GENETIC ALGORITHMS, FLOATING POINT NUMBERS AND APPLICATIONS

2005 ◽  
Vol 16 (11) ◽  
pp. 1811-1816 ◽  
Author(s):  
YORICK HARDY ◽  
WILLI-HANS STEEB ◽  
RUEDI STOOP
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Linear Equations ◽  
Floating Point ◽  
The Core ◽  
Point Number ◽  
Floating Point Number ◽  
Roots Of Polynomials ◽  
Bitwise Operations ◽  
Floating Point Numbers

The core in most genetic algorithms is the bitwise manipulations of bit strings. We show that one can directly manipulate the bits in floating point numbers. This means the main bitwise operations in genetic algorithm mutations and crossings are directly done inside the floating point number. Thus the interval under consideration does not need to be known in advance. For applications, we consider the roots of polynomials and finding solutions of linear equations.

scholarly journals Approximate Reciprocal Square Root with Single - and Half-Precision Floats

2018 ◽  
Author(s):  
Matheus M. Susin ◽  
Lucas Wanner
Keyword(s):  
Power Consumption ◽  
Floating Point ◽  
Square Root ◽  
Single Precision ◽  
Approximation Techniques ◽  
Point Number ◽  
Floating Point Number ◽  
Floating Point Numbers

In this work, we compared the precision, speed, and power consumption of the reciprocal square root of a single-precision floating point number, using different approximation techniques. We also devised an equivalent approximation for half-precision floating point numbers, and evaluated its performance across the whole range of positive non-zero 16-bit floating point values.

scholarly journals Modifikasi Algoritma Genetika untuk Penyelesaian Permasalahan Penjadwalan Pelajaran Sekolah

2014 ◽  
Vol 10 (1) ◽  
pp. 111
Author(s):  
Rahman Erama ◽  
Retantyo Wardoyo
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Effective Algorithm ◽  
Scheduling Problems ◽  
The Core ◽  
Alternative Algorithm ◽  
Crossover Phase ◽  
School Scheduling ◽  
Core Problem ◽  
Standard Genetic Algorithm

AbstrakModifikasi Algoritma Genetika pada penelitian ini dilakukan berdasarkan temuan-temuan para peneliti sebelumnya tentang kelemahan Algoritma Genetika. Temuan-temuan yang dimakasud terkait proses crossover sebagai salah satu tahapan terpenting dalam Algoritma Genetika dinilai tidak menjamin solusi yang lebih baik oleh beberapa peneliti. Berdasarkan temuan-temuan oleh beberapa peneliti sebelumnya, maka penelitian ini akan mencoba memodifikasi Algoritma Genetika dengan mengeliminasi proses crossover yang menjadi inti permasalahan dari beberapa peneliti tersebut. Eliminasi proses crossover ini diharapkan melahirkan algoritma yang lebih efektif sebagai alternative untuk penyelesaian permasalahan khususnya penjadwalan pelajaran sekolah.Tujuan dari penelitian ini adalah Memodifikasi Algoritma Genetika menjadi algoritma alternatif untuk menyelesaikan permasalahan penjadwalan sekolah, sehingga diharapkan terciptanya algoritma alternatif ini bisa menjadi tambahan referensi bagi para peneliti untuk menyelesaikan permasalahan penjadwalan lainnya.Algoritma hasil modifikasi yang mengeliminasi tahapan crossover pada algoritma genetika ini mampu memberikan performa 3,06% lebih baik dibandingkan algoritma genetika sederhana dalam menyelesaikan permasalahan penjadwalan sekolah. Kata kunci—algoritma genetika, penjadwalan sekolah, eliminasi crossover  AbstractModified Genetic Algorithm in this study was based on the findings of previous researchers about the weakness of Genetic Algorithms. crossover as one of the most important stages in the Genetic Algorithms considered not guarantee a better solution by several researchers. Based on the findings by previous researchers, this research will try to modify the genetic algorithm by eliminating crossover2 which is the core problem of several researchers. Elimination crossover is expected to create a more effective algorithm as an alternative to the settlement issue in particular scheduling school.This study is intended to modify the genetic algorithm into an algorithm that is more effective as an alternative to solve the problems of school scheduling. So expect the creation of this alternative algorithm could be an additional resource for researchers to solve other scheduling problems.Modified algorithm that eliminates the crossover phase of the genetic algorithm is able to provide 2,30% better performance than standard genetic algorithm in solving scheduling problems school. Keywords—Genetic Algorithm, timetabling school, eliminate crossover

COMPUTING LINE INTERSECTIONS

2001 ◽  
Vol 01 (02) ◽  
pp. 217-230 ◽  
Author(s):  
M. GAVRILOVA ◽  
J. ROKNE
Keyword(s):  
Intersection Point ◽  
Binary Search ◽  
Floating Point ◽  
Search Procedure ◽  
Single Precision ◽  
Exact Arithmetic ◽  
The Core ◽  
Speed Up ◽  
Computationally Expensive ◽  
Floating Point Numbers

The main result of the paper is a new and efficient algorithm to compute the closest possible representable intersection point between two lines in the plane. The coordinates of the points that define the lines are given as single precision floating-point numbers. The novelty of the algorithm is the method for deriving the best possible representable floating point numbers: instead of solving the equations to compute the line intersection coordinates exactly, which is a computationally expensive procedure, an iterative binary search procedure is applied. When the required precision is achieved, the algorithm stops. Only exact comparison tests are needed. Interval arithmetic is applied to further speed up the process. Experimental results demonstrate that the proposed algorithm is on the average ten times faster than an implementation of the line intersection computation subroutine using the CORE library exact arithmetic.

Neural network training based on FPGA with floating point number format and it’s performance

2010 ◽  
Vol 20 (2) ◽  
pp. 195-202 ◽  
Author(s):  
Mehmet Ali Çavuşlu ◽  
Cihan Karakuzu ◽  
Suhap Şahin ◽  
Mehmet Yakut
Keyword(s):  
Neural Network ◽  
Floating Point ◽  
Neural Network Training ◽  
Network Training ◽  
Point Number ◽  
Floating Point Number

Study on Benchmark Structure Damage Identification Base on Frequency Response Function and Genetic Algorithm

2010 ◽  
Vol 163-167 ◽  
pp. 2765-2769 ◽  
Author(s):  
Wan Jie Zou ◽  
Zhen Luo ◽  
Guo En Zhou
Keyword(s):  
Genetic Algorithm ◽  
Frequency Response ◽  
Response Function ◽  
Frequency Response Function ◽  
Damage Identification ◽  
Fitness Function ◽  
Floating Point ◽  
Combined Method ◽  
Structure Damage ◽  
Floating Point Number

A combined method for the Benchmark structure damage identification base on the frequency response function(FRF) and genetic algorithm(GA) is presented. The reducing factors of element stiffness are used as the optimization variables, and the cross signature assurance criterion (CSAC) of the test FRF and the analysis FRF is used to constructing the optimization object function and the fitness function of the GA. To avoid the weakness of binary encoding, the floating point number encoding is used in the GA. At last, the Benchmark structure established by IASC-ASCE SHM group is caculated by the proposed method, the results show that even if the serious testing noise is considered, the patterns of damage of the Benchmark structure can be identified well. The effectiveness of the presented method is verified.

Sign in / Sign up

Export Citation Format

Share Document