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 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.

Optimized Fuzzy Logic Control Strategy for Parallel Hybrid Electric Vehicle Based on Genetic Algorithm

2013 ◽  
Vol 274 ◽  
pp. 345-349 ◽  
Author(s):  
Mei Lan Zhou ◽  
Deng Ke Lu ◽  
Wei Min Li ◽  
Hui Feng Xu
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Electric Vehicle ◽  
Control Strategy ◽  
Fuzzy Logic Control ◽  
Hybrid Electric Vehicle ◽  
Fuzzy Rule ◽  
Rule Base ◽  
Membership Functions ◽  
Logic Control

For PHEV energy management, in this paper the author proposed an EMS is that based on the optimization of fuzzy logic control strategy. Because the membership functions of FLC and fuzzy rule base were obtained by the experience of experts or by designers through the experiment analysis, they could not make the FLC get the optimization results. Therefore, the author used genetic algorithm to optimize the membership functions of the FLC to further improve the vehicle performance. Finally, simulated and analyzed by using the electric vehicle software ADVISOR, the results indicated that the proposed strategy could easily control the engine and motor, ensured the balance between battery charge and discharge and as compared with electric assist control strategy, fuel consumption and exhaust emissions have also been reduced to less than 43.84%.

scholarly journals Speed control of brushless direct current motor using a genetic algorithm–optimized fuzzy proportional integral differential controller

2019 ◽  
Vol 11 (11) ◽  
pp. 168781401989019 ◽  
Author(s):  
Huangshui Hu ◽  
Tingting Wang ◽  
Siyuan Zhao ◽  
Chuhang Wang
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Direct Current ◽  
Fuzzy Logic Controller ◽  
Speed Control ◽  
Operating Conditions ◽  
Superior Performance ◽  
Membership Functions ◽  
Proportional Integral ◽  
Differential Type

In this article, a genetic algorithm–based proportional integral differential–type fuzzy logic controller for speed control of brushless direct current motors is presented to improve the performance of a conventional proportional integral differential controller and a fuzzy proportional integral differential controller, which consists of a genetic algorithm–based fuzzy gain tuner and a conventional proportional integral differential controller. The tuner is used to adjust the gain parameters of the conventional proportional integral differential controller by a new fuzzy logic controller. Different from the conventional fuzzy logic controller based on expert experience, the proposed fuzzy logic controller adaptively tunes the membership functions and control rules by using an improved genetic algorithm. Moreover, the genetic algorithm utilizes a novel reproduction operator combined with the fitness value and the Euclidean distance of individuals to optimize the shape of the membership functions and the contents of the rule base. The performance of the genetic algorithm–based proportional integral differential–type fuzzy logic controller is evaluated through extensive simulations under different operating conditions such as varying set speed, constant load, and varying load conditions in terms of overshoot, undershoot, settling time, recovery time, and steady-state error. The results show that the genetic algorithm–based proportional integral differential–type fuzzy logic controller has superior performance than the conventional proportional integral differential controller, gain tuned proportional integral differential controller, conventional fuzzy proportional integral differential controller, and scaling factor tuned fuzzy proportional integral differential controller.

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

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.

Chain Restaurant Work Scheduling Based on Genetic Algorithm with Fuzzy Logic

2006 ◽  
Vol 10 (1) ◽  
pp. 50-59 ◽  
Author(s):  
Makoto Watanabe ◽  
◽  
Hajime Nobuhara ◽  
Kazuhiko Kawamoto ◽  
Fangyan Dong ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Optimization Algorithm ◽  
Optimal Solution ◽  
Experimental Results ◽  
Membership Functions ◽  
A Chain ◽  
Professional Expert ◽  
Restaurant Work ◽  
Work Scheduling

A quasi-optimization algorithm to generate chain restaurant work scheduling (WS) is proposed based on a genetic algorithm with fuzzy logic, where the whole weekly chain restaurant WS problem is fuzzily decomposed into 7 daily WS problems and a combined weekly WS problem. The proposed algorithm expresses the requirements of individual members by membership functions in fuzzy logic and finds a near-optimal solution using the genetic algorithm. Experimental results verified that a 24-hour 7-day schedule for 15 workers at a chain restaurant is produced in 6 minutes using the proposed algorithm implemented with C++ and executed on a PC. A professional expert evaluated WS quality as satisfactory.

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.

