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Zatsarynin, Serhii. "Market Segmentation of Innovative Products Using Genetic Algoritms." Marketing and Digital Technologies 5, no. 2 (2021): 67–74. http://dx.doi.org/10.15276/mdt.5.2.2021.6.
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One way to increase the company's competitiveness is to find new market niches. The market niche is the result of innovations that stimulate hidden, potential demand, as a result of which the company, developing a new market, avoids intense competition and receives a higher rate of return. It is proved that the growing number and complexity of tasks in the field of marketing research, working with a large amount of information, leads to the need to group data. The aim of the study is to develop a universal approach to solving the problem of market segmentation of innovative products based on a combination of genetic algorithm with traditional clustering methods. An ideal market niche can be defined as a compact and isolated series of points, which in some space of characteristics are objects or data elements. The selection of a market niche in the medical equipment market is carried out using a top-down approach. This approach implies the traditional segmentation of customers, which is carried out in the following order: segmentation, segment selection, positioning. It is believed that segmentation is the starting point for the formation of a market niche. To segment the medical equipment market, it is proposed to use cluster analysis methods. According to the results of the analysis, it can be seen that the market segments of potential consumers of medical equipment and consumables of Siemens in Ukraine are characterized by a fairly dense grouping of images of consumers around the center of its cluster in the space of features. The presented genetic clustering algorithm is flexible in relation to the decision-making process, as it allows to perform clustering based on various criteria, such as maximum mutual removal of clusters, proximity of geometric images of objects to the center of the cluster, other criteria. This is achieved by changing the calculation formula of the fitness function, which takes into account the necessary combination of clustering criteria without changing the structure of the algorithm. The algorithm is insensitive to initialization, as in the process of evolution of chromosomes through the use of genetic operators, the algorithm completely covers the whole set of acceptable solutions, which, in turn, provides high quality market segmentation.
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Sherin Mary Andrews. "Emerging Role of Artificial Intelligence and Machine learning in precision medicine." international journal of engineering technology and management sciences 7, no. 4 (2023): 622–26. http://dx.doi.org/10.46647/ijetms.2023.v07i04.086.
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Precision medicine is a new discipline that customizes medical interventions and therapies to each patient based on their particular genetic, environmental, and lifestyle factors. Techniques in machine learning (ML) and artificial intelligence (AI) have become effective research tools in precision medicine. This study examines how ML and AI can be used to diagnose diseases, choose the best course of treatment, predictic prognosis and find new drugs, among other precision medicine applications.It also analyzes the algoritms that is used. It discusses the advantages, difficulties, and potential applications of ML and AI in precision medicine
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Lingga, Sri Wahyuni, Sutarman Sutarman, and Open Darnius. "Modelling of Subject Scheduling Systems Using Hybrid Artificial Bee Colony Algorithm." Sinkron 7, no. 3 (2023): 1599–608. http://dx.doi.org/10.33395/sinkron.v7i3.12560.
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A common schedule problem found in colleges is the positioning of courses in a certain space and time. This placement process often encounters barriers that must be met so that there is no imbalance in the school schedule. One of the problems that often arise is the placement of class capacity that does not match the course requirements. In this study, the researchers used the Artificial Bee Colony Hybrid Algorithm (HABC) to construct course schedules efficiently at the college. The objective of the research was to develop a course scheduling system using the HABC algorithm by combining the Engineering of Artificial Bee Colony (ABC) and genetic algoritms, especially on the crossover process to better address the schedule problems. The research procedure used is to design and implement a course scheduling system using the Hybrid ABC algorithm. The results of the research demonstrate that the Hybrid ABC algorithm is effective in generating optimal course schedule schedules, in line with time limits, room needs, and lecturer requirements and can automate course schedule processes, saving time and resources, while ensuring optimal schedules.
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Dzidolikaitė, Agnė. "GENETIC ALGORITHMS FOR MULTIDIMENSIONAL SCALING / GENETINIŲ ALGORITMŲ TAIKYMAS DAUGIAMATĖMS SKALĖMS." Mokslas – Lietuvos ateitis 7, no. 3 (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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Mendaña-Cuervo, C., and E. López-González. "La Gestión Presupuestaria de Distribución con un Algoritmo Genético Borroso." Información Tecnológica 16, no. 3 (2005): 45–56. https://doi.org/10.4067/S0718-07642005000300007.
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<strong>Resumen</strong> En este trabajo se presenta el diseño de un sistema de información para la toma de decisiones presupuestarias. Al tratarse de una decisión que afecta al futuro, este problema se caracteriza por la incertidumbre y no linealidad de la información disponible para su resolución. Además, el elevado número de variables que intervienen lo convierten en un problema de gran complejidad. El tratamiento de la incertidumbre se ha abordado con la aplicación de la Teoría de los Subconjuntos Borrosos y el desarrollo operativo se ha abordado utilizando Algoritmos Genéticos. El resultado del trabajo es la implementación de un Algoritmo Genético Borroso que facilita la gestión presupuestaria de distribución, incluyéndose un ejemplo de aplicación que permite contrastar su validez y operatividad. <strong>Abstract</strong> In this paper, the design of an information system for budget decision is presented. Being this a decision that affects the future, the problem is characterized by the uncertainty and not linearity of the information available for its resolution. Also, the high number of variables that are involved makes this a highly complex problem. The uncertainty has been treated using the Fuzzy Sets Theory and the operative development has been treated using Genetic Algorithms. The result of the work is the implementation of a Fuzzy Genetic Algorithm that facilitates distribution budget management, including an example of application that permits to verify its validity and operability.
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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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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 (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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Sumida, Brian. "Genetics for genetic algorithms." ACM SIGBIO Newsletter 12, no. 2 (1992): 44–46. http://dx.doi.org/10.1145/130686.130694.
