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SILIȘTEANU, Damian Cristian, Bogdan Costel MOCANU, Mihnea Horia VREJOIU, and Florin POP. "Soluție pentru planificarea în timp real în sisteme distribuite utilizând algoritmi genetici." Revista Română de Informatică și Automatică 32, no. 3 (September 30, 2022): 33–50. http://dx.doi.org/10.33436/v32i3y202203.
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Dzidolikaitė, Agnė. "GENETIC ALGORITHMS FOR MULTIDIMENSIONAL SCALING / GENETINIŲ ALGORITMŲ TAIKYMAS DAUGIAMATĖMS SKALĖMS." Mokslas – Lietuvos ateitis 7, no. 3 (July 13, 2015): 275–79. http://dx.doi.org/10.3846/mla.2015.781.
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The paper analyzes global optimization problem. In order to solve this problem multidimensional scaling algorithm is combined with genetic algorithm. Using multidimensional scaling we search for multidimensional data projections in a lower-dimensional space and try to keep dissimilarities of the set that we analyze. Using genetic algorithms we can get more than one local solution, but the whole population of optimal points. Different optimal points give different images. Looking at several multidimensional data images an expert can notice some qualities of given multidimensional data. In the paper genetic algorithm is applied for multidimensional scaling and glass data is visualized, and certain qualities are noticed. Analizuojamas globaliojo optimizavimo uždavinys. Jis apibrėžiamas kaip netiesinės tolydžiųjų kintamųjų tikslo funkcijos optimizavimas leistinojoje srityje. Optimizuojant taikomi įvairūs algoritmai. Paprastai taikant tikslius algoritmus randamas tikslus sprendinys, tačiau tai gali trukti labai ilgai. Dažnai norima gauti gerą sprendinį per priimtiną laiko tarpą. Tokiu atveju galimi kiti – euristiniai, algoritmai, kitaip dar vadinami euristikomis. Viena iš euristikų yra genetiniai algoritmai, kopijuojantys gyvojoje gamtoje vykstančią evoliuciją. Sudarant algoritmus naudojami evoliuciniai operatoriai: paveldimumas, mutacija, selekcija ir rekombinacija. Taikant genetinius algoritmus galima rasti pakankamai gerus sprendinius tų uždavinių, kuriems nėra tikslių algoritmų. Genetiniai algoritmai taip pat taikytini vizualizuojant duomenis daugiamačių skalių metodu. Taikant daugiamates skales ieškoma daugiamačių duomenų projekcijų mažesnio skaičiaus matmenų erdvėje siekiant išsaugoti analizuojamos aibės panašumus arba skirtingumus. Taikant genetinius algoritmus gaunamas ne vienas lokalusis sprendinys, o visa optimumų populiacija. Skirtingi optimumai atitinka skirtingus vaizdus. Matydamas kelis daugiamačių duomenų variantus, ekspertas gali įžvelgti daugiau daugiamačių duomenų savybių. Straipsnyje genetinis algoritmas pritaikytas daugiamatėms skalėms. Parodoma, kad daugiamačių skalių algoritmą galima kombinuoti su genetiniu algoritmu ir panaudoti daugiamačiams duomenims vizualizuoti.
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Erama, Rahman, and Retantyo Wardoyo. "Modifikasi Algoritma Genetika untuk Penyelesaian Permasalahan Penjadwalan Pelajaran Sekolah." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 10, no. 1 (July 31, 2014): 111. http://dx.doi.org/10.22146/ijccs.6539.
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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
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Misevičius, Alfonsas, Andrius Blažinskas, Jonas Blonskis, and Vytautas Bukšnaitis. "Genetiniai algoritmai komivojažieriaus uždaviniui: negatyvieji ir pozityvieji aspektai*." Informacijos mokslai 50 (January 1, 2009): 173–80. http://dx.doi.org/10.15388/im.2009.0.3242.
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Šiame straipsnyje nagrinėjami klausimai, susiję su genetinių algoritmų taikymu, sprendžiant gerai žinomą kombinatorinio optimizavimo uždavinį – komivojažieriaus uždavinį (KU) (angl. traveling salesman problem). Svarstoma, jog genetinio algoritmo efektyvumui didelę įtaką turi uždavinio specifi nės savybės, todėl labai svarbu kūrybiškai sudaryti genetinį algoritmą konkrečiam sprendžiamam uždaviniui. Pateikiami eksperimentų, atliktų su realizuotu genetiniu algoritmu, rezultatai, iliustruojantys skirtingų veiksnių įtaką rezultatų kokybei. Konstatuojama, kad tinkamas genetinių operatorių ir lokaliojo individų (sprendinių) gerinimo derinimas leidžia gerokai padidinti genetinės paieškos efektyvumą.On the Genetic Algorithms for the Traveling Salesman Problem: Negative and Positive AspectsAlfonsas Misevičius, Andrius Blažinskas, Jonas Blonskis, Vytautas Bukšnaitis SummaryIn this paper, we discuss some issues related to the application of genetic algorithms (GAs) to the well-known combinatorial optimization problem – the traveling salesman problem (TSP). The results obtained from the experiments with the different variants of the genetic algorithm are presented as well. Based on these results, it is concluded that the effi ciency of the genetic search is much infl uenced by both the specifi c nature of the problem and the features of the algorithm itself. In particular, it should be emphasized that the incorporation of the (postcrossover) procedures for the local improvement of offspring has one of the crucial roles in obtaining high-quality solutions.
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Santoso, Kiswara Agung, Bagus Arief Setiawan, and Kusbudiono Kusbudiono. "Application of Genetic Algorithm on Inclusive Labeling of a Graph." InPrime: Indonesian Journal of Pure and Applied Mathematics 4, no. 1 (April 15, 2022): 24–32. http://dx.doi.org/10.15408/inprime.v4i1.24327.
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AbstractAs science developed, heuristic methods began to be used in graph coloring. Heuristic methods that have been used for graph coloring include Genetic Algorithm, Tabu Search, and Ant Colony Algorithm. A Genetic Algorithm is a method for solving optimization problems. In this study, the Genetic Algorithm will be used for the issue of labeling irregular vertices of inclusive distances to label any graph inclusively. We restrict an inclusive 1-distance to a simple graph using one-point crossover and mutation. The steps are a generation of random chromosomes, evaluating chromosome fitness values with tournament selection, conducting an evolutionary process consisting of one-point crossover and mutation, repeating the process until the termination criteria are met. The results of implementing the genetic algorithm on inclusive labeling can be determined by the chromatic number based on the adjacency matrix. The results of this labeling can be used as an alternative solution to the problem of inclusive labeling.Keywords: Genetic Algorithm; graph labeling; inclusive labeling. AbstrakSeiring berkembangnya ilmu pengetahuan metode heuristic mulai digunakan dalam pewarnaan graf. Metode heuristic yang telah digunakan untuk pewarnaan graf antara lain Algoritma Genetika, Tabu Search, dan Algoritma Semut (Ant Colony). Algoritma Genetika merupakan metode untuk menyelesaikan masalah optimasi. Pada penelitian ini, Algoritma Genetika digunakan untuk masalah pelabelan titik tak-teratur jarak inclusive agar dapat melabeli sebarang graf secara inclusive. Kami membatasi lingkup penelitian dengan menerapkan jarak inclusive 1 pada graf sederhana, menggunakan crossover satu titik dan mutasi. Metode yang digunakan dalam penelitian ini adalah studi literatur dengan mengkaji penggunaan Algoritma Genetika pada pelabelan titik tak-teratur jarak inclusive suatu graf. Langkah-langkah yang dilakukan adalah: pembangkitan kromosom secara acak, evaluasi nilai fitness kromosom dengan tournament selection, melakukan proses evolusi yang terdiri dari crossover satu titik dan mutasi, perulangan proses sampai kriteria pemerhentian terpenuhi. Hasil implementasi algoritma genetika pada pelabelan inclusive adalah dapat mengetahui bilangan kromatik berdasarkan matriks adjacency. Hasil pelabelan ini dapat dijadikan sebagai salah satu alternatif penyelesaian masalah pelabelan inklusif. Kata Kunci : Algoritma Genetika; pelabelan graf; pelabelan inklusif.
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Sumida, Brian. "Genetics for genetic algorithms." ACM SIGBIO Newsletter 12, no. 2 (June 1992): 44–46. http://dx.doi.org/10.1145/130686.130694.
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Hikmawan, Sisferi. "Algoritma Genetika dengan Mutasi Terbatas untuk Penjadwalan Perkuliahan." Jurnal Kajian Ilmiah 21, no. 2 (May 27, 2021): 229–42. http://dx.doi.org/10.31599/jki.v21i2.520.
