Hard test generation for augmenting path maximum flow algorithms using genetic algorithms: Revisited

2015 ◽  
Author(s):  
Maxim Buzdalov ◽  
Anatoly Shalyto
Keyword(s):  
Genetic Algorithms ◽  
Test Generation ◽  
Maximum Flow ◽  
Augmenting Path

scholarly journals An Efficient Functional Test Generation Method For Processors Using Genetic Algorithms

2015 ◽  
Vol 66 (4) ◽  
pp. 185-193 ◽  
Author(s):  
Ján Hudec ◽  
Elena Gramatová
Keyword(s):  
Genetic Algorithms ◽  
Test Generation ◽  
Functional Test ◽  
Evolutionary Strategies ◽  
Instruction Set ◽  
Functional Tests ◽  
Automatic Test Generation ◽  
Code Coverage ◽  
Fitness Functions

Abstract The paper presents a new functional test generation method for processors testing based on genetic algorithms and evolutionary strategies. The tests are generated over an instruction set architecture and a processor description. Such functional tests belong to the software-oriented testing. Quality of the tests is evaluated by code coverage of the processor description using simulation. The presented test generation method uses VHDL models of processors and the professional simulator ModelSim. The rules, parameters and fitness functions were defined for various genetic algorithms used in automatic test generation. Functionality and effectiveness were evaluated using the RISC type processor DP32.

A Combinatorial Algorithm for Generalized Maximum Flow Problem in Lossy Network

2012 ◽  
Vol 263-266 ◽  
pp. 2295-2300
Author(s):  
Li Wei Dong ◽  
Xiaofen Zhang ◽  
Hong Wang
Keyword(s):  
Flow Problem ◽  
Maximum Flow ◽  
Dijkstra’S Algorithm ◽  
Combinatorial Algorithm ◽  
Residual Network ◽  
Maximum Flow Problem ◽  
Dijkstra's Algorithm ◽  
Augmenting Path ◽  
Strongly Polynomial

This paper first presents the method for finding a generalized augmenting path according to the idea of Dijkstra's algorithm. Then the combinatorial algorithm for solving the generalized maximum flow is given in lossy network. The algorithm runs in strongly polynomial times by finding the generalized f-augmenting path in a generalized residual network.

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