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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 (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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Mubarok, Muhammad Iqbal, Icih Sukarsih, and Yurika Permanasari. "Analisis Panjang Populasi dan Banyak Generasi Algoritma Genetika pada Traveling Salesman Problem." Bandung Conference Series: Mathematics 3, no. 2 (2023): 184–91. http://dx.doi.org/10.29313/bcsm.v3i2.9467.
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Abstrak. Traveling Salesman Problem (TSP) adalah masalah optimasi yang penting dalam bidang ilmu komputer dan matematika. Tujuan utama dari TSP adalah mencari rute terpendek yang melibatkan kunjungan ke sejumlah titik atau kota tertentu oleh seorang salesman. Algoritma Genetika (AG) telah menjadi salah satu pendekatan populer dalam menyelesaikan Traveling Salesman Problem karena kemampuannya untuk menghasilkan solusi yang mendekati optimum. Pada penelitian ini, dilakukan analisis mengenai panjang populasi dan banyak generasi pada Algoritma Genetika dalam menyelesaikan Traveling Salesman Problem. Tujuan penelitian ini adalah untuk menganalisis pengaruh kedua parameter tersebut terhadap kinerja Algoritma Genetika dalam mencapai solusi yang mendekati optimal. Digunakan studi kasus pendistribusian suatu produk UMKM (Usaha Mikro Kecil dan Menengah) di Bandung. Evaluasi dilakukan berdasarkan panjang rute terpendek yang ditemukan oleh Algoritma Genetika dalam jumlah iterasi tertentu. Hasil eksperimen menunjukkan bahwa dengan peningkatan panjang populasi pada Algoritma Genetika dapat meningkatkan kemampuan algoritma untuk menemukan solusi yang lebih baik. Dengan jumlah individu dalam populasi yang lebih besar, algoritma memiliki lebih banyak kesempatan untuk menjelajahi ruang solusi dan menemukan rute terpendek yang memenuhi kriteria Traveling Salesman Problem.
 Abstract. The Traveling Salesman Problem (TSP) is an important optimization problem in the fields of computer science and mathematics. Its main objective is to find the shortest route that involves visiting a specific set of points or cities by a salesman. Genetic Algorithms (GA) have become a popular approach in solving the Traveling Salesman Problem due to their ability to generate solutions that approximate optimality. In this study, an analysis was conducted on the population size and number of generations in the Genetic Algorithm for solving the Traveling Salesman Problem. The aim of this research was to analyze the influence of these two parameters on the performance of the genetic algorithm in achieving near-optimal solutions. A case study was conducted on the distribution of a small and medium-sized enterprise (SME) product in Bandung. The evaluation was based on the shortest route length discovered by the the Genetic Algorithms within a specified number of iterations. The experimental results indicated that increasing the population size in the Genetic Algorithms can enhance the algorithm's ability to find better solutions. With a larger number of individuals in the population, the algorithm had more opportunities to explore the solution space and discover the shortest routes that met the the Traveling Salesman Problem criteria.
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Rizky Fatih Syahputra and Yahfizham Yahfizham. "Menganalisis Konsep Dasar Algoritma Genetika." Bhinneka: Jurnal Bintang Pendidikan dan Bahasa 2, no. 1 (2023): 120–32. http://dx.doi.org/10.59024/bhinneka.v2i1.643.
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Genetic algorithms are computer techniques inspired by the theory of evolution and genetics. Individual definition, chromosome initialization, chromosome testing, selection (crossover) and mutation are fundamental elements of genetic algorithms. Genetic algorithms are used to solve optimization problems, such as lesson planning, community services and traffic light adjustment. By producing the best combination of chromosomes, the genetic algorithm can achieve ideal results. The genetic algorithm produces appropriate planning data to avoid delays. This research uses the methods of data collection, individual definition and chromosome initialization. The result of this research is a service application designed to be able to plan efficiently through development using a genetic algorithm. Optimal planning occurs when processing planning data produces solutions that are efficient in terms of time, energy, and resources, and avoids conflicting schedules in the same place..
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Raol, Jitendra R., and Abhijit Jalisatgi. "From genetics to genetic algorithms." Resonance 1, no. 8 (1996): 43–54. http://dx.doi.org/10.1007/bf02837022.
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Nico, Nico, Novrido Charibaldi, and Yuli Fauziah. "Comparison of Memetic Algorithm and Genetic Algorithm on Nurse Picket Scheduling at Public Health Center." International Journal of Artificial Intelligence & Robotics (IJAIR) 4, no. 1 (2022): 9–23. http://dx.doi.org/10.25139/ijair.v4i1.4323.
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 One of the most significant aspects of the working world is the concept of a picket schedule. It is difficult for the scheduler to make an archive since there are frequently many issues with the picket schedule. These issues include schedule clashes, requests for leave, and trading schedules. Evolutionary algorithms have been successful in solving a wide variety of scheduling issues. Evolutionary algorithms are very susceptible to data convergence. But no one has discussed where to start from, where the data converges from making schedules using evolutionary algorithms. The best algorithms among evolutionary algorithms for scheduling are genetic algorithms and memetics algorithms. When it comes to the two algorithms, using genetic algorithms or memetics algorithms may not always offer the optimum outcomes in every situation. Therefore, it is necessary to compare the genetic algorithm and the algorithm's memetic algorithm to determine which one is suitable for the nurse picket schedule. From the results of this study, the memetic algorithm is better than the genetic algorithm in making picket schedules. The memetic algorithm with a population of 10000 and a generation of 5000 does not produce convergent data. While for the genetic algorithm, when the population is 5000 and the generation is 50, the data convergence starts. For accuracy, the memetic algorithm violates only 24 of the 124 existing constraints (80,645%). The genetic algorithm violates 27 of the 124 constraints (78,225%). The average runtime used to generate optimal data using the memetic algorithm takes 20.935592 seconds. For the genetic algorithm, it takes longer, as much as 53.951508 seconds.