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In University, lecture scheduling is the most important factor in service satisfaction for students. UNISMA Bekasi still uses the manual method in scheduling lectures. Genetic Algorithms can solve scheduling with different constraints. In the proposed Genetic Algorithm, the mutation operator is changed to be a limited individual mutation and a selection feature that is adjusted to the constraints in the problem to be solved. And Genetic Algorithms with limited mutations are proven to have advantages in accommodating the constraints found in UNISMA Bekasi. The result of testing in experiments conducted on curriculum data for the Odd Semester of the Academic Year 2020/2021 using a Genetic Algorithm with mutation_individu_terbatas, namely minimum load = 0 with iterations = 10 and population = 500.
Keywords: Data Mining, Genetic Algorithm, Schedule, Mutation
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Dalam perkuliahan, penjadwalan perkuliahan merupakan faktor paling penting dalam kepuasan pelayanan terhadap mahasiswa. UNISMA Bekasi masih menggunakan cara manual dalam penjadwalan perkuliahan. Algoritma Genetika dapat memecahkan penjadwalan dengan constraint berbeda-beda. Pada Algoritma Genetika yang diajukan, dilakukan pengubahan operator mutasi menjadi mutasi individu terbatas dan fitur seleksi yang disesuaikan dengan constraint dalam permasalahan yang ingin dipecahkan. Dan Algoritma Genetika dengan mutasi terbatas terbukti memiliki kelebihan dalam mengakomodir permasalahan constraint yang terdapat di UNISMA Bekasi. Dihasilkan Pengujian dalam percobaan yang dilakukan terhadap data kurikulum untuk Semester Ganjil Tahun Akademik 2020/2021 dengan menggunakan Algoritma Genetika dengan mutasi_individu_terbatas yaitu beban minimum = 0 dengan iterasi = 10 dengan populasi = 500.
Kata kunci: Data Mining, Algoritma Genetika, Mutasi, Jadwal Perkuliahan
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Raol, Jitendra R., and Abhijit Jalisatgi. "From genetics to genetic algorithms." Resonance 1, no. 8 (August 1996): 43–54. http://dx.doi.org/10.1007/bf02837022.
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Wicaksono, Satrio Agung. "Optimasi Sistem Penempatan Magang Menerapkan Algoritme Genetika." Jurnal Teknologi Informasi dan Ilmu Komputer 6, no. 1 (January 16, 2019): 17. http://dx.doi.org/10.25126/jtiik.201961950.
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<p>Magang merupakan proses yang penting dalam proses belajar mengajar di SMK. Secara spesifik magang di SMK disebut Prakerin (Praktek Kerja Industri). Penempatan magang harus memerhatikan kompetensi siswa, kuota dari perusahaan, kesesuaian jurusan dengan lowongan, dan penghasilan orang tua. Penempatan magang secara manual selama ini memakan waktu, sehingga kurang efisien. Oleh karena itu, penelitian ini fokus mengembangkan aplikasi dengan menerapkan pendekatan Algoritme Genetika untuk mempermudah dalam menentukan penempatan magang dengan menerapkan aturan yang berlaku. Algoritme Genetika dinilai sebagai algoritma yang relevan dan solutif untuk diterapkan dalam penyelesaian masalah optimasi kompleks. Masalah yang dimaksud umumnya adalah masalah yang sulit dilakukan dengan menerapkan metode konvensional. Algoritme Genetika memberikan hasil yang lebih baik untuk setiap iterasi pencarian solusi. Hasil fitness terbaik dengan nilai 0.0014286 diperoleh pada jumlah individu 200, jumlah generasi 200, persentase crossover 50% dan mutasi 10%. Hasil validasi dengan pihak SMK menyatakan bahwa sistem ini mudah untuk digunakan dan bermanfaat bagi pihak SMK, dengan rata-rata persentase kualitas sistem 82,5%. Algoritme Genetika efektif untuk diterapkan pada studi kasus penjadwalan atau penempatan magang yang memiliki karakteristik data yang kompleks.</p><p> </p><p><em><strong>Abstract</strong></em></p><p><em>Internships are an important component of teaching and learning activities in vocational high schools (SMK). Specifically the internship in SMK is called Prakerin (Industrial Work Practice). The internship placement should considering student competence, number of vacancies, the suitability of majors with vacancy, and the income of the parent. During this time, manual internship placement takes more time, so less efficient. Therefore, this research tries to approach using Genetic Algorithm to make it easier in determining the internship placement by applying the applicable rules. The Genetic Algorithm is judged as the right algorithm used in solving complex optimization problems, which is difficult to do by conventional methods. The Genetic Algorithm provides better results for each iteration of the solution search. Best fitness results with value 0.0014286 obtained on the number of individuals 200, the number of generation 200, the percentage of crossover 50% and the mutation 10%. Validation results with the SMK stated that the system is easy to use and beneficial to the SMK, with an average percentage of system quality about 82.5%. Genetic Algorithms are effective to apply to scheduling case studies or internship placements that have complex data characteristics.</em></p><p><strong><br /></strong></p>
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Zebua, Alfian Wiranata. "PENGARUH PANJANG BENTANG TERHADAP UKURAN PENAMPANG OPTIMUM BETON PRATEGANG PADA BALOK SEDERHANA DENGAN MENGGUNAKAN ALGORITMA GENETIKA." Jurnal Teknik Sipil 7, no. 1 (May 28, 2018): 16–25. http://dx.doi.org/10.24815/jts.v7i1.10481.
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Abstract: Structural design philosophy expected to be low cost and safe at once. At prestressed concrete structure, concrete allowable stress for bending structural component have to fullfill limit values on tranfer and service condition. Prestressed force determined using moment coeffiecient β method based on allwoable stress. At this structure, to achieve optimum design is not an easy task due to interaction between dimension size and prestressing force value. To determine optimum design criteria, genetics algorithms as one of optimum design method has been used in this paper. Genetics algorithm is a method to find the best solution using organism genetics process based on Darwin theory which solutions with the best fitness value that could be survive as the optimization result. Fitness value is minimum construction cost. Optimization process using genetics algorithm has been worked with computer software assitance Matlab. Simpe beam has been considered as numerical example. Optimization result is optimum design of dimension size and prestressing force. Optimization procedure with beam length (L) 10 m result are beam width (b) 0,40 m, beam height (h) 0,47 m, prestressing force (Fi) value 1344 KN and bulding cost total Rp. 16.651.000,-. Next optimization procedure has been done using different beam length. From this study, the interaction between beam length to optimum dimension size and minimum building cost has been achieved.Keywords : beam length, genetics algoritm, optimum sizing, prestressed concreteAbstrak: Filosofi perencanaan struktur diharapkan ekonomis sekaligus aman. Pada struktur beton prategang, tegangan ijin beton untuk komponen struktur lentur harus memenuhi nilai batas pada saat transfer dan beban layan. Gaya prategang ditentukan dengan menggunakan metode koefisien momen β yang didasarkan pada tegangan ijin. Pada struktur ini, penentuan desain optimum tidak mudah karena adanya keterkaitan antara ukuran penampang dengan besaran gaya prategang yang dibutuhkan. Untuk memperoleh kriteria desain yang optimum digunakan metode optimasi dengan menggunakan algoritma genetika. Algoritma genetika merupakan metode pencarian sesuai dengan proses genetika organisme berdasarkan teori evolusi Darwin, dimana solusi dengan nilai fitness (tujuan) yang tinggi yang mampu bertahan sebagai hasil dari proses optimasi. Nilai fitness (tujuan) adalah memperoleh total harga konstruksi yang paling rendah. Proses optimasi dengan algoritma genetika dikerjakan dengan bantuan software komputer Matlab. Untuk melakukan optimasi digunakan algoritma genetika real. Jenis struktur yang ditinjau adalah balok sederhana. Dari hasil optimasi dengan berbagai panjang bentang diperoleh dimensi balok dan nilai gaya prategang yang optimum. Proses optimasi dengan panjang bentang (L) 10 m, diperoleh lebar balok (b) 0,40 m, tinggi balok (h) 0,47 m, nilai gaya prategang (Fi) sebesar 1344 KN dengan total harga Rp. 16.651.000,-. Proses optimasi selanjutnya dilakukan dengan panjang bentang balok yang bervariasi. Dari hasil penelitian diperoleh hubungan antara panjang bentang balok dengan ukuran penampang optimum serta harga total struktur yang paling rendah.Kata kunci : algoritma genetika, beton prategang, panjang bentang, ukuran penampang optimum
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Gotami, Nurina Savanti Widya, Yane Marita Febrianti, Robih Dini, Hamim Fathul Aziz, San Sayidul Akdam Augusta, and Vivi Nur Wijayaningrum. "Penentuan Rute Pengiriman Ice Tube di Kota Malang dengan Algoritma Genetika." Jurnal Buana Informatika 11, no. 1 (May 1, 2020): 10. http://dx.doi.org/10.24002/jbi.v11i1.2559.
