Minimization and Encoding of High Performance Asynchronous State Machines Based on Genetic Algorithm

2015 ◽  
Author(s):  
Tiago Curtinhas ◽  
Tassio Cortes Cavalcante ◽  
Duarte L. Oliveira ◽  
Lester A. Faria ◽  
Osamu Saotome
Keyword(s):  
Genetic Algorithm ◽  
High Performance ◽  
State Machines ◽  
Asynchronous State

scholarly journals High-Performance Parallel Implementation of Genetic Algorithm on FPGA

2019 ◽  
Vol 38 (9) ◽  
pp. 4014-4039 ◽  
Author(s):  
Matheus F. Torquato ◽  
Marcelo A. C. Fernandes
Keyword(s):  
Genetic Algorithm ◽  
High Performance ◽  
Parallel Implementation

Lane Detection Algorithm Based on Genetic Algorithm and its Parallel Computing Realization

2012 ◽  
Vol 479-481 ◽  
pp. 65-70
Author(s):  
Xiao Hui Zhang ◽  
Liu Qing ◽  
Mu Li
Keyword(s):  
Genetic Algorithm ◽  
Data Transmission ◽  
High Speed ◽  
High Performance ◽  
Large Data ◽  
Detection Algorithm ◽  
Lane Detection ◽  
The Road ◽  
Time Problem ◽  
High Speed Data

Based on the target detection of alignment template, the paper designs a lane alignment template by using correlation matching method, and combines with genetic algorithm for template stochastic matching and optimization to realize the lane detection. In order to solve the real-time problem of lane detection algorithm based on genetic algorithm, this paper uses the high performance multi-core DSP chip TMS320C6474 as the core, combines with high-speed data transmission technology of Rapid10, realizes the hardware parallel processing of the lane detection algorithm. By Rapid10 bus, the data transmission speed between the DSP and the DSP can reach 3.125Gbps, it basically realizes transmission without delay, and thereby solves the high speed transmission of the large data quantity between processor. The experimental results show that, no matter the calculated lane line, or the running time is better than the single DSP and PC at the parallel C6474 platform. In addition, the road detection is accurate and reliable, and it has good robustness.

Global Path Planning for Full-Area Coverage Robotic Systems by Employing an Active Genetic Algorithm

2011 ◽  
Vol 328-330 ◽  
pp. 1881-1886
Author(s):  
Cen Zeng ◽  
Qiang Zhang ◽  
Xiao Peng Wei
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
High Performance ◽  
Search Space ◽  
Area Coverage ◽  
Robotic Systems ◽  
Genetic Operators ◽  
A Algorithm ◽  
Coverage Path Planning ◽  
Search Spaces

Genetic algorithm (GA), a kind of global and probabilistic optimization algorithms with high performance, have been paid broad attentions by researchers world wide and plentiful achievements have been made.This paper presents a algorithm to develop the path planning into a given search space using GA in the order of full-area coverage and the obstacle avoiding automatically. Specific genetic operators (such as selection, crossover, mutation) are introduced, and especially the handling of exceptional situations is described in detail. After that, an active genetic algorithm is introduced which allows to overcome the drawbacks of the earlier version of Full-area coverage path planning algorithms.The comparison between some of the well-known algorithms and genetic algorithm is demonstrated in this paper. our path-planning genetic algorithm yields the best performance on the flexibility and the coverage. This meets the needs of polygon obstacles. For full-area coverage path-planning, a genotype that is able to address the more complicated search spaces.

scholarly journals Conformal Patch Antenna Arrays Design for Onboard Ship Deployment Using Genetic Algorithms

2013 ◽  
Vol 2013 ◽  
pp. 1-5 ◽  
Author(s):  
Stelios A. Mitilineos ◽  
Symeon K. Symeonidis ◽  
Ioannis B. Mpatsis ◽  
Dimitrios Iliopoulos ◽  
Georgios S. Kliros ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Antenna Arrays ◽  
Patch Antenna ◽  
High Performance ◽  
Aerodynamic Drag ◽  
A Priori ◽  
Conformal Antennas ◽  
Antenna Performance ◽  
And Performance ◽  
Aerodynamic Drag Reduction

Conformal antennas and antenna arrays (arrays) have become necessary for vehicular communications where a high degree of aerodynamic drag reduction is needed, like in avionics and ships. However, the necessity to conform to a predefined shape (e.g., of an aircraft’s nose) directly affects antenna performance since it imposes strict constraints to the antenna array’s shape, element spacing, relative signal phase, and so forth. Thereupon, it is necessary to investigate counterintuitive and arbitrary antenna shapes in order to compensate for these constraints. Since there does not exist any available theoretical frame for designing and developing arbitrary-shape antennas in a straightforward manner, we have developed a platform combining a genetic algorithm-based design, optimization suite, and an electromagnetic simulator for designing patch antennas with a shape that is not a priori known (the genetic algorithm optimizes the shape of the patch antenna). The proposed platform is further enhanced by the ability to design and optimize antenna arrays and is intended to be used for the design of a series of antennas including conformal antennas for shipping applications. The flexibility and performance of the proposed platform are demonstrated herein via the design of a high-performance GPS patch antenna.

scholarly journals Information Theoretic Objective Function for Genetic Software Clustering

Proceedings ◽  
2019 ◽  
Vol 46 (1) ◽  
pp. 18
Author(s):  
Habib Izadkhah ◽  
Mahjoubeh Tajgardan
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Objective Function ◽  
High Performance ◽  
Evolutionary Process ◽  
Program Comprehension ◽  
Software Systems ◽  
Objective Functions ◽  
Information Theoretic ◽  
Software Clustering

Software clustering is usually used for program comprehension. Since it is considered to be the most crucial NP-complete problem, several genetic algorithms have been proposed to solve this problem. In the literature, there exist some objective functions (i.e., fitness functions) which are used by genetic algorithms for clustering. These objective functions determine the quality of each clustering obtained in the evolutionary process of the genetic algorithm in terms of cohesion and coupling. The major drawbacks of these objective functions are the inability to (1) consider utility artifacts, and (2) to apply to another software graph such as artifact feature dependency graph. To overcome the existing objective functions’ limitations, this paper presents a new objective function. The new objective function is based on information theory, aiming to produce a clustering in which information loss is minimized. For applying the new proposed objective function, we have developed a genetic algorithm aiming to maximize the proposed objective function. The proposed genetic algorithm, named ILOF, has been compared to that of some other well-known genetic algorithms. The results obtained confirm the high performance of the proposed algorithm in solving nine software systems. The performance achieved is quite satisfactory and promising for the tested benchmarks.

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