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EZZIANE, ZOHEIR. "Solving the 0/1 knapsack problem using an adaptive genetic algorithm." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 16, no. 1 (2002): 23–30. http://dx.doi.org/10.1017/s0890060401020030.
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Probabilistic and stochastic algorithms have been used to solve many hard optimization problems since they can provide solutions to problems where often standard algorithms have failed. These algorithms basically search through a space of potential solutions using randomness as a major factor to make decisions. In this research, the knapsack problem (optimization problem) is solved using a genetic algorithm approach. Subsequently, comparisons are made with a greedy method and a heuristic algorithm. The knapsack problem is recognized to be NP-hard. Genetic algorithms are among search procedures based on natural selection and natural genetics. They randomly create an initial population of individuals. Then, they use genetic operators to yield new offspring. In this research, a genetic algorithm is used to solve the 0/1 knapsack problem. Special consideration is given to the penalty function where constant and self-adaptive penalty functions are adopted.
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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 (2017): 217–23. http://dx.doi.org/10.31142/ijtsrd5905.
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Srinath Murthy, Ahana, and Dattatreya P Mankame. "Genetic Algorithms - A Brief Study." International Journal of Science and Research (IJSR) 13, no. 7 (2024): 1195–200. http://dx.doi.org/10.21275/sr24721004409.
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Kanwal, Maxinder S., Avinash S. Ramesh, and Lauren A. Huang. "A novel pseudoderivative-based mutation operator for real-coded adaptive genetic algorithms." F1000Research 2 (November 19, 2013): 139. http://dx.doi.org/10.12688/f1000research.2-139.v2.
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Recent development of large databases, especially those in genetics and proteomics, is pushing the development of novel computational algorithms that implement rapid and accurate search strategies. One successful approach has been to use artificial intelligence and methods, including pattern recognition (e.g. neural networks) and optimization techniques (e.g. genetic algorithms). The focus of this paper is on optimizing the design of genetic algorithms by using an adaptive mutation rate that is derived from comparing the fitness values of successive generations. We propose a novel pseudoderivative-based mutation rate operator designed to allow a genetic algorithm to escape local optima and successfully continue to the global optimum. Once proven successful, this algorithm can be implemented to solve real problems in neurology and bioinformatics. As a first step towards this goal, we tested our algorithm on two 3-dimensional surfaces with multiple local optima, but only one global optimum, as well as on the N-queens problem, an applied problem in which the function that maps the curve is implicit. For all tests, the adaptive mutation rate allowed the genetic algorithm to find the global optimal solution, performing significantly better than other search methods, including genetic algorithms that implement fixed mutation rates.
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Kallab, Chadi, Samir Haddad, and Jinane Sayah. "Flexible Traceable Generic Genetic Algorithm." Open Journal of Applied Sciences 12, no. 06 (2022): 877–91. http://dx.doi.org/10.4236/ojapps.2022.126060.
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Neville, Melvin, and Anaika Sibley. "Developing a generic genetic algorithm." ACM SIGAda Ada Letters XXIII, no. 1 (2003): 45–52. http://dx.doi.org/10.1145/1066404.589462.
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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 (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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Megson, G. M., and I. M. Bland. "Generic systolic array for genetic algorithms." IEE Proceedings - Computers and Digital Techniques 144, no. 2 (1997): 107. http://dx.doi.org/10.1049/ip-cdt:19971126.
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Hikmawan, Sisferi. "Algoritma Genetika dengan Mutasi Terbatas untuk Penjadwalan Perkuliahan." Jurnal Kajian Ilmiah 21, no. 2 (2021): 229–42. http://dx.doi.org/10.31599/jki.v21i2.520.
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Abstract
 
 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
 
 Abstrak
 
 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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Agapie, Alexandru. "Theoretical Analysis of Mutation-Adaptive Evolutionary Algorithms." Evolutionary Computation 9, no. 2 (2001): 127–46. http://dx.doi.org/10.1162/106365601750190370.
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Adaptive evolutionary algorithms require a more sophisticated modeling than their static-parameter counterparts. Taking into account the current population is not enough when implementing parameter-adaptation rules based on success rates (evolution strategies) or on premature convergence (genetic algorithms). Instead of Markov chains, we use random systems with complete connections - accounting for a complete, rather than recent, history of the algorithm's evolution. Under the new paradigm, we analyze the convergence of several mutation-adaptive algorithms: a binary genetic algorithm, the 1/5 success rule evolution strategy, a continuous, respectively a dynamic (1+1) evolutionary algorithm.
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Borrero Guerrero, H., and A. Delgado Rivera. "Evolución de chip ADN emulado con algoritmo genético en FPGA para control de navegación de un robot móvil." Orinoquia 12, no. 1 (2008): 117–29. http://dx.doi.org/10.22579/20112629.96.
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Titulo en ingles: EDNA chip evolution emulated with genetic algorithm in FPGA for controlling mobile robot navigationRESUMEN: Los chips ADN constituyen una herramienta importante en biología y medicina porque ofrecen paralelismo así como memoria asociativa, características que optimizan la identificación del genoma y el diagnóstico de enfermedades, entre otros. La apropiación del concepto de chip ADN y el uso de los dispositivos electrónicos reconfigurables, genera el chip ADN emulado electrónicamente, capaz de procesar información en paralelo y acceder contenidos en memoria por asociación.La utilización de algoritmos genéticos extiende las capacidades propuestas en el chip ADN emulado, sumándole un nivel de aprendizaje. La funcionalidad de un chip ADN emulado entrenado por medio de un algoritmo genético, se demuestra revisando la capacidad de aprendizaje de un robot móvil de tracción diferencial para navegar en un espacio cambiante evitando colisiones.Palabras clave: chip ADN, emulación electrónica, algoritmo genético, sistema clasificador.SUMMARY: DNA chips represent an important tool in biology and medicine as they offer parallelism as well as asso- ciative memory, such characteristics optimising genome identification and diagnosing diseases. Appropri-ating DNA chip concept and using reconfigurable electronic devices produces an electronically-emulatedDNA chip able to process information in parallel and access memory content by association.Using genetic algorithms extends emulated DNA chip’s proposed capacity, thereby adding on a level of learning. A genetic algorithm-trained emulated DNA chip’s functionality can be demonstrated by reviewing a differential traction mobile robot’s learning ability in terms of navigating in a changing space and avoiding collisions.Key words: DNA chip, electronic emulation, genetic algorithm, classifier system.
