Relevant bibliographies by topics / Algoritmi genetici

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Academic literature on the topic 'Algoritmi genetici'

Author: Grafiati
Published: 4 June 2021
Last updated: 11 March 2023

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Journal articles on the topic "Algoritmi genetici"

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SILIȘTEANU, Damian Cristian, Bogdan Costel MOCANU, Mihnea Horia VREJOIU, and Florin POP. "Soluție pentru planificarea în timp real în sisteme distribuite utilizând algoritmi genetici." Revista Română de Informatică și Automatică 32, no. 3 (September 30, 2022): 33–50. http://dx.doi.org/10.33436/v32i3y202203.

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Dzidolikaitė, Agnė. "GENETIC ALGORITHMS FOR MULTIDIMENSIONAL SCALING / GENETINIŲ ALGORITMŲ TAIKYMAS DAUGIAMATĖMS SKALĖMS." Mokslas – Lietuvos ateitis 7, no. 3 (July 13, 2015): 275–79. http://dx.doi.org/10.3846/mla.2015.781.

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The paper analyzes global optimization problem. In order to solve this problem multidimensional scaling algorithm is combined with genetic algorithm. Using multidimensional scaling we search for multidimensional data projections in a lower-dimensional space and try to keep dissimilarities of the set that we analyze. Using genetic algorithms we can get more than one local solution, but the whole population of optimal points. Different optimal points give different images. Looking at several multidimensional data images an expert can notice some qualities of given multidimensional data. In the paper genetic algorithm is applied for multidimensional scaling and glass data is visualized, and certain qualities are noticed. Analizuojamas globaliojo optimizavimo uždavinys. Jis apibrėžiamas kaip netiesinės tolydžiųjų kintamųjų tikslo funkcijos optimizavimas leistinojoje srityje. Optimizuojant taikomi įvairūs algoritmai. Paprastai taikant tikslius algoritmus randamas tikslus sprendinys, tačiau tai gali trukti labai ilgai. Dažnai norima gauti gerą sprendinį per priimtiną laiko tarpą. Tokiu atveju galimi kiti – euristiniai, algoritmai, kitaip dar vadinami euristikomis. Viena iš euristikų yra genetiniai algoritmai, kopijuojantys gyvojoje gamtoje vykstančią evoliuciją. Sudarant algoritmus naudojami evoliuciniai operatoriai: paveldimumas, mutacija, selekcija ir rekombinacija. Taikant genetinius algoritmus galima rasti pakankamai gerus sprendinius tų uždavinių, kuriems nėra tikslių algoritmų. Genetiniai algoritmai taip pat taikytini vizualizuojant duomenis daugiamačių skalių metodu. Taikant daugiamates skales ieškoma daugiamačių duomenų projekcijų mažesnio skaičiaus matmenų erdvėje siekiant išsaugoti analizuojamos aibės panašumus arba skirtingumus. Taikant genetinius algoritmus gaunamas ne vienas lokalusis sprendinys, o visa optimumų populiacija. Skirtingi optimumai atitinka skirtingus vaizdus. Matydamas kelis daugiamačių duomenų variantus, ekspertas gali įžvelgti daugiau daugiamačių duomenų savybių. Straipsnyje genetinis algoritmas pritaikytas daugiamatėms skalėms. Parodoma, kad daugiamačių skalių algoritmą galima kombinuoti su genetiniu algoritmu ir panaudoti daugiamačiams duomenims vizualizuoti.
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Erama, Rahman, and Retantyo Wardoyo. "Modifikasi Algoritma Genetika untuk Penyelesaian Permasalahan Penjadwalan Pelajaran Sekolah." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 10, no. 1 (July 31, 2014): 111. http://dx.doi.org/10.22146/ijccs.6539.

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AbstrakModifikasi Algoritma Genetika pada penelitian ini dilakukan berdasarkan temuan-temuan para peneliti sebelumnya tentang kelemahan Algoritma Genetika. Temuan-temuan yang dimakasud terkait proses crossover sebagai salah satu tahapan terpenting dalam Algoritma Genetika dinilai tidak menjamin solusi yang lebih baik oleh beberapa peneliti. Berdasarkan temuan-temuan oleh beberapa peneliti sebelumnya, maka penelitian ini akan mencoba memodifikasi Algoritma Genetika dengan mengeliminasi proses crossover yang menjadi inti permasalahan dari beberapa peneliti tersebut. Eliminasi proses crossover ini diharapkan melahirkan algoritma yang lebih efektif sebagai alternative untuk penyelesaian permasalahan khususnya penjadwalan pelajaran sekolah.Tujuan dari penelitian ini adalah Memodifikasi Algoritma Genetika menjadi algoritma alternatif untuk menyelesaikan permasalahan penjadwalan sekolah, sehingga diharapkan terciptanya algoritma alternatif ini bisa menjadi tambahan referensi bagi para peneliti untuk menyelesaikan permasalahan penjadwalan lainnya.Algoritma hasil modifikasi yang mengeliminasi tahapan crossover pada algoritma genetika ini mampu memberikan performa 3,06% lebih baik dibandingkan algoritma genetika sederhana dalam menyelesaikan permasalahan penjadwalan sekolah. Kata kunci—algoritma genetika, penjadwalan sekolah, eliminasi crossover AbstractModified Genetic Algorithm in this study was based on the findings of previous researchers about the weakness of Genetic Algorithms. crossover as one of the most important stages in the Genetic Algorithms considered not guarantee a better solution by several researchers. Based on the findings by previous researchers, this research will try to modify the genetic algorithm by eliminating crossover2 which is the core problem of several researchers. Elimination crossover is expected to create a more effective algorithm as an alternative to the settlement issue in particular scheduling school.This study is intended to modify the genetic algorithm into an algorithm that is more effective as an alternative to solve the problems of school scheduling. So expect the creation of this alternative algorithm could be an additional resource for researchers to solve other scheduling problems.Modified algorithm that eliminates the crossover phase of the genetic algorithm is able to provide 2,30% better performance than standard genetic algorithm in solving scheduling problems school. Keywords—Genetic Algorithm, timetabling school, eliminate crossover
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Misevičius, Alfonsas, Andrius Blažinskas, Jonas Blonskis, and Vytautas Bukšnaitis. "Genetiniai algoritmai komivojažieriaus uždaviniui: negatyvieji ir pozityvieji aspektai*." Informacijos mokslai 50 (January 1, 2009): 173–80. http://dx.doi.org/10.15388/im.2009.0.3242.

