Gingivitis Detection by Fractional Fourier Entropy and 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

Download Full-text

Variational Data Assimilation for a Lorenz Model Usinga Non-Standard Genetic Algorithm

Meteorology and Atmospheric Physics ◽

10.1007/s007030050036 ◽

1999 ◽

Vol 70 (3-4) ◽

pp. 227-238 ◽

Cited By ~ 6

Author(s):

B. Ahrens

Keyword(s):

Genetic Algorithm ◽

Data Assimilation ◽

Variational Data Assimilation ◽

Lorenz Model ◽

Standard Genetic Algorithm

Download Full-text

EFFICIENT DNA MOTIF DISCOVERY USING MODIFIED GENETIC ALGORITHM

International Journal of Computational Intelligence and Applications ◽

10.1142/s146902681350017x ◽

2013 ◽

Vol 12 (03) ◽

pp. 1350017

Author(s):

ESSAM AL DAOUD

Keyword(s):

Genetic Algorithm ◽

Dna Sequences ◽

Motif Discovery ◽

Consensus Algorithms ◽

Gene Position ◽

Dna Motif ◽

Implementation Time ◽

Dna Motif Discovery ◽

Standard Genetic Algorithm ◽

New Distribution

In this study, a new genetic algorithm was developed to discover the best motifs in a set of DNA sequences. The main steps were: finding the potential positions in each sequence by using few voters (1–5 sequences), constructing the chromosomes from the potential positions, evaluating the fitness for each gene (position) and for each chromosome, calculating the new random distribution, and using the new distribution to generate the next generation. To verify the effectiveness of the proposed algorithm, several real and artificial datasets were used; the results are compared to the standard genetic algorithm, and Gibbs, MEME, and consensus algorithms. Although all the algorithms have low correlation with the correct motifs, the new algorithm exhibits higher accuracy, without sacrificing implementation time.

Download Full-text

Chaotic Genetic Algorithm for Wavefront Correction in Adaptive Optics

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.666.293 ◽

2014 ◽

Vol 666 ◽

pp. 293-297

Author(s):

Stephen Kotiang ◽

Jae Ho Choi

Keyword(s):

Genetic Algorithm ◽

Adaptive Optics ◽

Logistic Function ◽

Wavefront Correction ◽

Chaos Search ◽

Deformable Membrane ◽

Deformable Membrane Mirror ◽

Encoding Method ◽

Wave Aberration ◽

Standard Genetic Algorithm

Chaotic genetic algorithm (CGA) is presented as an optimization algorithm for sensorless adaptive optics system to compensate atmospheric wave aberration. Chaos search strategy is incorporated into standard genetic algorithm by the logistic function that possess convergent, bifurcating, and chaotic characteristics during evolution to control the convergence of genetic algorithm. A real number encoding method is adopted in the search process and CGA is used to control a 61-actuator deformable membrane mirror (DM).The algorithm uses light intensity detected on the focal plane as the objective function to optimize, and the simulation results show CGAperforms faster than GA and thus can effectively be used in AO systems.

Download Full-text