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Ankita, Ankita, and Rakesh Kumar. "Hybrid Simulated Annealing: An Efficient Optimization Technique." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 7s (2023): 45–53. http://dx.doi.org/10.17762/ijritcc.v11i7s.6975.
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Genetic Algorithm falls under the category of evolutionary algorithm that follows the principles of natural selection and genetics, where the best adapted individuals in a population are more likely to survive and reproduce, passing on their advantageous traits to their offsprings. Crossover is a crucial operator in genetic algorithms as it allows the genetic material of two or more individuals in the population to combine and create new individuals. Optimizing it can potentially lead to better solutions and faster convergence of the genetic algorithm. The proposed crossover operator gradually changes the alpha value as the search proceeds, similar to the temperature in simulated annealing. The performance of the proposed crossover operator is compared with the simple arithmetic crossover operator. The experiments are conducted using Python and results show that the proposed crossover operator outperforms the simple arithmetic crossover operator. This paper also emphasizes the importance of optimizing genetic operators, particularly crossover operators, to improve the overall performance of genetic algorithms.
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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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Ariyani, Amalia Kartika, Wayan Firdaus Mahmudy, and Yusuf Priyo Anggodo. "Hybrid Genetic Algorithms and Simulated Annealing for Multi-trip Vehicle Routing Problem with Time Windows." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 6 (2018): 4713. http://dx.doi.org/10.11591/ijece.v8i6.pp4713-4723.
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Vehicle routing problem with time windows (VRPTW) is one of NP-hard problem. Multi-trip is approach to solve the VRPTW that looking trip scheduling for gets best result. Even though there are various algorithms for the problem, there is opportunity to improve the existing algorithms in order gaining a better result. In this research, genetic algoritm is hybridized with simulated annealing algoritm to solve the problem. Genetic algoritm is employed to explore global search area and simulated annealing is employed to exploit local search area. Four combination types of genetic algorithm and simulated annealing (GA-SA) are tested to get the best solution. The computational experiment shows that GA-SA1 and GA-SA4 can produced the most optimal fitness average values with each value was 1.0888 and 1.0887. However GA-SA4 can found the best fitness chromosome faster than GA-SA1.
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Ariyani, Amalia Kartika, Wayan Firdaus Mahmudy, and Yusuf Priyo Anggodo. "Hybrid Genetic Algorithms and Simulated Annealing for Multi-trip Vehicle Routing Problem with Time Windows." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 6 (2018): 4713–23. https://doi.org/10.11591/ijece.v8i6.pp4713-4723.
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Vehicle routing problem with time windows (VRPTW) is one of NP-hard problem. Multi-trip is approach to solve the VRPTW that looking trip scheduling for gets best result. Even though there are various algorithms for the problem, there is opportunity to improve the existing algorithms in order gaining a better result. In this research, genetic algoritm is hybridized with simulated annealing algoritm to solve the problem. Genetic algoritm is employed to explore global search area and simulated annealing is employed to exploit local search area. Four combination types of genetic algorithm and simulated annealing (GA-SA) are tested to get the best solution. The computational experiment shows that GA-SA1 and GA-SA4 can produced the most optimal fitness average values with each value was 1.0888 and 1.0887. However GA-SA4 can found the best fitness chromosome faster than GA-SA1.
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Abbas, Basim K. "Genetic Algorithms for Quadratic Equations." Aug-Sept 2023, no. 35 (August 26, 2023): 36–42. http://dx.doi.org/10.55529/jecnam.35.36.42.
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A common technique for finding accurate solutions to quadratic equations is to employ genetic algorithms. The authors propose using a genetic algorithm to find the complex roots of a quadratic problem. The technique begins by generating a collection of viable solutions, then proceeds to assess the suitability of each solution, choose parents for the next generation, and apply crossover and mutation to the offspring. For a predetermined number of generations, the process is repeated. Comparing the evolutionary algorithm's output to the quadratic formula proves its validity and uniqueness. Furthermore, the utility of the evolutionary algorithm has been demonstrated by programming it in Python code and comparing the outcomes to conventional intuitions.
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Dharani Pragada, Venkata Aditya, Akanistha Banerjee, and Srinivasan Venkataraman. "OPTIMISATION OF NAVAL SHIP COMPARTMENT LAYOUT DESIGN USING GENETIC ALGORITHM." Proceedings of the Design Society 1 (July 27, 2021): 2339–48. http://dx.doi.org/10.1017/pds.2021.495.
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AbstractAn efficient general arrangement is a cornerstone of a good ship design. A big part of the whole general arrangement process is finding an optimized compartment layout. This task is especially tricky since the multiple needs are often conflicting, and it becomes a serious challenge for the ship designers. To aid the ship designers, improved and reliable statistical and computation methods have come to the fore. Genetic algorithms are one of the most widely used methods. Islier's algorithm for the multi-facility layout problem and an improved genetic algorithm for the ship layout design problem are discussed. A new, hybrid genetic algorithm incorporating local search technique to further the improved genetic algorithm's practicality is proposed. Further comparisons are drawn between these algorithms based on a test case layout. Finally, the developed hybrid algorithm is implemented on a section of an actual ship, and the findings are presented.
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D., KARDASH, and KOLLAROV O. "Solving optimization problems in energy with genetic algorithm." Journal of Electrical and power engineering 28, no. 1 (2023): 37–41. http://dx.doi.org/10.31474/2074-2630-2023-1-37-41.
