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rdf:type: yago:WikicatAlgorithms yago:WikicatGeneticAlgorithms yago:Algorithm105847438 yago:WikicatEvolutionaryAlgorithms yago:Act100030358 yago:Rule105846932 yago:YagoPermanentlyLocatedEntity yago:Event100029378 yago:Activity100407535 yago:WikicatOptimizationAlgorithmsAndMethods owl:Thing yago:PsychologicalFeature100023100 yago:WikicatSearchAlgorithms yago:Procedure101023820 yago:Abstraction100002137
rdfs:label: Генетический алгоритм Genetisch algoritme Algoritmo genético 遺伝的アルゴリズム Algorithme génétique Genetický algoritmus Algorisme genètic Algoritma genetik Genetic algorithm خوارزميات وراثية Genetischer Algorithmus Algorytm genetyczny Γενετικοί Αλγόριθμοι 遗传算法 유전 알고리즘 Генетичний алгоритм Algoritmo genetico Algoritmo genético
rdfs:comment: 유전 알고리즘(Genetic Algorithm)은 자연세계의 진화과정에 기초한 계산 모델로서 존 홀랜드(John Holland)에 의해서 1975년에 개발된 기법으로, 최적화 문제를 해결하는 기법의 하나이다. 생물의 진화를 모방한 진화 연산의 대표적인 기법으로, 실제 진화의 과정에서 많은 부분을 차용(채용)하였으며, 변이(돌연변이), 교배 연산 등이 존재한다. 또한 세대, 인구 등의 용어도 문제 풀이 과정에서 사용된다. Les algorithmes génétiques appartiennent à la famille des algorithmes évolutionnistes. Leur but est d'obtenir une solution approchée à un problème d'optimisation, lorsqu'il n'existe pas de méthode exacte (ou que la solution est inconnue) pour le résoudre en un temps raisonnable. Les algorithmes génétiques utilisent la notion de sélection naturelle et l'appliquent à une population de solutions potentielles au problème donné. La solution est approchée par « bonds » successifs, comme dans une procédure de séparation et évaluation (branch & bound), à ceci près que ce sont des formules qui sont recherchées et non plus directement des valeurs. Een genetisch algoritme (GA) is een algoritme ontstaan in de kunstmatige intelligentie, dat gebruikt wordt om oplossingen te vinden voor optimalisatie- en zoekproblemen. Genetische algoritmen zijn een klasse binnen de . Οι Γενετικοί αλγόριθμοι ανήκουν στο κλάδο της επιστήμης υπολογιστών και αποτελούν μια μέθοδο αναζήτησης βέλτιστων λύσεων σε συστήματα που μπορούν να περιγραφούν ως . Είναι χρήσιμοι σε προβλήματα που περιέχουν πολλές /διαστάσεις και δεν υπάρχει αναλυτική μέθοδος που να μπορεί να βρει το βέλτιστο συνδυασμό τιμών για τις μεταβλητές ώστε το υπό εξέταση σύστημα να αντιδρά με όσο το δυνατόν με το επιθυμητό τρόπο. Είναι μια τεχνική προγραμματισμού που εισήγαγε στα τέλη της δεκαετίας του 1960 ο Τζον Χόλαντ, ερευνητής του Ινστιτούτου της Σάντα Φε (ΗΠΑ). Генети́ческий алгори́тм (англ. genetic algorithm) — эвристический алгоритм поиска, используемый для решения задач оптимизации и моделирования путём случайного подбора, комбинирования и вариации искомых параметров с использованием механизмов, аналогичных естественному отбору в природе. Является разновидностью эволюционных вычислений, с помощью которых решаются оптимизационные задачи с использованием методов естественной эволюции, таких как наследование, мутации, отбор и кроссинговер. Отличительной особенностью генетического алгоритма является акцент на использование оператора «скрещивания», который производит операцию рекомбинации решений-кандидатов, роль которой аналогична роли скрещивания в живой природе. Algorytm genetyczny – rodzaj heurystyki przeszukującej przestrzeń alternatywnych rozwiązań problemu w celu wyszukania najlepszych rozwiązań. Sposób działania algorytmów genetycznych nieprzypadkowo przypomina zjawisko