scholarly journals The quasispecies regime for the simple genetic algorithm with roulette wheel selection

2017 ◽  
Vol 49 (3) ◽  
pp. 903-926 ◽  
Author(s):  
Raphaël Cerf
Keyword(s):  
Genetic Algorithm ◽  
Selection Mechanism ◽  
Roulette Wheel Selection ◽  
Mutation Probability ◽  
Roulette Wheel ◽  
Simple Genetic Algorithm ◽  
Crossover Probability ◽  
Mean Fitness ◽  
The Mean ◽  
Best Fit

Abstract We introduce a new parameter to discuss the behavior of a genetic algorithm. This parameter is the mean number of exact copies of the best-fit chromosomes from one generation to the next. We believe that the genetic algorithm operates best when this parameter is slightly larger than 1 and we prove two results supporting this belief. We consider the case of the simple genetic algorithm with the roulette wheel selection mechanism. We denote by ℓ the length of the chromosomes, m the population size, pC the crossover probability, and pM the mutation probability. Our results suggest that the mutation and crossover probabilities should be tuned so that, at each generation, the maximal fitness multiplied by (1 - pC)(1 - pM)ℓ is greater than the mean fitness.

Genetic Algorithm for Web-Based Food Stand Assignment Scheduling

2019 ◽  
Vol 5 (2) ◽  
pp. 104-108
Author(s):  
Sintya Oktaviani ◽  
Farica Perdana Putri ◽  
Dennis Gunawan
Keyword(s):  
Genetic Algorithm ◽  
Large Scale ◽  
Optimization Problem ◽  
Scale Problem ◽  
Web Based ◽  
Roulette Wheel Selection ◽  
Roulette Wheel ◽  
Best Value ◽  
Mean Fitness ◽  
Mathematical Techniques

Scheduling is a hard problem due to much considerations in many goals.  Combination of goals in this scheduling cause the problem hard to solve even when using mathematical techniques. Optimization is a method which aim to achieve the best result with the least cost as possible. Optimization for large scale problem usually done with more modern technic, such as metaheuristic. Genetic Algorithm belongs to a larger system called Evolutionary Algorithm which is often used for solving the best value in optimization problem. Hence, this food stand assignment scheduling is build using Genetic Algorithm with population size of 50, uniform crossover with crossover rate of 0.25, mutation rate of 0.0125, and roulette wheel selection. An interview was conducted with three coordinators of fund and consumtion that results in three constraints used in building this system. Testing is done for three events and achieve mean fitness that is 87.967%, 89.609%, and 85.001% for FesTIval, TechnoFest, and DISCO, respectively.

scholarly journals Aplikasi Pertimbangan Wisata di Pulau Lombok dengan Metode Fuzzy Mamdani & Algoritma Genetika

2020 ◽  
Vol 7 (6) ◽  
pp. 1261
Author(s):  
Zainul Harir ◽  
Ida Bagus Ketut Widiartha ◽  
Royana Afwani
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Growth Factor ◽  
Budget Allocation ◽  
Membership Functions ◽  
Mutation Probability ◽  
Interesting Culture ◽  
Crossover Probability ◽  
The Mean ◽  
Travel Decision

<p class="Abstrak">Pulau Lombok memiliki pariwisata berupa keindahan alam dan kebudayaan yang menarik, sehingga juga mendapat tiga penghargaan pada <em>World Halal Tourism Awards</em> 2016 dengan faktor pertumbuhan kunjungan wisatawan sebesar 13% pada tahun tersebut. Adanya sebuah aplikasi yang dapat membantu wisatawan dalam menentukan keputusan perjalanan wisata mereka adalah wajib. Aplikasi ini dikembangkan dengan logika <em>Fuzzy</em> Mamdani dan Algoritma Genetika dengan tujuan memberikan rekomendasi pariwisata.Logika <em>Fuzzy</em> Mamdani memberikan pertimbangan wisata berdasarkan 5 parameter (anggaran, rencana perjalanan, akomodasi, makanan dan minuman, serta biaya transportasi) yang kemudian menjadi 5 fungsi keanggotaan untuk membangun kombinasi aturan pada fuzzy dan menghasilkan keluaran berupa pertimbangan wisata, yaitu: Tidak Memungkinkan, Cukup Memungkinkan, dan Memungkinkan. Kombinasi lima fungsi keanggotaan tersebut, menghasilkan 10.080 aturan, yang digunakan untuk mengetahui seseorang memungkinkan, atau tidak untuk berwisata ke pulau Lombok dengan <em>constrain</em> parameter yang dimiliki, yang dibangkitkan dengan menggunakan fungsi Defuzzifikasi <em>Mean of Max</em> (MOM). Algortima Genetika digunakan dalam memberikan alokasi penggunaan budget yang optimal dalam berwisata di Pulau Lombok.Hasil pengujian dengan perhitungan manual dan model defuzzifikasi yang berbeda memiliki akurasi 100%.  Untuk implementasi Algoritma Genetika, aplikasi memperoleh alokasi anggaran optimal pada <em>probabilitas crossover</em> (pc) dan probabilitas mutasi (pm) dengan (pc) 0,7 dan (pm) 0,2.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Abstract"><em>Tourism in Lombok has an interesting culture, it makes Lombok got three awards at the 2016 World Halal Tourism Awards and became a growth factor for tourist visits by 13% that year. An application that can help tourists in determining travel decision is mandatory.The application developed with Mamdani Fuzzy Logic and Genetic Algorithm to provide tourism recommendations. The Fuzzy Mamdani Logic Method provides tourism considerations based on 5 parameters (budget, travel plans, accommodation, food and drinks, and transportation costs) which then become 5 membership functions to build a combination of rules on fuzzy and produce output in the form of tourism's considerations: Not Enable, Enough Enable, and Enable. The combination of the 5 membership functions constructed 10.080 fuzzy rules, that's used to know wheater tourists enables them to go to Lombok with the limitation that they have. The defuzzification used is the Mean of Max (MOM). Genetic Algorithm (GA) is used in providing optimal budget allocation in traveling on Lombok IslandThe results of testing with manual calculations and different defuzzification models have 100% accurate, the application of GA obtained optimal budget allocation on crossover probability (pc) and mutation probability (pm) combination with (pc) 0.7 and (pm) 0.2.</em></p><p class="Abstrak"><em><strong><br /></strong></em></p>