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Abstract. Determining routes for ice tube delivery in Malang is a complex combinatorial problem classified as NP-hard problem. This study aims for optimizing the sales travel routes determination for the delivery to several customers by considering the efficiency of distance traveled. This problem is modeled in the form of Multi Salesman Traveling Problem. Genetic algorithm was used to optimize the determination of ice tube delivery routes that must be taken by each sales. Problems were coded by using permutation representation in which order crossover and swap mutation methods were used for the reproduction process. The process of finding solution was done by using elitism selection. The best genetic algorithm parameters obtained from the test results are the number of iterations of 40 and the population of 40, with the shortest route of 30.3 km. The final solution given by the genetic algorithm is in the form of a travel route that must be taken by each ice tube sales.Keywords: genetic algorithm, mutli travelling salesman problem, optimization, routeAbstrak. Penentuan rute pengiriman ice tube di kota Malang merupakan permasalahan kombinatorial kompleks yang diklasifikasikan sebagai permasalahan NP-hard. Penelitian ini bertujuan untuk melakukan optimasi dalam pembentukan rute perjalanan sales dalam melakukan pengiriman ke beberapa pelanggan dengan mempertimbangkan efisiensi jarak tempuh. Permasalahan ini dimodelkan dalam bentuk Multi Salesman Travelling Problem. Algoritme genetika digunakan untuk mengoptimalkan pembentukan rute pengiriman ice tube yang harus dilalui oleh setiap sales. Permasalahan dikodekan menggunakan representasi permutasi, dengan proses reproduksi menggunakan metode order crossover dan swap mutation. Proses pencarian solusi dilakukan menggunakan elitism selection. Parameter algoritme genetika terbaik yang didapatkan dari hasil pengujian adalah banyaknya iterasi sebesar 40 dan banyaknya populasi sebesar 40, dengan rute terpendek sebesar 30.3 km. Solusi akhir yang diberikan oleh algoritme genetika berupa rute perjalanan yang harus ditempuh oleh setiap sales ice tube.Kata Kunci: algoritme genetika, multi travelling salesman problem, optimasi, rute
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Burke, Donald S., Kenneth A. De Jong, John J. Grefenstette, Connie Loggia Ramsey, and Annie S. Wu. "Putting More Genetics into Genetic Algorithms." Evolutionary Computation 6, no. 4 (December 1998): 387–410. http://dx.doi.org/10.1162/evco.1998.6.4.387.
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The majority of current genetic algorithms (GAs), while inspired by natural evolutionary systems, are seldom viewed as biologically plausible models. This is not a criticism of GAs, but rather a reflection of choices made regarding the level of abstraction at which biological mechanisms are modeled, and a reflection of the more engineering-oriented goals of the evolutionary computation community. Understanding better and reducing this gap between GAs and genetics has been a central issue in an interdisciplinary project whose goal is to build GA-based computational models of viral evolution. The result is a system called Virtual Virus (VIV). VIV incorporates a number of more biologically plausible mechanisms, including a more flexible genotype-to-phenotype mapping. In VIV the genes are independent of position, and genomes can vary in length and may contain noncoding regions, as well as duplicative or competing genes. Initial computational studies with VIV have already revealed several emergent phenomena of both biological and computational interest. In the absence of any penalty based on genome length, VIV develops individuals with long genomes and also performs more poorly (from a problem-solving viewpoint) than when a length penalty is used. With a fixed linear length penalty, genome length tends to increase dramatically in the early phases of evolution and then decrease to a level based on the mutation rate. The plateau genome length (i.e., the average length of individuals in the final population) generally increases in response to an increase in the base mutation rate. When VIV converges, there tend to be many copies of good alternative genes within the individuals. We observed many instances of switching between active and inactive genes during the entire evolutionary process. These observations support the conclusion that noncoding regions serve as scratch space in which VIV can explore alternative gene values. These results represent a positive step in understanding how GAs might exploit more of the power and flexibility of biological evolution while simultaneously providing better tools for understanding evolving biological systems.
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Aqham, Ahmad Ashifuddin, and Kristoko Dwi Hartomo. "Data Mining untuk Nasabah Bank Telemarketing Menggunakan kombinasi Algoritm Naïve Bayes Dan Algoritma Genetik." InfoTekJar (Jurnal Nasional Informatika dan Teknologi Jaringan) 4, no. 1 (September 6, 2019): 47–56. http://dx.doi.org/10.30743/infotekjar.v4i1.1574.
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The strategy used for telemarketing by conducting promotional media, this strategy is a marketing method used by banks, in offering products to customers, banks, one of the products that will be offered is time deposits, the bank has difficulty in knowing the obstacles experienced by customers in making a decision to make deposits against the bank, so that later it will have the effect of a financial crisis at the bank. Telemarketing banks must have targets for customers, where customers have the potential to join one of the bank's products, namely deposits by looking at existing customer data.With the existing problems will be overcome by the datamining technique that will be used for this research is the Naïve Bayes algorithm and genetic algorithm which aims to predict the Telemarketing customers' sources sourced from public UCI Repsitory data so that the bank offers a product to the customer right at the target. Naïve Bayes test with experimental results of 86.71% accuracy while cross validation testing using Genetic algorithm produces high accuracy 90.27%, Root proves the prediction of time series data Naïve Bayes method and Genetics produces an accuracy of 90.27%, so it can be concluded that using the Naive Bayes algorithm and Genetics can optimize in predicting Telemarketing client decisions right in the deposit offer.
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Al Rivan, Muhammad Ezar, Steven Steven, and William Tanzil. "Optimasi Fuzzy C-Means dan K-Means Menggunakan Algoritma Genetika untuk Pengklasteran Dataset Diabetic Retinopathy." Jurnal Teknologi Informasi dan Ilmu Komputer 7, no. 5 (October 8, 2020): 993. http://dx.doi.org/10.25126/jtiik.2020711872.
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<p class="Abstrak"><em>Diabetic Retinopathy</em> adalah komplikasi dari diabetes yang mengakibatkan gangguan pada retina mata. Gangguan tersebut dapat diketahui dengan deteksi awal melalui data yang diekstraksi dari citra mata. Deteksi awal dapat dilakukan dengan menggunakan metode <em>clustering</em>. Metode yang digunakan yaitu <em>Fuzzy C-Means</em> dan <em>K-Means</em>. <em>Fuzzy C-Means</em> dan <em>K-Means</em> memiliki kelemahan dari jumlah iterasi yang besar. Jumlah iterasi pada <em>Fuzzy C-Means</em> dan <em>K-Means</em> dapat dioptimasi dengan menggunakan Algoritma Genetika. Optimasi dilakukan dengan cara mengganti bagian pada <em>Fuzzy C-Means</em> dan <em>K-Means</em> pada saat menentukan pusat <em>cluster</em>. Dataset yang digunakan pada penelitian adalah dataset <em>Diabetic Retinopathy</em>. Hasil optimasi sebelum dan sesudah hybrid Algoritma Genetika pada <em>Fuzzy C-Means</em> terlihat pada nilai rata-rata iterasi dari 17,1 menjadi 6,65 terjadi penurunan sebesar 61,11% dan pada <em>K-Means</em> terlihat pada nilai rata-rata iterasi dari 10,85 menjadi 7,35 terjadi penurunan sebesar 32,25%. Berdasarkan hasil perbandingan nilai rata-rata iterasi Algoritma Genetika–<em>Fuzzy C-Means</em> dan Algoritma Genetika-K-Means maka dapat disimpulkan bahwa Algoritma Genetika-<em>Fuzzy C-Means</em> memiliki jumlah iterasi yang lebih baik dibanding Algoritma Genetika-<em>K-Means</em>. Algoritma Genetika-<em>Fuzzy C-Means</em> juga memiliki <em>inter cluster distance </em>yang paling kecil dan <em>intra cluster distance </em>yang paling besar.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Abstract"><em>Diabetic Retinopathy is diabetic complication that cause retina disorder. Retina disorder can be known from data extracted from eye image. Early detection conduct using clustering. These methods are Fuzzy C-Means and K-Means. These methods have large number of iteration as weakness. Number of iteration can be optimized using genetic algorithm. Optimization conducted by replace a part from Fuzzy C-Means dan K-Means that use to generate early centroid. The dataset used in the study is a dataset of diabetic retinopathy. The optimization results before and after hybrid GeneticAlgorithm on Fuzzy C-Means are the average iteration values decreased from 17.1 to 6.65, decreasing 61,11% and in K-Means are the average iteration values decreased from 10.85 to 7.35 decreasing 32,25%. Based on the comparison of Genetic Algorithm Fuzzy C-Means and Genetic Algorithm K-Means iterations, it can be concluded that Genetic Algorithm Fuzzy C-Means has a better number of iteration than Genetic Algorithm K-Means. Genetic Algorithm-Fuzzy-C-Means has smallest inter cluster distance and biggest intra cluster distance.</em></p><p class="Abstrak"><em><strong><br /></strong></em></p>
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Elva, Yesri. "SISTEM PENJADWALAN MATA PELAJARAN MENGGUNAKAN ALGORITMA GENETIKA." JURNAL TEKNOLOGI INFORMASI 3, no. 1 (July 20, 2019): 49. http://dx.doi.org/10.36294/jurti.v3i1.687.