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Šiame straipsnyje nagrinėjami klausimai, susiję su genetinių algoritmų taikymu, sprendžiant gerai žinomą kombinatorinio optimizavimo uždavinį – komivojažieriaus uždavinį (KU) (angl. traveling salesman problem). Svarstoma, jog genetinio algoritmo efektyvumui didelę įtaką turi uždavinio specifi nės savybės, todėl labai svarbu kūrybiškai sudaryti genetinį algoritmą konkrečiam sprendžiamam uždaviniui. Pateikiami eksperimentų, atliktų su realizuotu genetiniu algoritmu, rezultatai, iliustruojantys skirtingų veiksnių įtaką rezultatų kokybei. Konstatuojama, kad tinkamas genetinių operatorių ir lokaliojo individų (sprendinių) gerinimo derinimas leidžia gerokai padidinti genetinės paieškos efektyvumą.On the Genetic Algorithms for the Traveling Salesman Problem: Negative and Positive AspectsAlfonsas Misevičius, Andrius Blažinskas, Jonas Blonskis, Vytautas Bukšnaitis SummaryIn this paper, we discuss some issues related to the application of genetic algorithms (GAs) to the well-known combinatorial optimization problem – the traveling salesman problem (TSP). The results obtained from the experiments with the different variants of the genetic algorithm are presented as well. Based on these results, it is concluded that the effi ciency of the genetic search is much infl uenced by both the specifi c nature of the problem and the features of the algorithm itself. In particular, it should be emphasized that the incorporation of the (postcrossover) procedures for the local improvement of offspring has one of the crucial roles in obtaining high-quality solutions.
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Santoso, Kiswara Agung, Bagus Arief Setiawan, and Kusbudiono Kusbudiono. "Application of Genetic Algorithm on Inclusive Labeling of a Graph." InPrime: Indonesian Journal of Pure and Applied Mathematics 4, no. 1 (April 15, 2022): 24–32. http://dx.doi.org/10.15408/inprime.v4i1.24327.

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AbstractAs science developed, heuristic methods began to be used in graph coloring. Heuristic methods that have been used for graph coloring include Genetic Algorithm, Tabu Search, and Ant Colony Algorithm. A Genetic Algorithm is a method for solving optimization problems. In this study, the Genetic Algorithm will be used for the issue of labeling irregular vertices of inclusive distances to label any graph inclusively. We restrict an inclusive 1-distance to a simple graph using one-point crossover and mutation. The steps are a generation of random chromosomes, evaluating chromosome fitness values with tournament selection, conducting an evolutionary process consisting of one-point crossover and mutation, repeating the process until the termination criteria are met. The results of implementing the genetic algorithm on inclusive labeling can be determined by the chromatic number based on the adjacency matrix. The results of this labeling can be used as an alternative solution to the problem of inclusive labeling.Keywords: Genetic Algorithm; graph labeling; inclusive labeling. AbstrakSeiring berkembangnya ilmu pengetahuan metode heuristic mulai digunakan dalam pewarnaan graf. Metode heuristic yang telah digunakan untuk pewarnaan graf antara lain Algoritma Genetika, Tabu Search, dan Algoritma Semut (Ant Colony). Algoritma Genetika merupakan metode untuk menyelesaikan masalah optimasi. Pada penelitian ini, Algoritma Genetika digunakan untuk masalah pelabelan titik tak-teratur jarak inclusive agar dapat melabeli sebarang graf secara inclusive. Kami membatasi lingkup penelitian dengan menerapkan jarak inclusive 1 pada graf sederhana, menggunakan crossover satu titik dan mutasi. Metode yang digunakan dalam penelitian ini adalah studi literatur dengan mengkaji penggunaan Algoritma Genetika pada pelabelan titik tak-teratur jarak inclusive suatu graf. Langkah-langkah yang dilakukan adalah: pembangkitan kromosom secara acak, evaluasi nilai fitness kromosom dengan tournament selection, melakukan proses evolusi yang terdiri dari crossover satu titik dan mutasi, perulangan proses sampai kriteria pemerhentian terpenuhi. Hasil implementasi algoritma genetika pada pelabelan inclusive adalah dapat mengetahui bilangan kromatik berdasarkan matriks adjacency. Hasil pelabelan ini dapat dijadikan sebagai salah satu alternatif penyelesaian masalah pelabelan inklusif. Kata Kunci : Algoritma Genetika; pelabelan graf; pelabelan inklusif.
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Sumida, Brian. "Genetics for genetic algorithms." ACM SIGBIO Newsletter 12, no. 2 (June 1992): 44–46. http://dx.doi.org/10.1145/130686.130694.

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Hikmawan, Sisferi. "Algoritma Genetika dengan Mutasi Terbatas untuk Penjadwalan Perkuliahan." Jurnal Kajian Ilmiah 21, no. 2 (May 27, 2021): 229–42. http://dx.doi.org/10.31599/jki.v21i2.520.

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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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Raol, Jitendra R., and Abhijit Jalisatgi. "From genetics to genetic algorithms." Resonance 1, no. 8 (August 1996): 43–54. http://dx.doi.org/10.1007/bf02837022.