Medical Image Thresholding Using Genetic Algorithm and Fuzzy Membership Functions

2019 ◽  
Vol 8 (4) ◽  
pp. 39-59
Author(s):  
Shashwati Mishra ◽  
Mrutyunjaya Panda
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Hybrid Technique ◽  
Membership Functions ◽  
Novel Approach ◽  
Fitness Value ◽  
Sensitivity Specificity

Thresholding is one of the important steps in image analysis process and used extensively in different image processing techniques. Medical image segmentation plays a very important role in surgery planning, identification of tumours, diagnosis of organs, etc. In this article, a novel approach for medical image segmentation is proposed using a hybrid technique of genetic algorithm and fuzzy logic. Fuzzy logic can handle uncertain and imprecise information. Genetic algorithms help in global optimization, gives good results in noisy environments and supports multi-objective optimization. Gaussian, trapezoidal and triangular membership functions are used separately for calculating the entropy and finding the fitness value. CPU time, Root Mean Square Error, sensitivity, specificity, and accuracy are calculated using the three membership functions separately at threshold levels 2, 3, 4, 5, 7 and 9. MRI images are considered for applying the proposed method and the results are analysed. The experimental results obtained prove the effectiveness and efficiency of the proposed method.

Application on Query of Distributed Database Based on Improved Genetic Algorithm

2014 ◽  
Vol 556-562 ◽  
pp. 4617-4621
Author(s):  
Fu Xing Chen ◽  
Xu Sheng Xie
Keyword(s):  
Genetic Algorithm ◽  
Clustering Algorithm ◽  
Search Algorithm ◽  
Random Search ◽  
Distributed Database ◽  
Improved Genetic Algorithm ◽  
Mutation Probability ◽  
Probabilistic Search ◽  
Crossover Probability ◽  
Crossover And Mutation

The query cost usually as an important criterion for a distributed database. The genetic algorithm is an adaptive probabilistic search algorithm, but the crossover and mutation probability usually keep a probability in traditional genetic algorithm. If the crossover probability keep a large value, the possibility of damage for genetic algorithm model is greater; In turn, if the crossover probability keep a small value, the search process will transform a slow processing or even stagnating. If the mutation probability keep a small value, a new individual can be reproduced difficultly; In turn, if the mutation probability keep a large value, the genetic algorithm will as a Pure random search algorithm. To solve this problem, proposed a improved genetic algorithm that multiple possibility of crossover and mutation based on k-means clustering algorithm. The experiment results indicate that the algorithm is effective.

scholarly journals Relevance Feedback using Genetic Algorithm on Information Retrieval for Indonesian Language Documents

2019 ◽  
Vol 5 (2) ◽  
pp. 171
Author(s):  
Salman Dziyaul Azmi ◽  
Retno Kusumaningrum
Keyword(s):  
Genetic Algorithm ◽  
Information Retrieval ◽  
Population Size ◽  
Relevance Feedback ◽  
Mean Average Precision ◽  
Average Precision ◽  
Mutation Probability ◽  
Average Recall ◽  
Crossover Probability ◽  
Standard Information

Background: The Rapid growth of technological developments in Indonesia had resulted in a growing amount of information. Therefore, a new information retrieval environment is necessary for finding documents that are in accordance with the user’s information needs.Objective: The purpose of this study is to uncover the differences between using Relevance Feedback (RF) with genetic algorithm and standard information retrieval systems without relevance feedback for the Indonesian language documents.Methods: The standard Information Retrieval (IR) System uses Sastrawi stemmer and Vector Space Model, while Genetic Algorithm-based (GA-based) relevance feedback uses Roulette-wheel selection and crossover recombination. The evaluation metrics are Mean Average Precision (MAP) and average recall based on user judgments.Results: By using two Indonesian language document datasets, namely abstract thesis and news dataset, the results show 15.2% and 28.6% increase in the corresponding MAP values for both datasets as opposed to the standard Information Retrieval System. A respective 7.1% and 10.5% improvement on the recall value at 10th position was also observed for both datasets. The best obtained genetic algorithm parameters for abstract thesis datasets were a population size of 20 with 0.7 crossover probability and 0.2 mutation probability, while for news dataset, the best obtained genetic algorithm parameters were a population size of 10 with 0.5 crossover probability and 0.2 mutation probability.Conclusion: Genetic Algorithm-based relevance feedback increases both values of MAP and average recall at 10th position of retrieved document. Generally, the best genetic algorithm parameters are as follows, mutation probability is 0.2, whereas the size of population size and crossover probability depends on the size of dataset and length of the query.Keywords: Genetic Algorithm, Information Retrieval, Indonesian language document, Mean Average Precision, Relevance Feedback 

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