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Journal articles on the topic 'Genetic algoritms'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

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

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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

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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 En

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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 p

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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 opera

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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 lokalio

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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

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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 in

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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 Proble

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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 collecti

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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 algorit

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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

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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 pseudoderiva

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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. Th

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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 te

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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

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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

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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

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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 simu

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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 simu

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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. Furth

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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

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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 pr

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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 combin

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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

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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 o

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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

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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.

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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 lo

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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., M

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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

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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

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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 t

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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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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

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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 –

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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 t

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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 In

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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

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