executor embedded. We then use the Network Simulator (NS2) to evaluate this evolvable protocol module to demonstrate the feasibility of our new design approach.

Optimizing Time-multiplexing Raster Cellular Neural Network simulator using genetic algorithms with RK8(6)

2011 ◽  
Vol 3 (6) ◽  
pp. 87-90
Author(s):  
O. H. Abdelwahed O. H. Abdelwahed ◽  
◽  
M. El-Sayed Wahed ◽  
O. Mohamed Eldaken
Keyword(s):  
Neural Network ◽  
Genetic Algorithms ◽  
Cellular Neural Network ◽  
Network Simulator

Formulation of Colors Using a Genetic Algorithm

2012 ◽  
Vol 17 (4) ◽  
pp. 241-244
Author(s):  
Cezary Draus ◽  
Grzegorz Nowak ◽  
Maciej Nowak ◽  
Marcin Tokarski
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Production Process ◽  
Computer Technology ◽  
Manual Method ◽  
Apply Genetic ◽  
Computer Aided ◽  
Multiple Samples ◽  
Plastics Industry

Abstract The possibility to obtain a desired color of the product and to ensure its repeatability in the production process is highly desired in many industries such as printing, automobile, dyeing, textile, cosmetics or plastics industry. So far, most companies have traditionally used the "manual" method, relying on intuition and experience of a colorist. However, the manual preparation of multiple samples and their correction can be very time consuming and expensive. The computer technology has allowed the development of software to support the process of matching colors. Nowadays, formulation of colors is done with appropriate equipment (colorimeters, spectrophotometers, computers) and dedicated software. Computer-aided formulation is much faster and cheaper than manual formulation, because fewer corrective iterations have to be carried out, to achieve the desired result. Moreover, the colors are analyzed with regard to the metamerism, and the best recipe can be chosen, according to the specific criteria (price, quantity, availability). Optimaization problem of color formulation can be solved in many diferent ways. Authors decided to apply genetic algorithms in this domain.

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