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Wicaksono, Satrio Agung. "Optimasi Sistem Penempatan Magang Menerapkan Algoritme Genetika." Jurnal Teknologi Informasi dan Ilmu Komputer 6, no. 1 (January 16, 2019): 17. http://dx.doi.org/10.25126/jtiik.201961950.

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<p>Magang merupakan proses yang penting dalam proses belajar mengajar di SMK. Secara spesifik magang di SMK disebut Prakerin (Praktek Kerja Industri). Penempatan magang harus memerhatikan kompetensi siswa, kuota dari perusahaan, kesesuaian jurusan dengan lowongan, dan penghasilan orang tua. Penempatan magang secara manual selama ini memakan waktu, sehingga kurang efisien. Oleh karena itu, penelitian ini fokus mengembangkan aplikasi dengan menerapkan pendekatan Algoritme Genetika untuk mempermudah dalam menentukan penempatan magang dengan menerapkan aturan yang berlaku. Algoritme Genetika dinilai sebagai algoritma yang relevan dan solutif untuk diterapkan dalam penyelesaian masalah optimasi kompleks. Masalah yang dimaksud umumnya adalah masalah yang sulit dilakukan dengan menerapkan metode konvensional. Algoritme Genetika memberikan hasil yang lebih baik untuk setiap iterasi pencarian solusi. Hasil fitness terbaik dengan nilai 0.0014286 diperoleh pada jumlah individu 200, jumlah generasi 200, persentase crossover 50% dan mutasi 10%. Hasil validasi dengan pihak SMK menyatakan bahwa sistem ini mudah untuk digunakan dan bermanfaat bagi pihak SMK, dengan rata-rata persentase kualitas sistem 82,5%. Algoritme Genetika efektif untuk diterapkan pada studi kasus penjadwalan atau penempatan magang yang memiliki karakteristik data yang kompleks.</p><p> </p><p><em><strong>Abstract</strong></em></p><p><em>Internships are an important component of teaching and learning activities in vocational high schools (SMK). Specifically the internship in SMK is called Prakerin (Industrial Work Practice). The internship placement should considering student competence, number of vacancies, the suitability of majors with vacancy, and the income of the parent. During this time, manual internship placement takes more time, so less efficient. Therefore, this research tries to approach using Genetic Algorithm to make it easier in determining the internship placement by applying the applicable rules. The Genetic Algorithm is judged as the right algorithm used in solving complex optimization problems, which is difficult to do by conventional methods. The Genetic Algorithm provides better results for each iteration of the solution search. Best fitness results with value 0.0014286 obtained on the number of individuals 200, the number of generation 200, the percentage of crossover 50% and the mutation 10%. Validation results with the SMK stated that the system is easy to use and beneficial to the SMK, with an average percentage of system quality about 82.5%. Genetic Algorithms are effective to apply to scheduling case studies or internship placements that have complex data characteristics.</em></p><p><strong><br /></strong></p>
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Zebua, Alfian Wiranata. "PENGARUH PANJANG BENTANG TERHADAP UKURAN PENAMPANG OPTIMUM BETON PRATEGANG PADA BALOK SEDERHANA DENGAN MENGGUNAKAN ALGORITMA GENETIKA." Jurnal Teknik Sipil 7, no. 1 (May 28, 2018): 16–25. http://dx.doi.org/10.24815/jts.v7i1.10481.

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Abstract: Structural design philosophy expected to be low cost and safe at once. At prestressed concrete structure, concrete allowable stress for bending structural component have to fullfill limit values on tranfer and service condition. Prestressed force determined using moment coeffiecient β method based on allwoable stress. At this structure, to achieve optimum design is not an easy task due to interaction between dimension size and prestressing force value. To determine optimum design criteria, genetics algorithms as one of optimum design method has been used in this paper. Genetics algorithm is a method to find the best solution using organism genetics process based on Darwin theory which solutions with the best fitness value that could be survive as the optimization result. Fitness value is minimum construction cost. Optimization process using genetics algorithm has been worked with computer software assitance Matlab. Simpe beam has been considered as numerical example. Optimization result is optimum design of dimension size and prestressing force. Optimization procedure with beam length (L) 10 m result are beam width (b) 0,40 m, beam height (h) 0,47 m, prestressing force (Fi) value 1344 KN and bulding cost total Rp. 16.651.000,-. Next optimization procedure has been done using different beam length. From this study, the interaction between beam length to optimum dimension size and minimum building cost has been achieved.Keywords : beam length, genetics algoritm, optimum sizing, prestressed concreteAbstrak: Filosofi perencanaan struktur diharapkan ekonomis sekaligus aman. Pada struktur beton prategang, tegangan ijin beton untuk komponen struktur lentur harus memenuhi nilai batas pada saat transfer dan beban layan. Gaya prategang ditentukan dengan menggunakan metode koefisien momen β yang didasarkan pada tegangan ijin. Pada struktur ini, penentuan desain optimum tidak mudah karena adanya keterkaitan antara ukuran penampang dengan besaran gaya prategang yang dibutuhkan. Untuk memperoleh kriteria desain yang optimum digunakan metode optimasi dengan menggunakan algoritma genetika. Algoritma genetika merupakan metode pencarian sesuai dengan proses genetika organisme berdasarkan teori evolusi Darwin, dimana solusi dengan nilai fitness (tujuan) yang tinggi yang mampu bertahan sebagai hasil dari proses optimasi. Nilai fitness (tujuan) adalah memperoleh total harga konstruksi yang paling rendah. Proses optimasi dengan algoritma genetika dikerjakan dengan bantuan software komputer Matlab. Untuk melakukan optimasi digunakan algoritma genetika real. Jenis struktur yang ditinjau adalah balok sederhana. Dari hasil optimasi dengan berbagai panjang bentang diperoleh dimensi balok dan nilai gaya prategang yang optimum. Proses optimasi dengan panjang bentang (L) 10 m, diperoleh lebar balok (b) 0,40 m, tinggi balok (h) 0,47 m, nilai gaya prategang (Fi) sebesar 1344 KN dengan total harga Rp. 16.651.000,-. Proses optimasi selanjutnya dilakukan dengan panjang bentang balok yang bervariasi. Dari hasil penelitian diperoleh hubungan antara panjang bentang balok dengan ukuran penampang optimum serta harga total struktur yang paling rendah.Kata kunci : algoritma genetika, beton prategang, panjang bentang, ukuran penampang optimum
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Dissertations / Theses on the topic "Algoritmi genetici"

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Monari, Giovanni. "Ottimizzazione di strutture reticolari mediante algoritmi genetici." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2015. http://amslaurea.unibo.it/8698/.

