Evaluation Function for Synthesizing Security Protocols by means of Genetic Algorithms

2007 ◽  
Author(s):  
Luis Zarza ◽  
Josep Pegueroles ◽  
Miguel Soriano
Keyword(s):  
Genetic Algorithms ◽  
Security Protocols ◽  
Evaluation Function

Intelligent Product Brokering Services

2011 ◽  
pp. 527-543
Author(s):  
Sheng-Uei Guan
Keyword(s):  
Genetic Algorithms ◽  
Evaluation Function ◽  
Agent Based ◽  
Evolutionary Ontology ◽  
Intelligent Product

Agent-based system has great potential in the area of m-commerce and a lot of research has been done on making the system intelligent enough to personalize its service for users. In most systems, user-supplied keywords are normally used to generate a profile for each user. In this chapter, a design for an evolutionary ontology-based product-brokering agent for m-commerce applications has been proposed. It uses an evaluation function to represent the user’s preference instead of the usual keyword-based profile. By using genetic algorithms, the agent tries to track the user’s preferences for a particular product by tuning some of the parameters inside this function. A Java-based prototype has been implemented and the results obtained from our experiments look promising.

scholarly journals Genetic Algorithm in Hill Cipher Encryption

2017 ◽  
Author(s):  
Andysah Putera Utama Siahaan
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Evaluation Function ◽  
Hill Cipher ◽  
Encryption And Decryption ◽  
The Matrix ◽  
The Hill

The matrix in Hill Cipher was designed to perform encryption and decryption. Every column and row must be inserted by integer numbers. But, not any key that can be given to the matrix used for the process. The wrong determinant result cannot be used in the process because it produces the incorrect plaintext when doing the decryption after the encryption. Genetic algorithms offer the optimized way to determine the key used for encryption and decryption on the Hill Cipher. By determining the evaluation function in the genetic algorithm, the key that fits the composition will be obtained. By implementing this algorithm, the search of the key on the Hill Cipher will be easily done without spending too much time. Genetic algorithms do well if it is combined with Hill Cipher.

Deep Boltzmann Machines Aided Design Based on Genetic Algorithms

2014 ◽  
Vol 568-570 ◽  
pp. 848-851 ◽  
Author(s):  
Kai Liu ◽  
Li Min Zhang ◽  
Yong Wei Sun
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Global Search ◽  
Evaluation Function ◽  
Restricted Boltzmann Machines ◽  
Generalization Performance ◽  
Boltzmann Machines ◽  
Deep Boltzmann Machines ◽  
Aided Design

To resolve the problem of no guidance about how to set the values of numerical meta-parameters and difficulty to achieve optimization of Deep Boltzmann Machines, genetic algorithms are used to develop an automatic optimizing method named GA-RBMs (Genetic Algorithm-Restricted Boltzmann Machines) for this model’s aided design. Based on the Restricted Boltzmann Machines’ features and evaluation function, a genetic algorithm is designed and realizes the global search of satisfied structure. We also initialize the network’s weights to determine the number of visible units and hidden units. The experiments were conducted on MNIST digits handwritten datasets. The results proved that this optimization reduced the dimension of visible units and improved the performance of feature extracted by Deep Boltzmann Machines. The network optimized has good generalization performance and meets the demand of Deep Boltzmann Machines’ aided design.

scholarly journals Parallel Genetic Algorithms for University Scheduling Problem

2017 ◽  
Vol 7 (2) ◽  
pp. 1096
Author(s):  
Artan Berisha ◽  
Eliot Bytyçi ◽  
Ardeshir Tershnjaku
Keyword(s):  
Genetic Algorithms ◽  
Execution Time ◽  
Parallel Implementation ◽  
Coarse Grained ◽  
Scheduling Problem ◽  
Evaluation Function ◽  
Parallel Genetic Algorithms ◽  
Hard Problems ◽  
Np Hard Problems ◽  
Better Than

University scheduling timetabling problem, falls into NP hard problems. Re-searchers have tried with many techniques to find the most suitable and fastest way for solving the problem. With the emergence of multi-core systems, the parallel implementation was considered for finding the solution. Our approaches attempt to combine several techniques in two algorithms: coarse grained algorithm and multi thread tournament algorithm. The results obtained from two algorithms are compared, using an algorithm evaluation function. Considering execution time, the coarse grained algorithm performed twice better than the multi thread algorithm.

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