ewolucji biologicznej, ponieważ ich twórca John Henry Holland właśnie z biologii czerpał inspiracje do swoich prac. Obecnie zalicza się go do grupy algorytmów ewolucyjnych. Un algoritmo genetico è un algoritmo euristico utilizzato per tentare di risolvere problemi di ottimizzazione per i quali non si conoscono altri algoritmi efficienti di complessità lineare o polinomiale. L'aggettivo "genetico", ispirato al principio della selezione naturale ed evoluzione biologica teorizzato nel 1859 da Charles Darwin, deriva dal fatto che, al pari del modello evolutivo darwiniano che trova spiegazioni nella branca della biologia detta genetica, gli algoritmi genetici attuano dei meccanismi concettualmente simili a quelli dei processi biochimici scoperti da questa scienza. Um algoritmo genético (AG) é uma técnica de busca utilizada na ciência da computação e em investigação operacional para achar soluções aproximadas em problemas de otimização e busca, fundamentado principalmente pelo americano John Henry Holland.Algoritmos genéticos são uma classe particular de algoritmos evolutivos que usam técnicas inspiradas pela biologia evolutiva como hereditariedade, mutação, seleção natural e recombinação (ou crossing over). Alguns exemplos do uso de AG incluem otimização de aprendizagem de árvore de decisão para melhor performance, resolução de algoritmo de sudoku, , e etc. Algoritme genetik adalah teknik pencarian yang di dalam ilmu komputer untuk menemukan penyelesaian perkiraan untuk optimisasi dan masalah pencarian. Algoritme genetik adalah kelas khusus dari algoritme evolusioner dengan menggunakan teknik yang terinspirasi oleh biologi evolusioner seperti warisan, mutasi, seleksi alam dan rekombinasi (atau crossover) Algoritme Genetik pertama kali dikembangkan oleh John Holland pada tahun 1970-an di New York, Amerika Serikat. Dia beserta murid-murid dan teman kerjanya menghasilkan buku berjudul "Adaption in Natural and Artificial Systems" pada tahun 1975. Genetický algoritmus (GA) je heuristická optimalizační metoda, řadící se mezi tzv. , inspirovaná přirozeným výběrem živočišných druhů, tj. evoluční biologií, užívaná k řešení složitých optimalizačních úloh, které nelze řešit konvenčními metodami jako např. metoda Lagrangeových multiplikátorů, lineární programování, kvadratické programování atd. znázornění křížení a mutace 遺伝的アルゴリズム（いでんてきアルゴリズム、英語：genetic algorithm、略称：GA）とは、1975年にミシガン大学のジョン・H・ホランド（John Henry Holland）によって提案された近似解を探索するメタヒューリスティックアルゴリズムである。人工生命同様、偶然の要素でコンピューターの制御を左右する。4つの主要な進化的アルゴリズムの一つであり、その中でも最も一般的に使用されている。 In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on biologically inspired operators such as mutation, crossover and selection. Some examples of GA applications include optimizing decision trees for better performance, solving sudoku puzzles, hyperparameter optimization, etc. 遗传算法（英語：Genetic Algorithm，GA）是计算数学中用于解决最佳化的搜索算法，是进化算法的一种。进化算法最初是借鉴了进化生物学中的一些现象而发展起来的，这些现象包括遗传、突变、自然选择以及杂交等等。遗传算法通常实现方式为一种计算机模拟。对于一个最优化问题，一定数量的（称为个体）可抽象表示为染色體，使种群向更好的解进化。传统上，解用二进制表示（即0和1的串），但也可以用其他表示方法。进化从完全随机个体的种群开始，之后一代一代发生。在每一代中评价整个种群的适应度，从当前种群中随机地选择多个个体（基于它们的适应度），通过自然选择和突变产生新的生命种群，该种群在算法的下一次迭代中成为当前种群。 