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La tesi tratta dell'ottimizzazione di alcune tipologie di strutture reticolari. Per sviluppare i problemi analizzati ci si è avvalsi del software Grasshopper, conducendo poi l'ottimizzazione mediante un algoritmo genetico.
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Fantin, Pietro <1992&gt. "Ottimizzazione di una strategia di trading mediante algoritmi genetici." Master's Degree Thesis, Università Ca' Foscari Venezia, 2019. http://hdl.handle.net/10579/14191.

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Il lavoro consente nell'identificazione di alcune serie storiche alle quali verranno applicate delle strategie di trading; i parametri degli indicatori e degli oscillatori che compongo le sopracitate strategie saranno ottimizzati attraverso gli algoritmi genetici con l'implementazione in R di uno script.
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Soli, Andrea. "Identificazione di strutture reticolari mediante prove dinamiche e algoritmi genetici." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017.

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Nel presente elaborato di tesi viene descritto un metodo di indagine che, mediante prove dinamiche e misure di accelerazione, si propone di identificare eventuali danneggiamenti in strutture reticolari spaziali. L’identificazione consiste, sulla base di dati sperimentali in termini di frequenze proprie del sistema, nel determinare, con la migliore precisione possibile l’area della sezione trasversale degli elementi. Il problema è formulato come un problema di ottimizzazione e l’identificazione è condotta mediante l’utilizzo di Algoritmi Genetici (brevemente GAs). In particolare, una funzione obiettivo misura la differenza tra le grandezze misurate sperimentalmente e le grandezze ottenute numericamente mediante un codice FEM della struttura in esame. Il principio cui sono ispirati gli algoritmi genetici fa sì che questi ricerchino il miglior individuo all’interno di una popolazione che rappresenta le possibili soluzioni del problema. Si riportano i dati ottenuti, mediante prove dinamiche, su una struttura reale, che permettono di ottenere informazioni sui parametri modali: frequenze proprie e smorzamento. Si confrontano, infine due differenti tipi di sensori di accelerazione, dimostrando la validità e i possibili benefici dell'utilizzo di sensori con tecnologia MEMS nell'ambito della misura delle vibrazioni.
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Ceccarelli, Mattia. "Analisi della complessità di reti neurali generate tramite algoritmi genetici." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/16761/.

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L'enorme quantità di dati sviluppata tramite le infrastrutture moderne richiede strumentazioni di analisi sempre più precise, rapide e performanti. Per questo negli ultimi anni i metodi del machine learning hanno visto un'esplosione nella ricerca e nell'utilizzo di algoritmi mano a mano più efficienti per ogni disciplina. Due tra le numerose famiglie di tecniche appartenenti alla sfera del machine learning sono reti neurali e algoritmi genetici. Il progetto di tesi presentato ha come obiettivo quello di verificare la possibilità di evolvere la struttura di una rete neurale attraverso un algoritmo genetico in modo da automatizzarne il processo di costruzione, che ad oggi consiste in un procedimento di trial and error. La simulazione programmata consiste nell'evoluzione di una popolazione iniziale randomica di neural network tramite le tipiche metodologie di un algoritmo genetico, adattate al caso particolare di una funzione senza un predeterminato numero di variabili, il quale diventa un parametro della ricerca. Le reti verranno addestrate e valutate nella separazione di due classi di punti in dataset artificiali per verificare la bontà dell'algoritmo. Dopodiché lo studio si concentrerà sull'analisi del come e quando la rumorosità dei dati influenzi la complessità della rete ottenuta dall'algoritmo genetico, la quale viene misurata attraverso specifiche caratteristiche. La classificazione nei dataset testati è buona utilizzando un classico controllo binario del successo nella risposta della rete (giusto/sbagliato) tuttavia è migliore utilizzando un particolare metro di valutazione chiamato logarithmic loss. Lo studio sulla complessità della rete mostra una rilevante dipendenza di questa dalla rumorosità del dataset; inoltre, risulta che la separazione tra train test e test set nell'addestramento è sufficiente a regolarizzare la complessità della rete senza altri tipi di penalizzazione.
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Shikh, Farshi Christian. "L'utilizzo di algoritmi genetici nel progetto preliminare di un velivolo." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2013. http://amslaurea.unibo.it/5843/.

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Il progetto di un velivolo risulta essere un processo multidisciplinare molto complesso. Per poter determinare una configurazione di variabili che permetta di soddisfare i requisiti che si desiderano ottenere dal velivolo, sono necessarie una serie di stime che richiedono altrettanti cicli di analisi delle caratteristiche, prima di poter ottenere una configurazione completa o accettabile. Il processo di progetto richiede, così, un gran numero di iterazioni per poter trovare la migliore configurazione. In questo lavoro di tesi verranno descritti gli strumenti di ottimizzazione noti come algoritmi genetici e verrà presentato come questi possano essere inquadrati all'interno della fase preliminare del progetto di un velivolo.
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Marchi, Angela <1980&gt. "Ottimizzazione delle reti di distribuzione idrica tramite algoritmi genetici multi-obiettivo." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2009. http://amsdottorato.unibo.it/1381/1/Marchi_Angela_tesi.pdf.