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The article discusses the application of genetic algorithms in the field of energy optimization. Linear programming is commonly used for optimization problems in energy systems. Linear programming is a mathematical optimization method that seeks the optimal solution under constraints, where all constraints and the objective function are linear functions. In the realm of artificial intelligence,genetic algorithms are employed for optimization tasks. genetic algorithms mimic natural evolution processes, including selection, crossover, mutation, and adaptation, to solve optimization and search problems. The article outlines the process of a genetic algorithm, starting with the formation of an initial population and proceeding through selection, crossover, mutation, and evaluation. This cycle repeats until an optimal solution is achieved. Advantages of genetic algorithms include their ability to handle complex solution spaces, find global optima, adapt to changing conditions, optimize multi-objective functions, and work with non-linear and non-differentiable objective functions. However, they may require significant computational resources and parameter tuning. The article then presents a case study of applying a genetic algorithm to optimize the allocation of a power load in an energy system. The mathematical model is developed, and the simplex method is initially used for solution. Subsequently, a Python program for genetic algorithm implementation is provided. The algorithm's efficiency and convergence are demonstrated through a graphical representation of the optimization process. In conclusion, the article highlights the effectiveness of genetic algorithms in energy optimization, showcasing their rapid convergence and ability to find near-optimal solutions in complex scenarios.
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Mansouri, Taha, Ahad Zare Ravasan, and Mohammad Reza Gholamian. "A Novel Hybrid Algorithm Based on K-Means and Evolutionary Computations for Real Time Clustering." International Journal of Data Warehousing and Mining 10, no. 3 (2014): 1–14. http://dx.doi.org/10.4018/ijdwm.2014070101.
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One of the most widely used algorithms to solve clustering problems is the K-means. Despite of the algorithm's timely performance to find a fairly good solution, it shows some drawbacks like its dependence on initial conditions and trapping in local minima. This paper proposes a novel hybrid algorithm, comprised of K-means and a variation operator inspired by mutation in evolutionary algorithms, called Noisy K-means Algorithm (NKA). Previous research used K-means as one of the genetic operators in Genetic Algorithms. However, the proposed NKA is a kind of individual based algorithm that combines advantages of both K-means and mutation. As a result, proposed NKA algorithm has the advantage of faster convergence time, while escaping from local optima. In this algorithm, a probability function is utilized which adaptively tunes the rate of mutation. Furthermore, a special mutation operator is used to guide the search process according to the algorithm performance. Finally, the proposed algorithm is compared with the classical K-means, SOM Neural Network, Tabu Search and Genetic Algorithm in a given set of data. Simulation results statistically demonstrate that NKA out-performs all others and it is prominently prone to real time clustering.
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Zaidi, Ali, Djamal Chaabane, Larbi Asli, Lamine Idir, and Saida Matoub. "A genetics algorithms for optimizing a function over the integer efficient set." Croatian operational research review 15, no. 1 (2024): 75–88. http://dx.doi.org/10.17535/crorr.2024.0007.
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In this paper, we propose an algorithm called Directional Exploration Genetic Algorithm (DEGA) to resolve a function Phi over the efficient set of a multi-objective integer linear programming problem (MOILP). DEGA algorithm belongs to evolutionary algorithms, which operate on the decision space by choosing the fastest improving directions that improve the objectives functions and Phi function. Two variants of this algorithm and a basic version of the genetic algorithm (BVGA) are performed and implemented in Python. Several benchmarks are carried out to evaluate the algorithm's performances and interesting results are obtained and discussed.
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Meza Álvarez, Joaquín Javier, Juan Manuel Cueva Lovelle, and Helbert Eduardo Espitia. "REVISIÓN SOBRE ALGORITMOS DE OPTIMIZACIÓN MULTI-OBJETIVO GENÉTICOS Y BASADOS EN ENJAMBRES DE PARTÍCULAS." Redes de Ingeniería 6, no. 2 (2016): 54. http://dx.doi.org/10.14483/udistrital.jour.redes.2015.2.a06.
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El enfoque evolutivo como también el comportamiento social han mostrado ser una muy buena alternativa en los problemas de optimización donde se presentan varios objetivos a optimizar. De la misma forma, existen todavía diferentes vias para el desarrollo de este tipo de algoritmos. Con el fin de tener un buen panorama sobre las posibles mejoras que se pueden lograr en los algoritmos de optimización bio-inspirados multi-objetivo es necesario establecer un buen referente de los diferentes enfoques y desarrollos que se han realizado hasta el momento.En este documento se revisan los algoritmos de optimización multi-objetivo más recientes tanto genéticos como basados en enjambres de partículas. Se realiza una revisión critica con el fin de establecer las características más relevantes de cada enfoque y de esta forma identificar las diferentes alternativas que se tienen para el desarrollo de un algoritmo de optimización multi-objetivo bio-inspirado.Review about genetic multi-objective optimization algorithms and based in particle swarmABSTRACTThe evolutionary approach as social behavior have proven to be a very good alternative in optimization problems where several targets have to be optimized. Likewise, there are still different ways to develop such algorithms. In order to have a good view on possible improvements that can be achieved in the optimization algorithms bio-inspired multi-objective it is necessary to establish a good reference of different approaches and developments that have taken place so far. In this paper the algorithms of multi-objective optimization newest based on both genetic and swarms of particles are reviewed. Critical review in order to establish the most relevant characteristics of each approach and thus identify the different alternatives have to develop an optimization algorithm multi-purpose bio-inspired design is performed.Keywords: evolutionary computation, evolutionary multi-objective optimization.
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Geiger, D., C. Meek, and Y. Wexler. "A Variational Inference Procedure Allowing Internal Structure for Overlapping Clusters and Deterministic Constraints." Journal of Artificial Intelligence Research 27 (September 22, 2006): 1–23. http://dx.doi.org/10.1613/jair.2028.