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Abstract - Schedule is one important factor to support the learning process, one of which at SMKN 3 Pariaman. In SMKN 3 Pariaman scheduling process is still done manually, consequently there are conflicting schedules and timing of learning becomes too late. One of completion method to the problem is to use a genetic algorithm, because it is one of the Genetic Algorithm optimization algorithm that is robust and can be used on a wide variety of case studies such as scheduling. This algorithm is also often used to find the optimal solution both in the case of simple to complex problem-solving technique that determines the start and initialization pupulasi chromosomes, determine the value of fitness, selection, crossover, mutation. Mutations done to produce the best fitness value which can be used to determine the final outcome scheduling. If the best fitness values have been obtained, the process is stopped and reach the finish condition.Keywords - Genetic Algorithms, Scheduling Abstrak - Jadwal merupakan salah satu faktor penting untuk penunjang proses belajar mengajar, salah satunya pada SMKN 3 Pariaman. Pada SMKN 3 Pariaman proses penyusunan jadwal masih dilakukan secara manual, akibatnya masih terdapat jadwal yang bentrok dan waktu pelaksanaan belajar mengajar menjadi terlambat. Salah satu metode untuk penyelesain masalah tersebut adalah dengan menggunakan algoritma genetika, karena Algoritma Genetika merupakan salah satu algoritma optimasi yang kuat dan bisa digunakan pada berbagai macam studi kasus seperti penjadwalan. Algoritma ini juga sering digunakan untuk mencari solusi optimal baik pada kasus yang sederhana sampai yang rumit teknik pemecahan masalahnya yaitu menentukan pupulasi awal dan inisialisasi kromosom, menentukan nilai fitness, seleksi crossover, mutasi. Mutasi dilakukan sampai menghasilkan nilai fitness terbaik yang dapat digunakan untuk penentuan hasil akhir penyusunan jadwal. Jika nilai fitness terbaik sudah didapatkan maka proses dihentikan dan mencapai kondisi selesai.Kata kunci - Algoritma Genetika, Penjadwalan
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Babu, M. Nishidhar, Y. Kiran, and A. Ramesh V. Rajendra. "Tackling Real-Coded Genetic Algorithms." International Journal of Trend in Scientific Research and Development Volume-2, Issue-1 (December 31, 2017): 217–23. http://dx.doi.org/10.31142/ijtsrd5905.
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Suseno, Eka Widya, Alfian Ma'arif, and Riky Dwi Puriyanto. "Tuning Parameter Pengendali PID dengan Metode Algoritma Genetik pada Motor DC." TELKA - Telekomunikasi Elektronika Komputasi dan Kontrol 8, no. 1 (May 23, 2022): 1–13. http://dx.doi.org/10.15575/telka.v8n1.1-13.
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Saat ini, pengendali Proportional Integral Derivative (PID) digunakan secara umum untuk mendapatkan solusi optimum. Solusi dikatakan optimum apabila output di kehidupan nyata sesuai dengan output yang telah ditentukan. Oleh karena itu, pengendali adalah suatu hal yang dibutuhkan. Tantangan dalam menggunakan pengendali adalah tuning parameter untuk mencari konstanta parameter PID seperti Proporsional Gain (KP), Waktu Integral (KI) dan Waktu Derivatif (KD). Untuk memaksimalkan kinerja motor DC, pengaturan pengendali PID yang tepat merupakan hal yang sangat penting. Desain pengendali PID sebagai pengendali motor DC sudah sering dilakukan. Penggunaan pengendali PID membutuhkan pengaturan parameter yang tepat untuk mendapatkan kinerja yang optimal pada motor. Metode yang umum dalam menentukan parameter pengendali PID adalah trial and error. Namun hasil yang didapat tidak membuat pengendali PID optimal dan justru akan merusak sistem. Oleh karena itu, penelitian ini menggunakan salah satu metode penalaan parameter PID dengan menggunakan metode cerdas berbasis Genetic Algorithm (Algoritma Genetik) untuk mengoptimasi dan menentukan parameter yang tepat dari PID. Algoritma genetik adalah salah satu algoritma yang menggunakan genetika sebagai model algoritmanya. Algoritma genetik terinspirasi dari meniru proses seleksi alam, yaitu proses yang menyebabkan evolusi biologis. Konsep inilah yang diadaptasi dan diterapkan dengan baik untuk menala parameter PID. Penggunaan metode algoritma genetik dapat memberikan hasil yang lebih baik pada setiap iterasinya. Hasil penelitian menunjukkan bahwa overshoot yang dihasilkan karena adanya respon kecepatan setelah penambahan PID adalah kurang dari 10%. Currently, Proportional Integral Derivative (PID) controllers are generally used to obtain the optimum solution. The solution is said to be optimum if the output in real life matches the output determined. Therefore, the controller is needed. The challenge in using the controller is tuning parameters to find constants of PID parameters such as Proportional Gain (KP), Integral Time (KI) and Derivative Time (KD). In order to maximize the performance of a DC motor, proper PID controller settings are crucial. The design of PID controllers as DC motor controllers has often been done. The use of a PID controller requires setting the right parameters to get optimal performance on the motor. The common method for determining PID controller parameters is trial and error. However, the results obtained do not make the PID controller optimal and will actually damage the system. Therefore, this study uses one of the PID parameter tuning methods by using an intelligent method based on Genetic Algorithm to optimize and determine the appropriate parameters of PID. Genetic algorithm is an algorithm that uses genetics as a model algorithm. Genetic algorithms are inspired by imitating the process of natural selection, the process that causes biological evolution. This concept is well adapted and applied for tuning PID parameters. The use of genetic algorithm methods can give better results in each iteration. The results showed that the resulting overshoot due to the speed response after the addition of PID was less than 10%.
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Bahri, Saeful. "SELEKSI ATRIBUT PADA ALGORITMA C4.5 MENGGUNAKAN GENETIK ALGORITMA DAN BAGGING UNTUK ANALISA KELAYAKAN PEMBERIAN KREDIT." KLIK - KUMPULAN JURNAL ILMU KOMPUTER 4, no. 2 (September 28, 2017): 174. http://dx.doi.org/10.20527/klik.v4i2.99.