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Water distribution networks optimization is a challenging problem due to the dimension and the complexity of these systems. Since the last half of the twentieth century this field has been investigated by many authors. Recently, to overcome discrete nature of variables and non linearity of equations, the research has been focused on the development of heuristic algorithms. This algorithms do not require continuity and linearity of the problem functions because they are linked to an external hydraulic simulator that solve equations of mass continuity and of energy conservation of the network. In this work, a NSGA-II (Non-dominating Sorting Genetic Algorithm) has been used. This is a heuristic multi-objective genetic algorithm based on the analogy of evolution in nature. Starting from an initial random set of solutions, called population, it evolves them towards a front of solutions that minimize, separately and contemporaneously, all the objectives. This can be very useful in practical problems where multiple and discordant goals are common. Usually, one of the main drawback of these algorithms is related to time consuming: being a stochastic research, a lot of solutions must be analized before good ones are found. Results of this thesis about the classical optimal design problem shows that is possible to improve results modifying the mathematical definition of objective functions and the survival criterion, inserting good solutions created by a Cellular Automata and using rules created by classifier algorithm (C4.5). This part has been tested using the version of NSGA-II supplied by Centre for Water Systems (University of Exeter, UK) in MATLAB® environment. Even if orientating the research can constrain the algorithm with the risk of not finding the optimal set of solutions, it can greatly improve the results. Subsequently, thanks to CINECA help, a version of NSGA-II has been implemented in C language and parallelized: results about the global parallelization show the speed up, while results about the island parallelization show that communication among islands can improve the optimization. Finally, some tests about the optimization of pump scheduling have been carried out. In this case, good results are found for a small network, while the solutions of a big problem are affected by the lack of constraints on the number of pump switches. Possible future research is about the insertion of further constraints and the evolution guide. In the end, the optimization of water distribution systems is still far from a definitive solution, but the improvement in this field can be very useful in reducing the solutions cost of practical problems, where the high number of variables makes their management very difficult from human point of view.
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Marchi, Angela <1980&gt. "Ottimizzazione delle reti di distribuzione idrica tramite algoritmi genetici multi-obiettivo." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2009. http://amsdottorato.unibo.it/1381/.

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Water distribution networks optimization is a challenging problem due to the dimension and the complexity of these systems. Since the last half of the twentieth century this field has been investigated by many authors. Recently, to overcome discrete nature of variables and non linearity of equations, the research has been focused on the development of heuristic algorithms. This algorithms do not require continuity and linearity of the problem functions because they are linked to an external hydraulic simulator that solve equations of mass continuity and of energy conservation of the network. In this work, a NSGA-II (Non-dominating Sorting Genetic Algorithm) has been used. This is a heuristic multi-objective genetic algorithm based on the analogy of evolution in nature. Starting from an initial random set of solutions, called population, it evolves them towards a front of solutions that minimize, separately and contemporaneously, all the objectives. This can be very useful in practical problems where multiple and discordant goals are common. Usually, one of the main drawback of these algorithms is related to time consuming: being a stochastic research, a lot of solutions must be analized before good ones are found. Results of this thesis about the classical optimal design problem shows that is possible to improve results modifying the mathematical definition of objective functions and the survival criterion, inserting good solutions created by a Cellular Automata and using rules created by classifier algorithm (C4.5). This part has been tested using the version of NSGA-II supplied by Centre for Water Systems (University of Exeter, UK) in MATLAB® environment. Even if orientating the research can constrain the algorithm with the risk of not finding the optimal set of solutions, it can greatly improve the results. Subsequently, thanks to CINECA help, a version of NSGA-II has been implemented in C language and parallelized: results about the global parallelization show the speed up, while results about the island parallelization show that communication among islands can improve the optimization. Finally, some tests about the optimization of pump scheduling have been carried out. In this case, good results are found for a small network, while the solutions of a big problem are affected by the lack of constraints on the number of pump switches. Possible future research is about the insertion of further constraints and the evolution guide. In the end, the optimization of water distribution systems is still far from a definitive solution, but the improvement in this field can be very useful in reducing the solutions cost of practical problems, where the high number of variables makes their management very difficult from human point of view.
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Criscio, Davide. "identificazione di danneggiamenti in strutture reticolari mediante algoritmi genetici e prove dinamiche." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017.

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La valutazione dello stato di salute delle costruzioni, unitamente all'identificazione e quantificazione di eventuali danneggiamenti in esse presenti, costituiscono un tema di fondamentale importanza nell'ambito dell'Ingegneria Civile. Contestualmente, la necessità di ottenere risultati attendibili, in aggiunta al bisogno di arrecare il minimo disturbo possibile alle strutture indagate, ha condotto allo sviluppo di criteri all'avanguardia in grado di rispondere alle esigenze suddette. Le metodologie maggiormente impiegate nella diagnostica e nel monitoraggio delle strutture possono essere sostanzialmente suddivise in: • Tecniche di identificazione di tipo statico; • Tecniche di identificazione di tipo dinamico Le prime vengono impiegate nella valutazione di parametri variabili lentamente, durante un periodo di osservazione significativo a farne percepire la tendenza, e vengono utilizzate per la valutazione di lesioni negli edifici dovute a spostamenti e rotazioni degli stessi. Le seconde, trovano applicazione nella valutazione delle caratteristiche vibrazionali della struttura oggetto di indagine (frequenze, modi propri, smorzamenti). Il progredire della Ricerca Scientifica, parallelamente all'introduzione di strumenti di calcolo dalle importanti capacità computazionali , ha reso possibile lo sviluppo di metodologie avanzate (non distruttive) basate su specifici approcci numerici, in grado di restituire importanti informazioni in merito ai sistemi strutturali indagati. All'interno del presente lavoro di tesi viene riportato il risultato di un approccio numerico/sperimentale inerente il tema dell'identificazione strutturale. L'attenzione viene rivolta all'identificazione del danneggiamento all'interno di strutture reticolari, mediante l'utilizzo di Algoritmi Genetici e prove dinamiche.
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Morandi, Alessandro. "Algoritmi genetici implementati in C++ per l'ottimizzazione del consumo energetico in applicazioni ferroviarie." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017.