scholarly journals PENERAPAN TEKNIK KLASIFIKASI PADA SISTEM REKOMENDASI MENGGUNAKAN ALGORITMA GENETIKA

2016 ◽  
Vol 2 (3) ◽  
Author(s):  
Rita Rismala ◽  
Mahmud Dwi Sulistiyo
Keyword(s):  
Data Mining ◽  
Genetic Algorithm ◽  
Recommendation System ◽  
Classification Model ◽  
Mutation Probability ◽  
Linear Classifier ◽  
Average Accuracy ◽  
Crossover Probability ◽  
Best Parameter

[Id]Sistem rekomendasi yang dibangun dalam penelitian ini adalah sistem rekomendasi yang dapat memberikan rekomendasi sebuah item terbaik kepada user. Dari sisi data mining, pembangunan sistem rekomendasi satu item ini dapat dipandang sebagai upaya untuk membangun sebuah model classifier yang dapat digunakan untuk mengelompokkan data ke dalam satu kelas tertentu. Model classifier yang digunakan bersifat linier. Untuk menghasilkan konfigurasi model classifier yang optimal digunakan Algoritma Genetika (AG). Performansi AG dalam melakukan optimasi pada model klasifikasi linier yang digunakan cukup dapat diterima. Untuk dataset yang digunakan dengan kombinasi nilai parameter terbaik yaitu yaitu ukuran populasi 50, probabilitas crossover 0.7, dan probabilitas mutasi 0.1, diperoleh rata-rata akurasi sebesar 72.80% dengan rata-rata waktu proses 6.04 detik, sehingga penerapan teknik klasifikasi menggunakan AG dapat menjadi solusi alternatif dalam membangun sebuah sistem rekomendasi, namun dengan tetap memperhatikan pengaturan nilai parameter yang sesuai dengan permasalahan yang dihadapi.Kata kunci:sistem rekomendasi, klasifikasi, Algoritma Genetika[En]In this study was developed a recommendation system that can recommend top-one item to a user. In terms of data mining, it can be seen as a problem to develop a classifier model that can be used to classify data into one particular class. The model used was a linear classifier. To produce the optimal configuration of classifier model was used Genetic Algorithm (GA). GA performance in optimizing the linear classification model was acceptable. Using the case study dataset and combination of the best parameter value, namely population size 50, crossover probability 0.7 and mutation probability 0.1, obtained average accuracy 72.80% and average processing time of 6.04 seconds, so that the implementation of classification techniques using GA can be an alternative solution in developing a recommender system, due regard to setting the parameter value depend on the encountered problem.Keywords:Recommendation system, classification, Genetic Algorithm

An Effective Optimization Method Based on the Genetic Algorithm to Solve TSP

2013 ◽  
Vol 380-384 ◽  
pp. 1454-1459 ◽  
Author(s):  
Xi Tao Hu ◽  
Jun Huang ◽  
Shun Xiang Wu
Keyword(s):  
Genetic Algorithm ◽  
Optimization Method ◽  
Selection Strategy ◽  
Search Process ◽  
Computational Mathematics ◽  
Roulette Wheel Selection ◽  
Roulette Wheel ◽  
Best Value ◽  
Mutation Strategy ◽  
The Traveling Salesman Problem

The Traveling Salesman Problem is one of the most intensively studied problems in computational mathematics. Due to the basic genetic algorithm convergence speed is slow, easy to stagnation. We present in this paper a new improved mutation strategy to solve this problem. Roulette wheel selection strategy is used to avoid running into trap of the part best value. And simulated niche method is introduced to accelerate the search process effetively. This algorithm has been checked on a set of 144 cities in China and it outperforms the results obtained with other TSP heuristic methods.