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<p><em>According to the banking ACT No. 9 of 1992 is the provision of credit or money bills which can dipersama-kan with it, based on the approval of an agreement between the bank pinjam-meminjam with other parties that require that the borrower to pay off a loan after a certain period of time with the giving of flowers. Credit analysis aims to evaluate the customer able to or not in fulfilling obligations. In analyzing the sometimes an analyst is not accurate in analyzing causing bad credit. Of the problems that existed then used a method of classification for an analysis of the feasibility of granting credit using a model algorithm Genetic Algorithm with C4.5 (AG) as a selection of attributes and bagging method to improve accuracy. After testing two models namely algorithm C4.5 and C4.5 with Genetic Algorithms (AG) and the results obtained bagging method is the algorithm C 4.5 produces a value accuracy 93,47% and AUC values 0,932 with excellent levels of Clasification diagnose but after Genetic Algorithm added (AG) and increased accuracy value bagging 2.87% to 96,34% and AUC values increased 0.044 became 0.976</em><em>.</em></p><p><em><strong>Keywords</strong></em><em>: </em><em>Credit, the algorithm C 4.5, Genetic Algorithms (GA), Bagging</em></p><p><em>Menurut UU Perbankan No.9 Tahun 1992 kredit merupakan </em><em>penyediaan uang atau tagihan yang dapat dipersama-kan dengan itu, berdasarkan persetujuan atau kesepakatan pinjam-meminjam antara bank dengan pihak lain yang mewajibkan pihak peminjam untuk melunasi utangnya setelah jangka waktu tertentu dengan pemberian bunga. Analisa kredit bertujuan untuk mengevaluasi nasabah mampu atau tidak dalam memenuhi kewajiban. Dalam menganalisa terkadang seorang analis tidak akurat dalam menganalisa sehingga menyebabkan kredit macet. </em><em>Dari permasalahan yang ada maka digunakan sebuah metode klasifikasi untuk analisis kelayakan pemberian kredit menggunakan model algoritma C4.5 dengan Algoritma Genetika (AG) sebagai seleksi atribut dan metode bagging untuk meningkatkan akurasi. Setelah dilakukan pengujian dua model yaitu algoritma C4.5 dan C4.5 dengan Algoritma Genetika (AG) dan metode bagging hasil yang diperoleh adalah algoritma C4.5 menghasilkan nilai akurasi 93,47 % dan nilai AUC </em><em>0,932 dengan tingkat diagnose excellent Clasification namun setelah ditambahkan Algoritma Genetika(AG) dan bagging nilai akurasi meningkat 2,87% menjadi 96,34 % dan nilai AUC meningkat 0.044 menjadi 0.976</em><em>.</em></p><p><em><strong>Kata kunci</strong></em><em>: </em><em>Kredit, Algoritma C4.5, Algoritma Genetika (AG), Bagging</em></p><p><em><br /></em></p>
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Turčaník, Michal, and Martin Javurek. "Cryptographic Key Generation by Genetic Algorithms." Information & Security: An International Journal 43, no. 1 (2019): 54–61. http://dx.doi.org/10.11610/isij.4305.
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Kandeeban, Selvakani S., and R. S. Rajesh. "Desegregated ID Execution Using Genetic Algorithm." International Journal of Engineering and Technology 1, no. 1 (2009): 45–49. http://dx.doi.org/10.7763/ijet.2009.v1.8.
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Chouh, M., and K. Boukhetala. "Semi-nonnegative Matrix Factorization Algorithm Based on Genetic Algorithm Initialization." International Journal of Machine Learning and Computing 6, no. 4 (August 2016): 231·—234. http://dx.doi.org/10.18178/ijmlc.2016.6.4.603.
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Widodo, Arif. "Optimasi Linieritas Rangkaian R-2R Ladder DAC Menggunakan Algoritma Genetika." INAJEEE : Indonesian Journal of Electrical and Eletronics Engineering 1, no. 1 (February 1, 2018): 7. http://dx.doi.org/10.26740/inajeee.v1n1.p7-11.
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Abstrak - rangkaian R-2R ladder digital-to-analog converter (DAC) adalah rangkaian elektronika sederhanayang dapat dibuat dengan dua nilai resistor serta banyak digunakan untuk proses konversi nilai digital keanalog secara langsung. Pemilihan nilai serta penempatan resistor pada rangkaian ini sangat berpengaruhpada linieritas sinyal hasil konversi. Penelitian ini bertujuan untuk memberikan solusi dalam merancangrangkaian R-2R ladder DAC dengan linieritas yang medekati optimal menggunakan komponen resistor yangada di pasaran. Dengan bantuan algoritma genetika, komponen resistor yang ada dapat dirangkai sedemikianrupa sehingga menghasilkan rangkaian DAC dengan nilai integral non-linearity (INL) yang mendekatioptimal. Hasil yang didapatkan dari simulasi rangkaian DAC dengan jumlah bit 6 sampai 12 menunjukkanbahwa algoritma genetika dapat digunakan untuk mengoptimalkan linieritas rangkaian R-2R ladder DACdengan INL hingga di bawah 0.5 LSB dalam waktu kurang dari 1.5 menit.Kata Kunci: algoritma genetika, R-2R ladder, digital-to-analog converter, integral non-linearityAbstract - R-2R ladder digital-to-analog converter (DAC) is a simple electronic circuit that can beimplemented with only two resistor values. It is used for converting digital values to analog voltagedirectly. The resistor selection and placement in this circuit have a significant effect on the DAClinearity. This research aims to provide solution in designing R-2R ladder DAC circuit with close-tooptimumlinearity using only resistors available on the market. With the help of genetic algorithm, theexisting resistors can be sequenced in such a way to produce a DAC circuit with close-to-optimumintegral non-linearity (INL). The results obtained from the simulation using 6 to 12 bits DAC circuitsshow that the genetic algorithm can be used to optimize the linearity of R-2R ladder DAC with INLless than 0.5 LSB in less than 1.5 minutes.Keywords: genetic algorithm, R-2R ladder, digital-to-analog converter, integral non-linearity
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Prasetio, Rizki Tri, Ali Akbar Rismayadi, and Iedam Fardian Anshori. "Implementasi Algoritma Genetika pada k-nearest neighbours untuk Klasifikasi Kerusakan Tulang Belakang." Jurnal Informatika 5, no. 2 (September 29, 2018): 186–94. http://dx.doi.org/10.31311/ji.v5i2.4123.
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AbstrakKerusakan tulang belakang dialami oleh sekitar dua pertiga orang dewasa serta termasuk ke dalam penyakit yang paling umum kedua setelah sakit kepala. Klasifikasi gangguan tulang belakang sulit dilakukan karena membutuhkan radiologist untuk menganalisa citra Magnetic Resonance Imaging (MRI). Penggunaan Computer Aided Diagnosis (CAD) System dapat membantu radiologist untuk mendeteksi kelainan pada tulang belakang dengan lebih optimal. Dataset vertebral column memiliki tiga kelas sebagai klasifikasi penyakit kerusakan tulang belakang yaitu, herniated disk, spondylolisthesis dan kelas normal yang diambil berdasarkan hasil ekstraksi citra MRI. Dataset akan diolah dalam lima eksperimen berdasarkan validasi dataset menggunakan split validation dengan pembagian data training dan data testing yang bervariasi. Pada penelitian ini diusulkan implementasi algoritma genetika pada algoritma k-nearest neighbours untuk meningkatkan akurasi dari klasifikasi gangguan tulang belakang. Algoritma genetika digunakan untuk fitur seleksi dan optimasi parameter algoritma k-nearest neighbours. Hasil penelitian menunjukan bahwa metode yang diusulkan menghasilkan peningkatan yang signifikan dalam klasifikasi kerusakan pada tulang belakang. Metode yang diusulkan menghasilkan rata-rata akurasi sebesar 93% dari lima eksperimen. Hasil ini lebih baik dari algoritma k-nearest neighbours yang menghasilkan rata-rata akurasi hanya sebesar 82.54%. Kata kunci: algoritma genetika, k-nearest neighbours, kerusakan tulang belakang, vertebral AbstractSpinal disorder is experienced by about two-thirds of adults and is included in the second most common disease after headaches. Classification of spinal disorders is difficult because it requires a radiologist to analyze Magnetic Resonance Imaging (MRI) images. The use of Computer Aided Diagnosis (CAD) System can help radiologists to detect abnormalities in the spine more optimally. The vertebral column dataset has three classes as a classification of spinal disorders, namely, herniated disk, spondylolisthesis and normal classes taken based on MRI Image extraction. The dataset will be processed in five experiments based on dataset validation using split validation with various training data and testing data. In this study proposed the implementation of genetic algorithms in the k-nearest neighbors algorithm to improve the accuracy of the classification of spinal disorders. Genetic algorithms are used for algorithm feature selection and parameter optimization of k-nearest neighbors. The results showed that the proposed method produced a significant increase in the classification of spinal disorder. The proposed method produces an average accuracy of 93% from five experiments. This result is better than the k-nearest neighbors algorithm which produces an average accuracy of only 82.54%. Keywords: genetic algorithm, k-nearest neighbours, spinal disorder, vertebral column.
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Prasetio, Rizki Tri, Ali Akbar Rismayadi, and Iedam Fardian Anshori. "Implementasi Algoritma Genetika pada k-nearest neighbours untuk Klasifikasi Kerusakan Tulang Belakang." Jurnal Informatika 5, no. 2 (September 29, 2018): 186–94. http://dx.doi.org/10.31294/ji.v5i2.4123.