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We develop a novel algorithm, called VIP*, for structured variational approximate inference. This algorithm extends known algorithms to allow efficient multiple potential updates for overlapping clusters, and overcomes the difficulties imposed by deterministic constraints. The algorithm's convergence is proven and its applicability demonstrated for genetic linkage analysis.
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Sherstnev, Pavel A., and Evgeniy S. Semenkin. "Self-configuring genetic programming algorithms with Success History-based Adaptation." Siberian Aerospace Journal 26, no. 1 (2025): 60–70. https://doi.org/10.31772/2712-8970-2025-26-1-60-70.
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In this work, a novel method for self-tuning genetic programming (GP) algorithms is presented, based on the ideas of the Success History based Parameter Adaptation (SHA) method, originally developed for the Differential Evolution (DE) algorithm. The main idea of the method is to perform a dynamic analysis of the history of successful solutions to adapt the algorithm's parameters during the search process. To implement this concept, the operation scheme of classical GP was modified to mimic the DE scheme, allowing the integration of the success history mechanism into GP. The resulting algorithm, denoted as SHAGP (Success-History based Adaptive Genetic Programming), demonstrates new capabilities for parameter adaptation, such as the adjustment of crossover and mutation probabilities. The work also includes a detailed review of existing self-tuning methods for GP algorithms, which allowed for the identification of their key advantages and limitations and the application of this knowledge in the development of SHAGP. Additionally, new crossover operators are proposed that enable dynamic adjustment of the crossover probability, account for the selective pressure at the current stage, and implement a multi-parent approach. This modification allows for more flexible control over the process of genotype recombination, thereby enhancing the algorithm's adaptability to the problem at hand. To adjust the probabilities of applying various operators (selection, crossover, mutation), self-configuring evolutionary algorithm methods are employed, in particular, the Self-Configuring Evolutionary Algorithm and the Population-Level Dynamic Probabilities Evolutionary Algorithm. Within the framework of this work, two variants of the algorithm were implemented – SelfCSHAGP and PDPSHAGP. The efficiency of the proposed algorithms was tested on problem sets from the Feynman Symbolic Regression Database. Each algorithm was run multiple times on each problem to obtain a reliable statistical sample, and the results were compared using the Mann–Whitney statistical test. The experimental data showed that the proposed algorithms achieve a higher reliability metric compared to existing GP self-tuning methods, with the PDPSHAGP method demonstrating the best efficiency in more than 90 % of the cases. Such a universal self-tuning mechanism can find applications in a wide range of fields, such as automated machine learning, big data processing, engineering design, and medicine, as well as in space applications – for example, in the design of navigation systems for spacecraft and the development of control systems for aerial vehicles. In these areas, the high reliability of algorithms and their ability to find optimal solutions in complex multidimensional spaces are critically important.
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Misevičius, Alfonsas, Vytautas Bukšnaitis, and Jonas Blonskis. "Euristinių algoritmų klasifikavimas." Informacijos mokslai 48 (January 1, 2009): 117–26. http://dx.doi.org/10.15388/im.2009.0.3327.
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Straipsnis skiriamas euristinių optimizavimo algoritmų, kurie jau kelis dešimtmečius traukia kompiuterių mokslo specialistų dėmesį, klasifikavimo klausimų aptarčiai. Jame apibrėžiami euristinių algoritmų tikslai, paskirtis, jų principiniai skiriamieji faktoriai, savybės. Apžvelgiamos svarbesnių euristinių optimizavimo algoritmų (tokių kaip atkaitinimo modeliavimas, tabu paieška, genetiniai algoritmai ir pan.) klasifikavimo schemos (metodikos). Nagrinėjamas universalios algoritmų sudedamųjų komponentų matricos – substancinių konceptų sistemos – naudojimas klasifikuojant euristinius algoritmus. Pabaigoje pateikiamos apibendrinamosios išvados.Reikšminiai žodžiai: algoritmai, algoritmų klasės, euristiniai ir metaeuristiniai algoritmai, algoritmų klasifikavimas.On the classification of heuristic algorithmsAlfonsas Misevičius, Vytautas Bukšnaitis, Jonas Blonskis SummaryIn this paper, the issues related to the classification (taxonomy) of heuristic optimization algorithms are discussed. Firstly, the main goals and features of heuristic techniques are introduced. Further, we outline some important classification schemes (templates) for the classical and modern heuristic algorithms such as (descent) local search, simulated annealing, tabu search, genetic (evolutionary) algorithms, ant colony optimization, etc. We also analyze the basic aspects of a universal classification template based on a set of so-called substantial concepts, i.e. the fundamental structural components of the algorithms. The paper is completed with concluding remarks. Key words: algorithms, heuristic and metaheuristic algorithms, classification of algorithms.
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Chintada, Sravani. "A Novel Method for Energy Efficient Clustering in Wireless Sensor Networks." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem35010.
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Wireless Sensor Networks (WSNs) play a crucial role in various applications, including environmental monitoring, industrial automation, and healthcare. However, optimizing WSNs for efficient resource utilization, energy conservation, and reliable data transmission remains a challenging task due to the dynamic nature of the network environment and resource-constrained sensor nodes. In this study, we propose a Hybrid Firefly Genetic Algorithm (HFGA) for optimizing WSN performance. The HFGA combines the strengths of the firefly algorithm's global search capabilities and the genetic algorithm's local search and optimization efficiency. By integrating these two evolutionary algorithms, the HFGA aims to achieve superior performance in terms of energy efficiency, network coverage, and convergence speed. We evaluate the effectiveness of the proposed HFGA through extensive simulation experiments in various WSN scenarios. The results demonstrate that the HFGA outperforms traditional optimization approaches and baseline algorithms in optimizing WSN performance metrics. Furthermore, we discuss the practical implications and future research directions for deploying the HFGA in real-world WSN applications. Overall, this study contributes to advancing WSN optimization techniques and enhancing the reliability and efficiency of WSN deployments. Keywords: Clustering, Genetic algorithm, Firefly algorithm, FAG algorithm.