Research on GA-ANN Integration and Its Applications to Cold Extrusion Process Design

2006 ◽  
Vol 532-533 ◽  
pp. 897-900
Author(s):  
Ting Wei Ji ◽  
Jun Gao ◽  
Guo Qun Zhao ◽  
Cheng Rui Zhang
Keyword(s):  
Genetic Algorithm ◽  
Process Design ◽  
Binary Code ◽  
Fitness Function ◽  
Extrusion Process ◽  
Selection Method ◽  
Cold Extrusion ◽  
Design System ◽  
Roulette Wheel Selection ◽  
Roulette Wheel

Based on the research of the functions of ANN-based cold extrusion process design system, genetic algorithm (GA) is proposed to optimize the topology and parameters of artificial neural networks (ANN), in order to improve the running efficiency of the networks. The binary encoding approach is implemented to represent the GA chromosome. The code string or the chromosome was divided into three parts: the first part is the binary code of the cold extruded part; the second part is the binary code of the topology and parameters of ANN; the last is the binary code of the semi-cold-extruded-part or the billet. The 1/F(X) function is selected as the fitness function in GA, where, X represents the binary code of the cold extruded part, F(X) represents the error between the real outputs of ANN and the desired results; the biased roulette wheel selection method is used for selecting operation in this paper; two-point crossover and one-point mutation are selected for these two types of genetic operations. Finally, the typical cold extruded part is used for verification as an example by using the optimized ANN, the result shows that ANN optimized by GA has efficiency and validity in the cold extrusion process design system.

scholarly journals SCHEDULING USING GENETIC ALGORITHM AND ROULETTE WHEEL SELECTION METHOD CONSIDERING LECTURER TIME

2019 ◽  
Vol 2 (1) ◽  
pp. 24
Author(s):  
Herman Herman ◽  
Lukman Syafie ◽  
Irawati Irawati ◽  
Lilis Nur Hayati ◽  
Harlinda Harlinda
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Computer Science ◽  
Selection Method ◽  
Scheduling Problems ◽  
Scheduling System ◽  
Roulette Wheel Selection ◽  
Roulette Wheel

Scheduling lectures is not something easy, considering many factors that must be considered. The factors that must be considered are the courses that will be held, the space available, the lecturers, the suitability of the credits with the duration of courses, the availability of lecturers' time, and so on. One algorithm in the field of computer science that can be used in lecture scheduling automation is Genetic Algorithms. Genetic Algorithms can provide the best solution from several solutions in handling scheduling problems and the selksi method used is roulette wheel. This study produces a scheduling system that can work automatically or independently which can produce optimal lecture schedules by applying Genetic Algorithms. Based on the results of testing, the resulting system can schedule lectures correctly and consider the time of lecturers. In this study, the roulette wheel selection method was more effective in producing the best individuals than the rank selection method.

scholarly journals Optimasi Biaya Distribusi Rantai Pasok Tiga Tingkat dengan Menggunakan Algoritma Genetika Adaptif dan Terdistribusi

2014 ◽  
Vol 10 (1) ◽  
pp. 189
Author(s):  
Zulfahmi Indra ◽  
Subanar Subanar
Keyword(s):  
Genetic Algorithm ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Migration Rate ◽  
Adaptive Genetic Algorithm ◽  
Chain Management ◽  
Stepping Stone ◽  
Mutation Probability ◽  
Crossover Probability ◽  
Distributed Genetic Algorithm

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.

scholarly journals A Parameter Adaptive Genetic Algorithm Based Service Compositions

2018 ◽  
Vol 173 ◽  
pp. 03051
Author(s):  
Huizhou Yang ◽  
Li Zhang
Keyword(s):  
Genetic Algorithm ◽  
Service Composition ◽  
Adaptive Genetic Algorithm ◽  
Multi Objective ◽  
Mutation Probability ◽  
Adaptive Parameter ◽  
Main Challenge ◽  
Composition Problem ◽  
Crossover Probability ◽  
Parameter Adaptive

How to select and combine many services with similar functions reasonably and efficiently to provide users with better service is the main challenge in the service composition problem. This is thorny when the number of the candidate Services is huge. Recently, researches transform the service compositions problem as a multi-objective optimizing task, and then the genetic algorithm is commonly used to tackle this issue. However, the fixed crossover probability and mutation probability settings in genetic algorithm usually result to it falls into a local optimal. To improve the performance of the genetic algorithm in the service composition task, this paper proposes an adaptive parameter adjust strategy, which can adjust the crossover probability and mutation probability automatically. The experiment result shows our method has greatly improved the maximum fitness of the final solutions of traditional genetic algorithm.

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