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AbstrakKerusakan tulang belakang dialami oleh sekitar dua pertiga orang dewasa serta termasuk ke dalam penyakit yang paling umum kedua setelah sakit kepala. Klasifikasi gangguan tulang belakang sulit dilakukan karena membutuhkan radiologist untuk menganalisa citra Magnetic Resonance Imaging (MRI). Penggunaan Computer Aided Diagnosis (CAD) System dapat membantu radiologist untuk mendeteksi kelainan pada tulang belakang dengan lebih optimal. Dataset vertebral column memiliki tiga kelas sebagai klasifikasi penyakit kerusakan tulang belakang yaitu, herniated disk, spondylolisthesis dan kelas normal yang diambil berdasarkan hasil ekstraksi citra MRI. Dataset akan diolah dalam lima eksperimen berdasarkan validasi dataset menggunakan split validation dengan pembagian data training dan data testing yang bervariasi. Pada penelitian ini diusulkan implementasi algoritma genetika pada algoritma k-nearest neighbours untuk meningkatkan akurasi dari klasifikasi gangguan tulang belakang. Algoritma genetika digunakan untuk fitur seleksi dan optimasi parameter algoritma k-nearest neighbours. Hasil penelitian menunjukan bahwa metode yang diusulkan menghasilkan peningkatan yang signifikan dalam klasifikasi kerusakan pada tulang belakang. Metode yang diusulkan menghasilkan rata-rata akurasi sebesar 93% dari lima eksperimen. Hasil ini lebih baik dari algoritma k-nearest neighbours yang menghasilkan rata-rata akurasi hanya sebesar 82.54%. Kata kunci: algoritma genetika, k-nearest neighbours, kerusakan tulang belakang, vertebral AbstractSpinal disorder is experienced by about two-thirds of adults and is included in the second most common disease after headaches. Classification of spinal disorders is difficult because it requires a radiologist to analyze Magnetic Resonance Imaging (MRI) images. The use of Computer Aided Diagnosis (CAD) System can help radiologists to detect abnormalities in the spine more optimally. The vertebral column dataset has three classes as a classification of spinal disorders, namely, herniated disk, spondylolisthesis and normal classes taken based on MRI Image extraction. The dataset will be processed in five experiments based on dataset validation using split validation with various training data and testing data. In this study proposed the implementation of genetic algorithms in the k-nearest neighbors algorithm to improve the accuracy of the classification of spinal disorders. Genetic algorithms are used for algorithm feature selection and parameter optimization of k-nearest neighbors. The results showed that the proposed method produced a significant increase in the classification of spinal disorder. The proposed method produces an average accuracy of 93% from five experiments. This result is better than the k-nearest neighbors algorithm which produces an average accuracy of only 82.54%. Keywords: genetic algorithm, k-nearest neighbours, spinal disorder, vertebral column.
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Dan Liu, Dan Liu, Shu-Wen Yao Dan Liu, Hai-Long Zhao Shu-Wen Yao, Xin Sui Hai-Long Zhao, Yong-Qi Guo Xin Sui, Mei-Ling Zheng Yong-Qi Guo, and Li Li Mei-Ling Zheng. "Research on Mutual Information Feature Selection Algorithm Based on Genetic Algorithm." 電腦學刊 33, no. 6 (December 2022): 131–41. http://dx.doi.org/10.53106/199115992022123306011.
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<p>Feature selection is an important part of data preprocessing. Feature selection algorithms that use mutual information as evaluation can effectively handle different types of data, so it has been widely used. However, the potential relationship between relevance and redundancy in the evaluation criteria is often ignored, so that effective feature subsets cannot be selected. Optimize the evaluation criteria of the mutual information feature selection algorithm and propose a mutual information feature selection algorithm based on dynamic penalty factors (Dynamic Penalty Factor Mutual Information Feature Selection Algorithm, DPMFS). The penalty factor is dynamically calculated with different selected features, so as to achieve a relative balance between relevance and redundancy, and effectively play the synergy between relevance and redundancy, and select a suitable feature subset. Experimental results verify that the DPMFS algorithm can effectively improve the classification accuracy of the feature selection algorithm. Compared with the traditional chi-square, MIM and MIFS feature selection algorithms, the average classification accuracy of the random forest classifier for the six standard datasets is increased by 3.73%, 3.51% and 2.44%, respectively.</p> <p> </p>
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Indra, Zulfahmi, and Subanar Subanar. "Optimasi Biaya Distribusi Rantai Pasok Tiga Tingkat dengan Menggunakan Algoritma Genetika Adaptif dan Terdistribusi." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 10, no. 1 (July 31, 2014): 189. http://dx.doi.org/10.22146/ijccs.6546.
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AbstrakManajemen rantai pasok merupakan hal yang penting. Inti utama dari manajemen rantai pasok adalah proses distribusi. Salah satu permasalahan distribusi adalah strategi keputusan dalam menentukan pengalokasian banyaknya produk yang harus dipindahkan mulai dari tingkat manufaktur hingga ke tingkat pelanggan. Penelitian ini melakukan optimasi rantai pasok tiga tingkat mulai dari manufaktur-distributor-gosir-retail. Adapun pendekatan yang dilakukan adalah algoritma genetika adaptif dan terdistribusi. Solusi berupa alokasi banyaknya produk yang dikirim pada setiap tingkat akan dimodelkan sebagai sebuah kromosom. Parameter genetika seperti jumlah kromosom dalam populasi, probabilitas crossover dan probabilitas mutasi akan secara adaptif berubah sesuai dengan kondisi populasi pada generasi tersebut. Dalam penelitian ini digunakan 3 sub populasi yang bisa melakukan pertukaran individu setiap saat sesuai dengan probabilitas migrasi. Adapun hasil penelitian yang dilakukan 30 kali untuk setiap perpaduan nilai parameter genetika menunjukkan bahwa nilai biaya terendah yang didapatkan adalah 80,910, yang terjadi pada probabilitas crossover 0.4, probabilitas mutasi 0.1, probabilitas migrasi 0.1 dan migration rate 0.1. Hasil yang diperoleh lebih baik daripada metode stepping stone yang mendapatkan biaya sebesar 89,825. Kata kunci— manajemen rantai pasok, rantai pasok tiga tingkat, algortima genetika adaptif, algoritma genetika terdistribusi. Abstract Supply chain management is critical in business area. The main core of supply chain management is the process of distribution. One issue is the distribution of decision strategies in determining the allocation of the number of products that must be moved from the level of the manufacture to the customer level. This study take optimization of three levels distribution from manufacture-distributor-wholeshale-retailer. The approach taken is adaptive and distributed genetic algorithm. Solution in the form of allocation of the number of products delivered at each level will be modeled as a chromosome. Genetic parameters such as the number of chromosomes in the population, crossover probability and adaptive mutation probability will change adaptively according to conditions on the population of that generation. This study used 3 sub-populations that exchange individuals at any time in accordance with the probability of migration. The results of research conducted 30 times for each value of the parameter genetic fusion showed that the lowest cost value obtained is 80,910, which occurs at the crossover probability 0.4, mutation probability 0.1, the probability of migration 0.1 and migration rate 0.1. This result has shown that adaptive and distributed genetic algorithm is better than stepping stone method that obtained 89,825. Keywords— management supply chain, three level supply chain, adaptive genetic algorithm, distributed genetic algorithm.
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Petoukhov, S., E. Petukhova, and V. Svirin. "Genetic alphabets, unitary matrixes and quantum-algorithmic genetics." IOP Conference Series: Materials Science and Engineering 489 (March 26, 2019): 012048. http://dx.doi.org/10.1088/1757-899x/489/1/012048.
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Patel, Roshni V., and Jignesh S. Patel. "Optimization of Linear Equations using Genetic Algorithms." Indian Journal of Applied Research 2, no. 3 (October 1, 2011): 56–58. http://dx.doi.org/10.15373/2249555x/dec2012/19.
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Lim, Siew Mooi, Abu Bakar Md Sultan, Md Nasir Sulaiman, Aida Mustapha, and K. Y. Leong. "Crossover and Mutation Operators of Genetic Algorithms." International Journal of Machine Learning and Computing 7, no. 1 (February 2017): 9–12. http://dx.doi.org/10.18178/ijmlc.2017.7.1.611.
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Shi, Jiahe. "Fourier Filtering Denoising Based on Genetic Algorithms." International Journal of Trend in Scientific Research and Development Volume-1, Issue-5 (August 31, 2017): 1142–62. http://dx.doi.org/10.31142/ijtsrd2420.
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Mathur, Y. P., and S. J. Nikam. "Optimal Reservoir Operation Policies Using Genetic Algorithm." International Journal of Engineering and Technology 1, no. 2 (2009): 184–87. http://dx.doi.org/10.7763/ijet.2009.v1.34.