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Questa tesi presenta un algoritmo genetico implementato in C + + che risolve euristicamente il problema di ottimizzazione energetica di un treno in transito lungo la propria linea ferroviaria. La soluzione del problema è una delle tante possibili configurazioni di velocità che il treno potrebbe avere secondo un orario tabellato tenuto fissato. La soluzione rappresenta una proposizione al macchinista, guidatore del treno, di un profilo di marcia che viene figurato in quattro fasi ripetute consequenzialmente (accelerazione, crociera, coast, frenata) che costituiscono il profilo di velocità teorico ottimizzato energeticamente. Il fine ultimo dell’algoritmo è l’ottenimento di un profilo di marcia ottimizzato in tempo reale . Di conseguenza si ricerca tra le varie tipologie sviluppate una serie di accorgimenti volti sia ad aumentare la qualità delle soluzioni, sia a velocizzare i processi interni all’algoritmo. A tal fine si è analizzata l’efficacia combinata delle varie caratteristiche dell’algoritmo attraverso un test condotto su istanze verosimili. Infine si è messo a confronto questo lavoro di tesi con un altro algoritmo scritto in un linguaggio di programmazione differente al fine di condurre un’analisi benchmarking sia sulla qualità delle soluzioni e sia sul tempo di calcolo.
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Gotti, Carlo. "Utilizzo di algoritmi genetici nell'ambito della bioingegneria: Applicazione alla identificazione di modelli cardiaci." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2013. http://amslaurea.unibo.it/6446/.

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In questo studio sarà trattato lo sviluppo degli algoritmi genetici, uno strumento di calcolo nato ispirandosi alle leggi Darwiniane sull’evoluzione naturale. Questi algoritmi, le cui basi furono introdotte a partire dagli anni '40, mirano alla risoluzione di una vasta categoria di problemi computazionali utilizzando un approccio differente, basato sulle regole di mutazione e ricombinazione proprie della genetica. Essi permettono infatti di valutare delle soluzioni di partenza e, grazie alle variazioni introdotte dalla modifica casuale o dalla ricombinazione di queste, crearne di nuove nel tentativo di convergere verso soluzioni ottimali. Questo studio si propone come una descrizione di questo strumento, dei suoi sviluppi e delle sue potenzialità in ambito bioingegneristico, focalizzandosi sul suo utilizzo recente nell’ identificazione parametrica di modelli cardiaci.
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Books on the topic "Algoritmi genetici"

1

E, Rawlins Gregory J., ed. Foundations of genetic algorithms. San Mateo, Calif: M. Kaufmann Publishers, 1991.

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Man, K. F., K. S. Tang, and S. Kwong. Genetic Algorithms. London: Springer London, 1999. http://dx.doi.org/10.1007/978-1-4471-0577-0.

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Anup, Kumar, and Gupta Yash P, eds. Genetic algorithms. Oxford: Pergamon, 1995.

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1942-, Buckles Bill P., and Petry Fred, eds. Genetic algorithms. Los Alamitos, Calif: IEEE Computer Society Press, 1986.

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J, Grefenstette John, ed. Genetic algorithms for machine learning. Boston: Kluwer Academic Publishers, 1994.

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Luque, Gabriel, and Enrique Alba. Parallel Genetic Algorithms. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22084-5.

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Mutingi, Michael, and Charles Mbohwa. Grouping Genetic Algorithms. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-44394-2.

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Kramer, Oliver. Genetic Algorithm Essentials. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-52156-5.

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E, Haupt S., ed. Practical genetic algorithms. New York: Wiley, 1998.

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E, Haupt S., ed. Practical genetic algorithms. 2nd ed. Hoboken, N.J: John Wiley, 2004.

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Book chapters on the topic "Algoritmi genetici"

1

Hajian, Alireza, Giuseppe Nunnari, and Roohollah Kimiaefar. "Evolutionary Algorithms with Focus on Genetic Algorithm." In Intelligent Methods with Applications in Volcanology and Seismology, 141–67. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-15432-4_6.

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Petoukhov, Sergey V., and Elena S. Petukhova. "On Genetic Unitary Matrices and Quantum-Algorithmic Genetics." In Advances in Artificial Systems for Medicine and Education II, 103–15. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12082-5_10.

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Mühlenbein, H. "Parallel genetic algorithms, population genetics and combinatorial optimization." In Parallelism, Learning, Evolution, 398–406. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/3-540-55027-5_23.

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Gorges-Schleuter, Martina. "ASPARAGOS a parallel genetic algorithm and population genetics." In Parallelism, Learning, Evolution, 407–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/3-540-55027-5_24.

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Hardy, Yorick, and Willi-Hans Steeb. "Genetic Algorithms." In Classical and Quantum Computing, 313–400. Basel: Birkhäuser Basel, 2001. http://dx.doi.org/10.1007/978-3-0348-8366-5_15.

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Du, Ke-Lin, and M. N. S. Swamy. "Genetic Algorithms." In Search and Optimization by Metaheuristics, 37–69. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41192-7_3.

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Čepin, Marko. "Genetic Algorithm." In Assessment of Power System Reliability, 257–69. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-688-7_18.

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Sastry, Kumara, David E. Goldberg, and Graham Kendall. "Genetic Algorithms." In Search Methodologies, 93–117. Boston, MA: Springer US, 2013. http://dx.doi.org/10.1007/978-1-4614-6940-7_4.