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Palit, Herry Christian, Haris Lienardo, and I. Gede Agus Widyadana. "APLIKASI KOMBINASI ALGORITMA GENETIK DAN DATA ENVELOPMENT ANALYSIS PADA PENJADWALAN FLOWSHOP MULTIKRITERIA." Jurnal Teknik Industri 10, no. 1 (2008): 86–96. http://dx.doi.org/10.9744/jti.10.1.86-96.
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This article discusses the combination of genetic algorithm (GA) and Data Envelopment Analysis (DEA) to solve the flowshop scheduling problems with multicriteria. The criteria are makespan, total weighted tardiness, and mean flow time. DEA is used to calculate the overall value of criteria from each sequence. Relative efficiency value is employed as the fitted value in genetic algorithm, in order to have overall value that independent to a particular weight. The proposed algorithm that combines GA and DEA attain optimal solutions with relative efficiency as good as analytical solution, i.e., Mixed Integer Programming (MIP). From 30 problems generated, only one problem (3,33%) has relative efficienly less than 1.
 
 
 Abstract in Bahasa Indonesia:
 
 Artikel ini membahas kombinasi algoritma genetik dengan Data Envelopment Analysis (DEA) untuk pemecahan masalah penjadwalan flowshop multikriteria. Kriteria-kriteria yang digunakan, yaitu makespan, total weighted tardiness, dan mean flow time. DEA digunakan untuk menghitung nilai keseluruhan kriteria dari setiap sequence dengan menggunakan nilai efisiensi relatif sebagai fitted value dalam algoritma genetik. Hal ini ditujukan agar nilai keseluruhan dari kriteria-kriteria yang ada tidak terikat pada satu jenis bobot saja. Kombinasi dua metode ini menghasilkan suatu algoritma yang mampu menghasilkan kumpulan solusi optimal dengan nilai efisiensi relatif yang tidak kalah jika dibandingkan dengan hasil dari model Mixed Integer Programming (MIP), dimana dari 30 masalah yang dibangkitkan, hanya ada 1 masalah (3,33%) yang memiliki efisiensi relatif di bawah 1.
 
 Kata kunci: penjadwalan flowshop, algoritma genetik, Data Envelopment Analysis.
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Armanto, Hendrawan, Kevin Setiabudi, and C. Pickerling. "Komparasi Algoritma WOA, MFO dan Genetic pada Optimasi Evolutionary Neural Network dalam Menyelesaikan Permainan 2048." Jurnal Inovasi Teknologi dan Edukasi Teknik 1, no. 9 (2021): 676–84. http://dx.doi.org/10.17977/um068v1i92021p676-684.
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Neural network optimization using evolutionary algorithms is an interesting research topic. But right now, there are not much research in this topic that focused on Game, especially 2048. The 2048 game is one of the interesting games to study considering that the level of difficulty of this game will increase when the value of the resulting number increases. In addition, this game is also not limited by time but can be played continuously until the game ends. Neural network and tree are 2 architectures that can be used to play 2048 but require a long training time if you want to play well. In this study, this problem was optimized by an evolutionary algorithm (3 algorithms used in this study: Genetic Algorithm, WOA, and MFO). With this optimization, the best weight will be obtained in either the NN or Tree architecture to produce good intelligence in playing 2048. After going through various trials, it is concluded that the combination with the NN architecture is better than the Tree architecture and the WOA and MFO algorithms have succeeded in optimizing the architecture with better than the genetic algorithm.
 Optimasi neural network menggunakan algoritma evolutionary adalah topik penelitian yang menarik akan tetapi tidak banyak penelitian terkait hal ini yang berfokus pada game terutama game 2048. Game 2048 adalah salah satu game yang menarik untuk diteliti mengingat tingkat kesulitan permainan ini akan semakin meningkat disaat nilai angka yang dihasilkan semakin tinggi. Selain itu, permainan ini juga tidak dibatasi oleh waktu melainkan dapat dimainkan terus menerus hingga permainan berakhir. Neural network dan tree adalah 2 arsitektur yang dapat digunakan untuk memainkan 2048 akan tetapi membutuhkan waktu training yang lama jika ingin bermain dengan baik. Lama training tersebut yang pada penelitian ini dioptimasi oleh algoritma evolutionary (3 algoritma yang digunakan pada penelitian ini: Algoritma Genetic, WOA, dan MFO). Dengan adanya optimasi ini maka akan diperoleh bobot terbaik baik pada arsitektur NN ataupun Tree sehingga menghasilkan kecerdasan yang baik dalam memainkan 2048. Setelah melalui berbagai ujicoba maka disimpulkan bahwa kombinasi dengan arsitektur NN lebih baik dibandingkan dengan arsitektur Tree dan algoritma WOA dan MFO berhasil mengoptimasi arsitektur dengan lebih baik dibandingkan algoritma genetic.
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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 (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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Patel, Roshni V., and Jignesh S. Patel. "Optimization of Linear Equations using Genetic Algorithms." Indian Journal of Applied Research 2, no. 3 (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 (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 (2017): 1142–62. http://dx.doi.org/10.31142/ijtsrd2420.
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M., Nishidhar Babu, Kiran Y., and Ramesh |. V. Rajendra A. "Tackling Real Coded Genetic Algorithms." International Journal of Trend in Scientific Research and Development 2, no. 1 (2017): 217–23. https://doi.org/10.31142/ijtsrd5905.