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Ohn, Syng-Yup, and Seung-Do Chi. "Cancer Diagnosis System using Genetic Algorithm and Multi-boosting Classifier." Journal of the Korea Society for Simulation 20, no. 2 (June 30, 2011): 77–85. http://dx.doi.org/10.9709/jkss.2011.20.2.077.
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WANG Gaiyun, 王改云, 郭智超 GUO Zhichao, 路皓翔 LU Haoxiang, 陆家卓 LU Jiazhuo, and 张琦 ZHANG Qi. "融合遗传算法的多域值分块低照度图像增强算法." ACTA PHOTONICA SINICA 51, no. 4 (2022): 0410007. http://dx.doi.org/10.3788/gzxb20225104.0410007.
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Setiawanda, Yongky Budi, Muhammad Khulqi Rasyid, Muhammad Jauharul Ramadhan, and Anggit Dwi Hartanto. "Penerapan Algoritma Genetika dan Algoritma Ghost Framework pada Game Ms. Pacman." Creative Information Technology Journal 5, no. 3 (September 6, 2019): 174. http://dx.doi.org/10.24076/citec.2018v5i3.206.
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Pacman merupakan salah satu game klasik yang terkenal pada dekade 80-an dan masih sampai sekarang menarik untuk menjadi objek penelitian tentang penerapan berbagai konsep Artificial Intellegent (AI). Tujuan utama dari game ini adalah, mendapatkan poin sebanyak-banyaknya sambil menghidar dari kejaran ghost dan mendapatkan poin tambahan saat memakan ghost setelah mendapatkan pil super. Pergerakan masing-masing ghost ditentukan oleh algoritma DFS dan BFS yang diterapkan pada Ghost Framework. Algoritma tersebut sering digunakan untuk membandingkan algoritma lain dalam hal efektifitas. Penulis memanfaatkan Algoritma DFS dan BFS pada ghost digame Pacman untuk membandingkan keefektifan Algoritma Genetika (GA). Penulis menerapkan Algoritma Genetika pada player Pacman sebagai pengganti control. Terminal akan bertugas untuk mengambil nilai semua kemungkinan arah gerakan di setiap waktu selama permainan. Berdasarkan nilai-nilai ini pengontrol dapat mengontrol MS. Pacman melawan Ghost framework. Dengan desain ini, Penulis mengurangi kompleksitas solusi GA dengan menghapus semua control tindakan tugas manajemen dari system. Setelah melakukan beberapa pengujian, Penulis mendapatkan hasil dimana GA menghasilkan rerata score 8,330 yang lebih tinggi dibandingkan dengan player amatir dan kontroler lain. Dari hasil yang tersedia, Penulis menyimpulkan bahwa kinerja GA sebagai controller MS. Pacman dapat dikatakan baik.Kata Kunci — Algoritma Genetika, Pacman, Ghost FrameworkPacman is one of the famous classic games in the 80s and is still interesting to be the object of research on Artificial Intelligence (AI). The main goal of this game is to get as many points as possible while avoiding ghost chases and get extra points when eating ghost after getting super pills. The movement of each ghost is determined by the DFS and BFS algorithms that are applied to Ghost Framework. The algorithm is often used to compare other algorithms in terms of effectiveness. Writer use the DFS and BFS Algorithm on ghost in the Pacman game to compare the effectiveness of the Genetic (GA) Algorithm. The terminal will take the value of all possible direction of movement during the game. Based on these values the controller can control MS. Pacman against Ghost framework. With this design, Writer reduce the complexity of the GA solution by removing all management actions from the control system. After doing some testing, Writer get results where GA produces a higher score compared to other amateur players and controllers. From available results, Writer conclude that the performance of GA as a MS. Pacman controller is good.Keywords — Genetic Algorithm, Pacman, Ghost Framework
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Liang, W. Y., and Peter O'Grady. "Genetic algorithms for design for assembly: The remote constrained genetic algorithm." Computers & Industrial Engineering 33, no. 3-4 (December 1997): 593–96. http://dx.doi.org/10.1016/s0360-8352(97)00200-3.
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Arowolo, Micheal Olaolu, Marion O. Adebiyi, Ayodele A. Adebiyi, and Olatunji J. Okesola. "Predicting RNA-seq data using genetic algorithm and ensemble classification algorithms." Indonesian Journal of Electrical Engineering and Computer Science 21, no. 2 (February 1, 2021): 1073. http://dx.doi.org/10.11591/ijeecs.v21.i2.pp1073-1081.
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<p>Malaria parasites accept uncertain, inconsistent life span breeding through vectors of mosquitoes stratospheres. Thousands of different transcriptome parasites exist. A prevalent ribonucleic acid sequencing (RNA-seq) technique for gene expression has brought about enhanced identifications of genetical queries. Computation of RNA-seq gene expression data transcripts requires enhancements using analytical machine learning procedures. Numerous learning approaches have been adopted for analyzing and enhancing the performance of biological data and machines. In this study, a genetic algorithm dimensionality reduction technique is proposed to fetch relevant information from a huge dimensional RNA-seq dataset, and classification uses Ensemble classification algorithms. The experiment is performed using a mosquito Anopheles gambiae dataset with a classification accuracy of 81.7% and 88.3%.</p>
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Mursalim, Mursalim, Purwanto Purwanto, and M. Arief Soeleman. "Penentuan Centroid Awal Pada Algoritma K-Means Dengan Dynamic Artificial Chromosomes Genetic Algorithm Untuk Tuberculosis Dataset." Techno.Com 20, no. 1 (February 9, 2021): 97–108. http://dx.doi.org/10.33633/tc.v20i1.4230.
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Data merupakan hal penting diera sekarang begitu juga dengan metode data mining yang dapat mengekstraksi data menghasilkan informasi. Klastering 1 dari 5 peran data mining yang berfungsi untuk mengelompokkan data berdasarkan tingkat kemiripan dan jarak minimum. Algoritma K-Means termasuk algoritma yang populer banyak digunakan diberbagai bidang seperti bidang pendidikan, kesehatan, sosial, biologi, ilmu komputer. Seringkali metode K-Means dikombinasikan dengan metode optimasi seperti algoritma genetika untuk mengatasi permasalah pada K-Means yaitu sensitif dalam penentuan centroid awal .Namun metode algoritma genetika memiliki kekurangan yaitu mengalamai konvergen prematur sehingga hasil dari algorima genetika terjebak pada optimum lokal. Penelitian ini mengkombinasikan dynamic artificial cromosomes genetic algorithm dengan K-Means dalam menentukan nilai centroid awal pada k-means. Hasil eksperimen menunjukkan bahwa metode DAC GA + K-Means lebih unggul dibandingkan dengan K-Means dan GA + K-Means pada 2 dataset yang diuji dengan optimal nilai klaster sebanyak 2 dan 1 dataset sebanyak 3 klaster. Metode tersebut perolehan nilai DBI sebesar 0.138, 0.279 serta 0.382, nilai Sum Square Error sebesar 92.56, 332,39 dan 1280.68 serta nilai fitness yang tebentuk adalah 7.12, 3.57 dan 2.13.
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Nair, Dr Prabha Shreeraj. "Clustered Genetic Algorithm to solve Multidimensional Knapsack Problem." International Journal of Trend in Scientific Research and Development Volume-1, Issue-4 (June 30, 2017): 737–45. http://dx.doi.org/10.31142/ijtsrd2237.
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Suganthy, K. Beulah. "Identification of Disease in Leaves using Genetic Algorithm." International Journal of Trend in Scientific Research and Development Volume-3, Issue-3 (April 30, 2019): 1264–67. http://dx.doi.org/10.31142/ijtsrd22901.
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Kaur, Manpreet. "A Review on Pattern Recognition Using Genetic Algorithms." International journal of Emerging Trends in Science and Technology 04, no. 05 (June 2, 2017): 5213–20. http://dx.doi.org/10.18535/ijetst/v4i5.16.
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CHA, Joo-Heon, and In-Ho LEE. "Synthesis of Mechanical Structures Using a Genetic Algorithm." Proceedings of Design & Systems Conference 2004.14 (2004): 243–46. http://dx.doi.org/10.1299/jsmedsd.2004.14.243.
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Pornpanomchai, Chomtip, Verachad Wongsawangtham, Satheanpong Jeungudomporn, and Nannaphat Chatsumpun. "Thai Handwritten Character Recognition by Genetic Algorithm (THCRGA)." International Journal of Engineering and Technology 3, no. 2 (2011): 148–53. http://dx.doi.org/10.7763/ijet.2011.v3.214.