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Rathore, Heena. "Genetic Algorithms." In Mapping Biological Systems to Network Systems, 97–106. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-29782-8_8.

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Ansari, Nirwan, and Edwin Hou. "Genetic Algorithms." In Computational Intelligence for Optimization, 83–97. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6331-0_6.

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Conference papers on the topic "Algoritmi genetici"

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Chapman, Colin D., Kazuhiro Saitou, and Mark J. Jakiela. "Genetic Algorithms As an Approach to Configuration and Topology Design." In ASME 1993 Design Technical Conferences. American Society of Mechanical Engineers, 1993. http://dx.doi.org/10.1115/detc1993-0338.

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Abstract The Genetic Algorithm, a search and optimization technique based on the theory of natural selection, is applied to problems of structural topology optimization. Given a structure’s boundary conditions and maximum allowable design domain, a discretized design representation is created. Populations of genetic algorithm “chromosomes” are then mapped into the design representation, creating potentially optimal structure topologies. Utilizing genetics-based operators such as crossover and mutation, generations of increasingly-desirable structure topologies are created. In this paper, the use of the genetic algorithm (GA) in structural topology optimization is presented. An overview of the genetic algorithm will describe the genetics-based representations and operators used in a typical genetic algorithm search. After defining topology optimization and its relation to the broader area of structural optimization, a review of previous research in GA-based and non-GA-based structural optimization is provided. The design representations, and methods for mapping genetic algorithm “chromosomes” into structure topology representations, are then detailed. Several examples of genetic algorithm-based structural topology optimization are provided: we address the optimization of beam cross-section topologies and cantilevered plate topologies, and we also investigate efficient techniques for using finite element analysis in a genetic algorithm-based search. Finally, a description of potential future work in genetic algorithm-based structural topology optimization is offered.
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Averill, R. C., W. F. Punch, E. D. Goodman, S. C. Lin, Y. C. Yip, and Y. Ding. "Genetic Algorithm-Based Design of Energy Absorbing Laminated Composite Beams." In ASME 1995 Design Engineering Technical Conferences collocated with the ASME 1995 15th International Computers in Engineering Conference and the ASME 1995 9th Annual Engineering Database Symposium. American Society of Mechanical Engineers, 1995. http://dx.doi.org/10.1115/detc1995-0012.

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Abstract This paper describes a general approach to structural design using Genetic Algorithms, and an application of that approach to the design of energy absorbing laminated composite beams containing distributed thin, compliant layers. We first discuss a method for applying a Genetic Algorithm (GA) to structural design, using it as an evolutionary search optimizer in conjunction with a structural simulator as its objective function. The simulator used is an efficient and robust special purpose finite element model based on a layerwise laminate theory. The GA “designs” the beam by selecting material assignments for the subregions and the locations of compliant layers, and evaluates the design using the simulator. The efficiency of the GA search is improved by use of the “injection island” architecture. The results demonstrate that the parallel GA architectures achieved algorithmic superlinear speedup to similar quality of solution in comparison with single-population genetic algorithms.
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Ladkany, George S., and Mohamed B. Trabia. "Incorporating Twinkling in Genetic Algorithms for Global Optimization." In ASME 2008 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2008. http://dx.doi.org/10.1115/detc2008-49256.

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Genetic algorithms have been extensively used as a reliable tool for global optimization. However these algorithms suffer from their slow convergence. To address this limitation, this paper proposes a two-fold approach to address these limitations. The first approach is to introduce a twinkling process within the crossover phase of a genetic algorithm. Twinkling can be incorporated within any standard algorithm by introducing a controlled random deviation from its standard progression to avoiding being trapped at a local minimum. The second approach is to introduce a crossover technique: the weighted average normally-distributed arithmetic crossover that is shown to enhance the rate of convergence. Two possible twinkling genetic algorithms are proposed. The performance of the proposed algorithms is successfully compared to simple genetic algorithms using various standard mathematical and engineering design problems. The twinkling genetic algorithms show their ability to consistently reach known global minima, rather than nearby sub-optimal points with a competitive rate of convergence.
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Jesus, Alexandre D., Arnaud Liefooghe, Bilel Derbel, and Luís Paquete. "Algorithm selection of anytime algorithms." In GECCO '20: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3377930.3390185.

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Misir, Mustafa, Stephanus Daniel Handoko, and Hoong Chuin Lau. "Building algorithm portfolios for memetic algorithms." In GECCO '14: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2598394.2598455.

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Ladkany, George S., and Mohamed B. Trabia. "A Hybrid Biomimetic Genetic Algorithm Using a Local Fuzzy Simplex Search." In ASME 2010 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/detc2010-29085.

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This paper presents a hybrid genetic algorithm that expands upon the previously successful approach of twinkling genetic algorithm (TGA) by incorporating a highly efficient local fuzzy-simplex search within the algorithm. The TGA was in principle a bio-mimetic algorithm that introduced a controlled deviation from a typical GA method, by not requiring that every genevariable of an offspring be the result of a crossover. Instead, twinkling allowed the genetic information of the randomly chosen gene locations to be directly passed on from one parent, which was shown to increase the likelihood of survival of a successful gene value within the offspring, rather than requiring it to be blended. The twinkling genetic algorithms proved highly effective at locating exact global optimum with a competitive rate of convergence for a wide variety of benchmark problems. In this work, it is proposed to couple the TGA with a fuzzy simplex local search to increase the rate of convergence of the algorithm. The proposed algorithm is tested using common mathematical and engineering design benchmark problems. Comparison of the results of this algorithm with earlier algorithms is presented.
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Савостин, Игорь, Igor' Savostin, Андрей Трубаков, and Andrey Trubakov. "The Combination of Morphological and Evolutionary Algorithms for Graph-based Pathfinding on Maps with Complex Topologies." In 29th International Conference on Computer Graphics, Image Processing and Computer Vision, Visualization Systems and the Virtual Environment GraphiCon'2019. Bryansk State Technical University, 2019. http://dx.doi.org/10.30987/graphicon-2019-2-300-303.