Un algorisme genètic (GA, de l'anglès Genetic Algorithm) és una tècnica de cerca utilitzada en informàtica per a trobar solucions aproximades a problemes d'optimització i recerca. Els algorismes genètics són una classe particular que utilitzen tècniques inspirades per l'evolució biològica, com l', la mutació, la selecció i l'encreuament (també anomenada recombinació genètica). Un algoritmo es una serie de pasos organizados que describe el proceso que se debe seguir, para dar solución a un problema específico. En los años 1970, de la mano de John Henry Holland, surgió una de las líneas más prometedoras de la inteligencia artificial, la de los algoritmos genéticos, (AG). Son llamados así porque se inspiran en la evolución biológica y su base genético-molecular. Los algoritmos genéticos se enmarcan dentro de los algoritmos evolutivos, que incluyen también las estrategias evolutivas, la programación evolutiva y la programación genética. Генети́чний алгори́тм (англ. genetic algorithm) — це еволюційний алгоритм пошуку, що використовується для вирішення задач оптимізації і моделювання шляхом послідовного підбору, комбінування і варіації шуканих параметрів з використанням механізмів, що нагадують біологічну еволюцію. الخوارزمية الوراثية (بالإنجليزية: Genetic Algorithms)‏ هي طريقة من طرق الاستمثال والبحث. يمكن تصنيف هذه الطريقة كإحدى طرق الخوارزميات التطورية التي تعتمد على تقليد عمل الطبيعة من منظور دارويني. تستعمل الخوارزمية الوراثية تقنية بحث لإيجاد حلولِ مضبوطة أَو تقريبية تحقق الأمثلية. الخوارزميات الوراثية تصنف على أنها من طرق البحث الشامل الاستدلالي (بالإنجليزية: Global search heuristics)‏. وهي أيضا فئة معينة من الخوارزميات التطورية المعروفة كذلك بالحساب التطوري (بالإنجليزية: evolutionary computation)‏ التي تستخدم تكنولوجيا مستوحاة من البيولوجيا التطورية مثل التوريث والطفرات والاختيار والتهجين (crossover).
rdfs:seeAlso: dbr:List_of_genetic_algorithm_applications
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dbo:abstract: Algorytm genetyczny – rodzaj heurystyki przeszukującej przestrzeń alternatywnych rozwiązań problemu w celu wyszukania najlepszych rozwiązań. Sposób działania algorytmów genetycznych nieprzypadkowo przypomina zjawisko ewolucji biologicznej, ponieważ ich twórca John Henry Holland właśnie z biologii czerpał inspiracje do swoich prac. Obecnie zalicza się go do grupy algorytmów ewolucyjnych. 遺伝的アルゴリズム（いでんてきアルゴリズム、英語：genetic algorithm、略称：GA）とは、1975年にミシガン大学のジョン・H・ホランド（John Henry Holland）によって提案された近似解を探索するメタヒューリスティックアルゴリズムである。人工生命同様、偶然の要素でコンピューターの制御を左右する。4つの主要な進化的アルゴリズムの一つであり、その中でも最も一般的に使用されている。 Um algoritmo genético (AG) é uma técnica de busca utilizada na ciência da computação e em investigação operacional para achar soluções aproximadas em problemas de otimização e busca, fundamentado principalmente pelo americano John Henry Holland.Algoritmos genéticos são uma classe particular de algoritmos evolutivos que usam técnicas inspiradas pela biologia evolutiva como hereditariedade, mutação, seleção natural e recombinação (ou crossing over). Alguns exemplos do uso de AG incluem otimização de aprendizagem de árvore de decisão para melhor performance, resolução de algoritmo de sudoku, , e etc. Algoritme genetik adalah teknik pencarian yang di dalam ilmu komputer untuk menemukan penyelesaian perkiraan untuk optimisasi dan masalah pencarian. Algoritme genetik adalah kelas khusus dari algoritme evolusioner dengan menggunakan teknik yang terinspirasi oleh biologi evolusioner seperti warisan, mutasi, seleksi alam dan rekombinasi (atau crossover) Algoritme Genetik pertama kali dikembangkan oleh John Holland pada tahun 1970-an di New York, Amerika Serikat. Dia beserta murid-murid dan teman