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Lakshmi, V. Rajya. "Optimization of Thinned Dipole Arrays Using Genetic Algorithm." International Journal of Engineering and Technology 3, no. 6 (2011): 658–62. http://dx.doi.org/10.7763/ijet.2011.v3.301.
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RAJALAKSHMI.M, RAJALAKSHMI M. "Software System Re-Modularization using Interactive Genetic Algorithm." Paripex - Indian Journal Of Research 3, no. 4 (January 15, 2012): 105–7. http://dx.doi.org/10.15373/22501991/apr2014/32.
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Rybickova, Ing Alena, Ing DenisaMockova Ing. DenisaMockova, and Ing Bc AdelaKaraskova Ing.Bc. AdelaKaraskova. "Application of Genetic Algorithm to The TsP Problem." Paripex - Indian Journal Of Research 3, no. 7 (January 1, 2012): 1–3. http://dx.doi.org/10.15373/22501991/july2014/35.
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Hackenberger, Branimir K. "Genetics without genes: application of genetic algorithms in medicine." Croatian Medical Journal 60, no. 2 (April 2019): 177–80. http://dx.doi.org/10.3325/cmj.2019.60.177.
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Wati, Vera, Yuliana Yuliana, Paradise Paradise, and Kusrini Kusrini. "TRAVELLING SALESMAN PROBLEM (TSP) OPTIMIZATION SEED DIS-TRIBUTION USING GENETIC ALGORITHM." JURTEKSI (Jurnal Teknologi dan Sistem Informasi) 8, no. 3 (August 18, 2022): 359–68. http://dx.doi.org/10.33330/jurteksi.v8i3.1738.
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Abstract: Distribution is an important the business sector, the agricultural sector for distributing seeds to ensure the location of customers selling seeds. Problems that are often encountered seed distribution process are the efficiency of the time and distance distribution. Re search will build software entering initial location data and several dynamically added consumer agents. The distance parameter uses latitude-longitude integrated on google maps and detects varying store locations, the generation of chromosomes or the best distribution path with the minimum distance route. The heuristic approach using the Genetic Algorithm imitates the concept of biological evolution of random exchange structure series. This study is to distribute 3 types of seeds with a choice of weights that have been divided into 3 areas located on the map of Indonesia using land routes. The results of the test of the population of the average fitness value tend to remain from the previous value of 1-10 the fitness value and the optimum iteration with 9-12 with an average fitness value of 44.2. Optimal results are obtained when Mr is higher than the Cr values. Thus, the Genetic Algorithm can be used for TSP seed distribution paths. 1:2 fitness evaluation compared with the usual estimates used . Keywords: Genetic Algorithm; Route Optimization; Seed Distribution; TSP Abstrak: Distribusi menjadi hal penting berwirausaha, salah satunya pada bidang pertanian untuk pendistribusian benih sampai lokasi tujuan. Permasalahan sering ditemui dalam proses pendistribusian adalah efektifan, efisiensi waktu dan jarak tempuh. Sehingga penelitian akan membangun perangkat lunak dengan memasukan data titik lokasi awal dan beberapa lokasi tujuan agen konsumen ditambahkan secara dinamis. Parameter jarak menggunakan latitude-longitude terintegrasi pada google maps yang mendeteksi keberadaan lokasi, selanjutnya diketahui generasi kromosom atau jalur distribusi terbaik dengan rute minimum. Pendekatan Heuristic menggunakan Algoritma Genetika meniru konsep evolusi biologis deretan struktur pertukaran informasi secara acak. Tujuan dalam penelitian ini dapat mendistribusikan jenis benih dengan pilihan bobot yang telah terbagi dalam wilayah lokasi. Satu wilayah lokasi terdapat beberapa lokasi toko ditambahkan secara dinamis, dengan proses yang sudah ditentukan titik awal keberangkatan. Penelitian ini menekankan pada proses penentuan rute lokasi saja. Hasil pengujian jumlah populasi rata-rata nilai fitness cenderung bersifat tetap dari nilai sebelumnya selisih 1-10 nilai fitness dan iterasi optimum dengan 9-12 dengan rata-rata nilai fitness 44,2. Hasil optimum didapatkan ketika Mutation rate (Mr) lebih tinggi dibanding nilai Crossover rate (Cr). Maka, Algoritma Genetika bisa digunakan untuk TSP jalur distribusi benih pengujian menghasilkan evaluasi fitnes 1:2 untuk Algoritma Genetika dibandingkan dengan estimasi jarak biasa digunakan.Kata kunci: Algoritma Genetika; Distribusi Benih; Optimalisasi Rute; TSP
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Aydın, Levent, Hatice Seçil Artem, and Melih Savran. "Optimization of Dimensionally Stable Laminated Composites by Using Genetic Algorithms." Afyon Kocatepe University Journal of Sciences and Engineering 17, no. 3 (December 1, 2017): 1136–45. http://dx.doi.org/10.5578/fmbd.64041.
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ÇETİN YAĞMUR, Ece, and Sercan YAĞMUR. "Feature Selection by Genetic Algorithm for Wind Power Prediction." Afyon Kocatepe University Journal of Sciences and Engineering 22, no. 5 (October 27, 2022): 1028–40. http://dx.doi.org/10.35414/akufemubid.1117779.
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Sürdürülebilir gelişim için yenilenebilir enerji kaynaklarına olan ihtiyaç her geçen gün artmaktadır. Bu kaynaklardan birisi de rüzgar enerjisidir. Rüzgarın stokastik yapısı nedeniyle rüzgar hızı ve rüzgar gücünün tahmini son yıllarda araştırmacılar tarafından oldukça ilgi çeken bir konu haline gelmiştir. Yapılan çalışmada Türkiye’de yer alan bir rüzgar türbini için 2018 yılı boyunca SCADA sistemi ile elde edilen veri seti ile aynı konum için NASA tarafından paylaşılan meteorolojik veri seti kullanılarak rüzgar gücü tahmini gerçekleştirilmiştir. Girdi değişkenleri olarak SCADA sisteminden çekilen rüzgar hızı, rüzgar yönü ve teorik güç eğrisi; NASA sisteminden çekilen meteorolojik parametreler ve rüzgar gücüne ait geçmiş veriler kullanılmıştır. Modelde yer alan ve hesaplama karmaşıklığına neden olan gereksiz öznitelikler model performansını artırmak amacıyla sarmal seçim yöntemi ile modelden çıkarılmıştır. Sarmal seçim yöntemi olarak Genetik Algoritma (GA) kullanılmıştır. Yapılan çalışmada hem farklı makine öğrenme algoritmalarının tahmin gücü, farklı performans ölçütlerine göre karşılaştırılmış hem de öznitelik seçiminin modele etkisi değerlendirilmiştir. GA ile önerilen nihai modelde değişken sayısı 47’den 9’a indirgenerek gereksiz değişkenler modelden uzaklaştırılmış ve en az sayıda değişken ile R2 değeri 0,98 olan güçlü bir tahmin modeli elde edilmiştir.
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Utami, Dwi Yuni, Elah Nurlelah, and Noer Hikmah. "Attribute Selection in Naive Bayes Algorithm Using Genetic Algorithms and Bagging for Prediction of Liver Disease." JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING 4, no. 1 (July 20, 2020): 76–85. http://dx.doi.org/10.31289/jite.v4i1.3793.
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Liver disease is an inflammatory disease of the liver and can cause the liver to be unable to function as usual and even cause death. According to WHO (World Health Organization) data, almost 1.2 million people per year, especially in Southeast Asia and Africa, have died from liver disease. The problem that usually occurs is the difficulty of recognizing liver disease early on, even when the disease has spread. This study aims to compare and evaluate Naive Bayes algorithm as a selected algorithm and Naive Bayes algorithm based on Genetic Algorithm (GA) and Bagging to find out which algorithm has a higher accuracy in predicting liver disease by processing a dataset taken from the UCI Machine Learning Repository database (GA). University of California Invene). From the results of testing by evaluating both the confusion matrix and the ROC curve, it was proven that the testing carried out by the Naive Bayes Optimization algorithm using Algortima Genetics and Bagging has a higher accuracy value than only using the Naive Bayes algorithm. The accuracy value for the Naive Bayes algorithm model is 66.66% and the accuracy value for the Naive Bayes model with attribute selection using Genetic Algorithms and Bagging is 72.02%. Based on this value, the difference in accuracy is 5.36%.Keywords: Liver Disease, Naïve Bayes, Genetic Agorithms, Bagging.