Data Structures and Algorithms

2405 Submissions

[2] viXra:2405.0074 [pdf] submitted on 2024-05-14 21:17:31

Software Development Tool with Petri Net and Object-Oriented Programming Language: Net Oriented System Description Language

Authors: Han Yong Gil, Min Hyok, Kim Song Hyok, Choe Yong Su, Song Kwang Hyok, Choe Tae Hyok, Kim Jing Yong
Comments: 15 Pages.

This paper aims to describe the development and application of Net-oriented System Description Language (NSDL), a new and independent tool of software development combined with advantages of Petri net and object-oriented programming language VB. Unlike previous tools, the transitions of custom controls such as Textbox, Table, Graph, Button and Checkbox, etc. and the extension (or restriction) of place, transition and arc were introduced to improve modeling capability of Petri net. NSDL was enhanced the flexibility, convenience and extensibility of the software development by Visual Basic(VB) language support based on Microsoft Net Framework 4.0 library that is easy to use and learning. Approximation of BP neural net was carried out to validate NSDL’s effectiveness in three manners. NSDL can be used in the development of software or modelling of complex information systems with its great modeling capability.
Category: Data Structures and Algorithms

[1] viXra:2405.0040 [pdf] submitted on 2024-05-07 21:07:59

Speedup Genetic Algorithm Using Parallel Processing Method

Authors: Hak Kun Ri, Chol Hun Pak, Nam Song An
Comments: 7 Pages.

Genetic Algorithm (GA) is one of most popular swarm based evolutionary search algorithms that involve multiple data independent computations. Such computations can be made in parallel processing method on GPU cores using Compute Unified Design Architecture (CUDA) platform. In this paper, various operations of GA such as fitness evaluation, selection, crossover and mutation, etc. are implemented in parallel on GPU cores and then performance is compared with its serial implementation. Result shows that the overall computational time can substantially be decreased by parallel implementation on GPU cores. The proposed implementations resulted in 1.18 to 3.68 times faster than the corresponding serial implementation on CPU.
Category: Data Structures and Algorithms