kerjanya menghasilkan buku berjudul "Adaption in Natural and Artificial Systems" pada tahun 1975. Algoritme Genetik khususnya diterapkan sebagai simulasi komputer di mana sebuah populasi representasi abstrak (disebut dari solusi-solusi calon (disebut individual) pada sebuah masalah optimisasi akan berkembang menjadi solusi-solusi yang lebih baik. Secara tradisional, solusi-solusi dilambangkan dalam biner sebagai string '0' dan '1', walaupun dimungkinkan juga penggunaan yang berbeda. Evolusi dimulai dari sebuah populasi individual acak yang lengkap dan terjadi dalam generasi-generasi. Dalam tiap generasi, kemampuan keseluruhan populasi dievaluasi, kemudian multiple individuals dipilih dari populasi sekarang (current) tersebut secara stochastic (berdasarkan kemampuan mereka), lalu dimodifikasi (melalui mutasi atau rekombinasi) menjadi bentuk populasi baru yang menjadi populasi sekarang (current) pada iterasi berikutnya dari algoritme. الخوارزمية الوراثية (بالإنجليزية: Genetic Algorithms)‏ هي طريقة من طرق الاستمثال والبحث. يمكن تصنيف هذه الطريقة كإحدى طرق الخوارزميات التطورية التي تعتمد على تقليد عمل الطبيعة من منظور دارويني. تستعمل الخوارزمية الوراثية تقنية بحث لإيجاد حلولِ مضبوطة أَو تقريبية تحقق الأمثلية. الخوارزميات الوراثية تصنف على أنها من طرق البحث الشامل الاستدلالي (بالإنجليزية: Global search heuristics)‏. وهي أيضا فئة معينة من الخوارزميات التطورية المعروفة كذلك بالحساب التطوري (بالإنجليزية: evolutionary computation)‏ التي تستخدم تكنولوجيا مستوحاة من البيولوجيا التطورية مثل التوريث والطفرات والاختيار والتهجين (crossover). تعتبر الخوارزميات الجينية من التقنيات الهامة في البحث عن الخيار الأمثل من مجموعة حلول متوفرة لتصميم معين، وتعتمد مبدأ داروين في الاصطفاء حيث تقوم هذه المعالجة الوراثية بتمرير المزايا المثلى من خلال عمليات التوالد المتعاقبة، وتدعيم هذه الصفات، وتكون لهذه الصفات القدرة الأكبر على دخول عملية التوالد، وإنتاج ذرية أمثل، وبتكرار الدورة الوراثية تتحسن نوعية الذرية تدريجياً. Un algoritmo es una serie de pasos organizados que describe el proceso que se debe seguir, para dar solución a un problema específico. En los años 1970, de la mano de John Henry Holland, surgió una de las líneas más prometedoras de la inteligencia artificial, la de los algoritmos genéticos, (AG). Son llamados así porque se inspiran en la evolución biológica y su base genético-molecular. Estos algoritmos hacen evolucionar una población de individuos sometiéndola a acciones aleatorias, semejantes a las que actúan en la evolución biológica (mutaciones y recombinaciones genéticas), así como también a una selección. De acuerdo con algún criterio, se decide cuáles son los individuos más adaptados, que sobreviven, y cuáles son los menos aptos, que son descartados. Los algoritmos genéticos se enmarcan dentro de los algoritmos evolutivos, que incluyen también las estrategias evolutivas, la programación evolutiva y la programación genética. 유전 알고리즘(Genetic Algorithm)은 자연세계의 진화과정에 기초한 계산 모델로서 존 홀랜드(John Holland)에 의해서 1975년에 개발된 기법으로, 최적화 문제를 해결하는 기법의 하나이다. 생물의 진화를 모방한 진화 연산의 대표적인 기법으로, 실제 진화의 과정에서 많은 부분을 차용(채용)하였으며, 변이(돌연변이), 교배 연산 등이 존재한다. 