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Genetic algorithms play a significant role, as search techniques for handling complex spaces, in many fields such as artificial intelligence, engineering, robotic, etc. Genetic algorithms are based on the underlying genetic process in biological organisms and on the natural evolution principles of populations. These algorithms process a population of chromosomes, which represent search space solutions, with three operations selection, crossover and mutation.Under its initial formulation, the search space solutions are coded using the binary alphabet. However, the good properties related with these algorithms do not stem from the use of this alphabet other coding types have been considered for the representation issue, such as real coding, which would seem particularly natural when tackling optimization problems of parameters with variables in continuous domains. In this paper we review the features of real coded genetic algorithms. Different models of genetic operators and some mechanisms available for studying the behavior of this type of genetic algorithms are revised and compared. M. Nishidhar Babu | Y. Kiran | A. Ramesh | V. Rajendra "Tackling Real-Coded Genetic Algorithms" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: https://www.ijtsrd.com/papers/ijtsrd5905.pdf
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CHIRIAC, Liubomir, Natalia LUPAŞCO, and Maria PAVEL. "Development of genetic algorithms from inter/transdisciplinary perspectives." Acta et commentationes: Științe ale Educației 33, no. 3 (2023): 31–42. http://dx.doi.org/10.36120/2587-3636.v33i3.31-42.
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The theoretical-practical foundations of Genetic Algorithms, which are built on the principle of "survival of the fittest", enunciated by Charles Darwin, are dealt with in this paper. The paper describes the basic characteristics of the genetic algorithm, highlighting its advantages and disadvantages. Genetic algorithm problems are examined. The Genetic Algorithm is examined from the perspective of examining problems in which finding the optimal solution is not simple or at least inefficient due to the characteristics of the probabilistic search. The steps are shown in which Genetic Algorithms encode a possible solution to a specific problem in a single data structure called a "chromosome" and set the stage for applying genetic operators to these structures in order to maintain critical information.
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Riwanto, Yudha, Muhammad Taufiq Nuruzzaman, Shofwatul Uyun, and Bambang Sugiantoro. "Data Search Process Optimization using Brute Force and Genetic Algorithm Hybrid Method." IJID (International Journal on Informatics for Development) 11, no. 2 (2023): 222–31. http://dx.doi.org/10.14421/ijid.2022.3743.
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High accuracy and speed in data search, which are aims at finding the best solution to a problem, are essential. This study examines the brute force method, genetic algorithm, and two proposed algorithms which are the development of the brute force algorithm and genetic algorithm, namely Multiple Crossover Genetic, and Genetics with increments values. Brute force is a method with a direct approach to solving a problem based on the formulation of the problem and the definition of the concepts involved. A genetic algorithm is a search algorithm that uses genetic evolution that occurs in living things as its basis. This research selected the case of determining the pin series by looking for a match between the target and the search result. To test the suitability of the method, 100-time tests were conducted for each algorithm. The results of this study indicated that brute force has the highest average generation rate of 737146.3469 and an average time of 1960.4296, and the latter algorithm gets the best score with an average generation rate of 36.78 and an average time of 0.0642.
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Ababneh, Jehad. "Greedy particle swarm and biogeography-based optimization algorithm." International Journal of Intelligent Computing and Cybernetics 8, no. 1 (2015): 28–49. http://dx.doi.org/10.1108/ijicc-01-2014-0003.
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Purpose – The purpose of this paper is to propose an algorithm that combines the particle swarm optimization (PSO) with the biogeography-based optimization (BBO) algorithm. Design/methodology/approach – The BBO and the PSO algorithms are jointly used in to order to combine the advantages of both algorithms. The efficiency of the proposed algorithm is tested using some selected standard benchmark functions. The performance of the proposed algorithm is compared with that of the differential evolutionary (DE), genetic algorithm (GA), PSO, BBO, blended BBO and hybrid BBO-DE algorithms. Findings – Experimental results indicate that the proposed algorithm outperforms the BBO, PSO, DE, GA, and the blended BBO algorithms and has comparable performance to that of the hybrid BBO-DE algorithm. However, the proposed algorithm is simpler than the BBO-DE algorithm since the PSO does not have complex operations such as mutation and crossover used in the DE algorithm. Originality/value – The proposed algorithm is a generic algorithm that can be used to efficiently solve optimization problems similar to that solved using other popular evolutionary algorithms but with better performance.
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Dan Liu, Dan Liu, Shu-Wen Yao Dan Liu, Hai-Long Zhao Shu-Wen Yao, et al. "Research on Mutual Information Feature Selection Algorithm Based on Genetic Algorithm." 電腦學刊 33, no. 6 (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>&nbsp;</p>
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Finki Dona Marleny and Mambang. "OPTIMASI GENETIC ALGORITHM DENGAN JARINGAN SYARAF TIRUAN UNTUK KLASIFIKASI CITRA." Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) 4, no. 1 (2019): 1–6. http://dx.doi.org/10.20527/jtiulm.v4i1.32.
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Klasifikasi Citra adalah sebuah teknik pengelompokan piksel untuk memperoleh suatu gambar objek yang diwakili oleh fitur, kelas atau materi. Banyak algoritma telah dicoba dalam penerapan di klasifikasi citra, salah satu yang sangat terkenala adalah Neural Network. Neural Network dalam pengembangan algoritma Backpropagation mampu mempelajari pola dari data training sehingga menghasilkan bobot-bobot baru dengan error serendah-rendahnya. Genetic Algorithm (GA) merupakan salah satu metode yang sering diterapkan dalam optimasi, Metode ini berbasis teori evolusi, algoritma ini bekerja pada populasi calon penyelesaian yang disebut kromosom yang awalnya dibangkitkan secara random dari ruang penyelesaian fungsi tujuan. Dengan menggunakan mekanisme opearator genetik yaitu persilangan dan mutasi populasi dievolusikan melalui fungsi fitness yang diarahkan pada kondisi konvergensi. Algoritma ini dapat diterapkan dalam banyak area fungsi-fungsi optimasi. Penelitian ini bertujuan untuk mengklasifikasi citra berdasarkan fitur menggunakan metode Backpropagation Optimasi Genetic Algorithm. Data yang digunakan adalah data kayu kelapa yang dikelompokkan berdasarkan kerapatan yang bermanfaat untuk seleksi kualitas kayu tersebut berdasarkan visualisasi.