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One of the difficult problems to solve has always been and still remains the problem of finding a path either in a graphic chart or a graphic maze of large size. The main problem is that traditional algorithms require a lot of time due to combinatorial complexity. At the same time, both classical algorithms based on the search of variants (such as Dijkstra's algorithm, A*, ARA*, D* lite), and stochastic algorithms (ant algorithm, genetic), alongside with algorithms based on morphology (wave) are not always able to achieve the goal. The article proposes a new modification of the path-finding algorithm, which is a hybrid of the following: the morphological operations on graphic chart approach and genetic algorithm having a useful property of elasticity in time. The experiments (both synthetic and real data) have shown the feasibility of the proposed idea and its comparison with the most commonly used algorithms of contemporaneity.
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Brackin, Patricia, and Jonathan Colton. "Using Genetic Algorithms to Set Target Values for Engineering Characteristics in the House of Quality." In ASME 1999 Design Engineering Technical Conferences. American Society of Mechanical Engineers, 1999. http://dx.doi.org/10.1115/detc99/cie-9088.

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Abstract As part of a strategy for obtaining preliminary design specifications from the House of Quality, genetic algorithms were used to generate and optimize preliminary design specifications for an automotive case study. This paper describes the House of Quality for the automotive case study. In addition, the genetic algorithm chosen, the genetic coding, the methods used for mutation and reproduction, and the fitness and penalty functions are descrobed. Methods for determining convergence are examined. Finally, test results show that the genetic algorithm produces reasonable preliminary design specifications.
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Lo, C. H., Eric H. K. Fung, and Y. K. Wong. "Knowledge-Based Automatic Fault Detection in Flight Control System." In ASME 2007 International Mechanical Engineering Congress and Exposition. ASMEDC, 2007. http://dx.doi.org/10.1115/imece2007-41495.

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There are various possible failures, like, actuator, sensor, or structural, which can occur on a sophisticated modern aircraft. In certain situations the need for an automatic fault detection system provides additional information about the status of the aircraft to assist pilots to compensate for failures. In this paper, we develop an intelligent technique based on fuzzy-genetic algorithm for automatically detecting failures in flight control system. The fuzzy-genetic algorithm is proposed to construct the automatic fault detection system for monitoring aircraft behaviors. Fuzzy system is employed to estimates the times and types of actuator failure. Genetic algorithms are used to generate an optimal fuzzy rule set based on the training data. The optimization capability of genetic algorithms provides and efficient and effective way to generate optimal fuzzy rules. Different types of actuator failure can be detected by the fuzzy-genetic algorithm based automatic fault detection system after tuning its rule table. Simulations with different actuator failures of the non-linear F-16 aircraft model are conducted to appraise the performance of the proposed automatic fault detection system.
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Kavi, Deniz. "Towards Adversarial Genetic Text Generation." In 8th International Conference on Computer Science and Information Technology (CoSIT 2021). AIRCC Publishing Corporation, 2021. http://dx.doi.org/10.5121/csit.2021.110407.

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Text generation is the task of generating natural language, and producing outputs similar to or better than human texts. Due to deep learning’s recent success in the field of natural language processing, computer generated text has come closer to becoming indistinguishable to human writing. Genetic Algorithms have not been as popular in the field of text generation. We propose a genetic algorithm combined with text classification and clustering models which automatically grade the texts generated by the genetic algorithm. The genetic algorithm is given poorly generated texts from a Markov chain, these texts are then graded by a text classifier and a text clustering model. We then apply crossover to pairs of texts, with emphasis on those that received higher grades. Changes to the grading system and further improvements to the genetic algorithm are to be the focus of future research.
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Reports on the topic "Algoritmi genetici"

1

Arthur, Jennifer Ann. Genetic Algorithms. Office of Scientific and Technical Information (OSTI), August 2017. http://dx.doi.org/10.2172/1375151.

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Sharp, David H., John Reinitz, and Eric Mjolsness. Genetic Algorithms for Genetic Neural Nets. Fort Belvoir, VA: Defense Technical Information Center, January 1991. http://dx.doi.org/10.21236/ada256223.

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Kargupta, H. Messy genetic algorithms: Recent developments. Office of Scientific and Technical Information (OSTI), September 1996. http://dx.doi.org/10.2172/378868.

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Messa, K., and M. Lybanon. Curve Fitting Using Genetic Algorithms. Fort Belvoir, VA: Defense Technical Information Center, October 1991. http://dx.doi.org/10.21236/ada247206.

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Thomas, E. V. Frequency selection using genetic algorithms. Office of Scientific and Technical Information (OSTI), May 1993. http://dx.doi.org/10.2172/10177075.

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Arthur, Jennifer Ann. Genetic algorithm for nuclear data evaluation. Office of Scientific and Technical Information (OSTI), February 2018. http://dx.doi.org/10.2172/1419729.

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Arthur, Jennifer Ann. Genetic algorithm for nuclear data evaluation. Office of Scientific and Technical Information (OSTI), June 2018. http://dx.doi.org/10.2172/1441274.

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Cobb, Helen G., and John J. Grefenstette. Genetic Algorithms for Tracking Changing Environments. Fort Belvoir, VA: Defense Technical Information Center, January 1993. http://dx.doi.org/10.21236/ada294075.

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Pittman, Jennifer, and C. A. Murthy. Optimal Line Fitting Using Genetic Algorithms. Fort Belvoir, VA: Defense Technical Information Center, July 1997. http://dx.doi.org/10.21236/ada328266.

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Goldberg, David. Competent Probabilistic Model Building Genetic Algorithms. Fort Belvoir, VA: Defense Technical Information Center, July 2003. http://dx.doi.org/10.21236/ada416564.

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