또한 세대, 인구 등의 용어도 문제 풀이 과정에서 사용된다. Οι Γενετικοί αλγόριθμοι ανήκουν στο κλάδο της επιστήμης υπολογιστών και αποτελούν μια μέθοδο αναζήτησης βέλτιστων λύσεων σε συστήματα που μπορούν να περιγραφούν ως . Είναι χρήσιμοι σε προβλήματα που περιέχουν πολλές /διαστάσεις και δεν υπάρχει αναλυτική μέθοδος που να μπορεί να βρει το βέλτιστο συνδυασμό τιμών για τις μεταβλητές ώστε το υπό εξέταση σύστημα να αντιδρά με όσο το δυνατόν με το επιθυμητό τρόπο. Ο τρόπος λειτουργίας των Γενετικών Αλγορίθμων είναι εμπνευσμένος από τη βιολογία. Χρησιμοποιεί την ιδέα της εξέλιξης μέσω , φυσικής επιλογής και διασταύρωσης. Οι Γενετικοί Αλγόριθμοι είναι αρκετά απλοί στην υλοποίησή τους. Οι τιμές για τις παραμέτρους του συστήματος πρέπει να κωδικοποιούνται με τρόπο ώστε να αναπαρασταθούν από μια μεταβλητή που περιέχει σειρά χαρακτήρων ή δυαδικών ψηφίων (0/1). Αυτή η μεταβλητή μιμείται το γενετικό κώδικα που υπάρχει στους ζωντανούς οργανισμούς. Αρχικά, ο Γενετικός Αλγόριθμος παράγει πολλαπλά αντίγραφα της μεταβλητής/γεννητικού κώδικα, συνήθως με τυχαίες τιμές, δημιουργώντας ένα πληθυσμό λύσεων. Κάθε λύση (τιμές για τις παραμέτρους του συστήματος) δοκιμάζεται για το πόσο κοντά φέρνει την αντίδραση του συστήματος στην επιθυμητή, μέσω μιας συνάρτησης που δίνει το μέτρο ικανότητας της λύσης και η οποία ονομάζεται (Σ.Ι). Οι λύσεις που βρίσκονται πιο κοντά στην επιθυμητή, σε σχέση με τις άλλες, σύμφωνα με το μέτρο που μας δίνει η Σ.Ι, αναπαράγονται στην επόμενη γενιά λύσεων και λαμβάνουν μια τυχαία μετάλλαξη. Επαναλαμβάνοντας αυτή τη διαδικασία για αρκετές γενιές, οι τυχαίες μεταλλάξεις σε συνδυασμό με την επιβίωση και αναπαραγωγή των γονιδίων/λύσεων που πλησιάζουν καλύτερα το επιθυμητό αποτέλεσμα θα παράγουν ένα γονίδιο/λύση που θα περιέχει τις τιμές για τις παραμέτρους που ικανοποιούν όσο καλύτερα γίνεται την Σ.Ι. Υπάρχουν διάφορες εκδοχές της παραπάνω διαδικασίας για τους Γ.Α από τις οποίες κάποιες περιλαμβάνουν και τη διασταύρωση (ζευγάρωμα) γονιδίων/λύσεων ώστε ο αλγόριθμος να φτάσει στο αποτέλεσμα πιο γρήγορα.Καθώς υπάρχει το (τυχαίο) συστατικό της μετάλλαξης και ζευγαρώματος, κάθε εκτέλεση του Γ.Α μπορεί να συγκλίνει σε διαφορετική λύση και σε διαφορετικό χρόνο. Η απόδοση του Γ.Α εξαρτάται επί το πλείστον από την συνάρτηση ικανότητας και συγκεκριμένα από το κατά πόσο το μέτρο της περιγράφει την βέλτιστη λύση.Οι γενετικοί αλγόριθμοι είναι ένα πεπερασμένο σύνολο οδηγιών για την εκπλήρωση ενός έργου, το οποίο δεδομένης μιας αρχικής κατάστασης θα οδηγήσει σε μια αναγνωρίσιμη τελική κατάσταση, και το οποίο προσπαθεί να μιμηθεί την διαδικασία της βιολογικής εξέλιξης. Οι γενετικοί αλγόριθμοι προσπαθούν να βρουν τη λύση ενός προβλήματος με το να προσομοιώνουν την εξέλιξη ενός πληθυσμού «λύσεων» του προβλήματος. Είναι μια τεχνική προγραμματισμού που εισήγαγε στα τέλη της δεκαετίας του 1960 ο Τζον Χόλαντ, ερευνητής του Ινστιτούτου της Σάντα Φε (ΗΠΑ). Οι γενετικοί αλγόριθμοι είναι μια από τις βάσεις των Προγραμμάτων Τεχνητής Ζωής. Συγκεκριμένα, επιχειρεί να αναπαράγει στους υπολογιστές τους μηχανισμούς της βιολογικής εξέλιξης με τον ίδιο τρόπο που η τεχνητή νοημοσύνη επιχειρεί να αναπαραστήσει και να μιμηθεί τις διαδικασίες της γνώσης. Τα προγράμματα εξελίσσονται μέχρι να φτάσουν, μέσω μεταλλάξεων, διασταυρώσεων και φυσικής επιλογής, σε μια αποτελεσματική φόρμουλα η οποία θα εκτελεί με τον καλύτερο δυνατό τρόπο μια συγκεκριμένη εργασία. Genetický algoritmus (GA) je heuristická optimalizační metoda, řadící se mezi tzv. , inspirovaná přirozeným výběrem živočišných druhů, tj. evoluční biologií, užívaná k řešení složitých optimalizačních úloh, které nelze řešit konvenčními metodami jako např. metoda Lagrangeových multiplikátorů, lineární programování, kvadratické programování atd. znázornění křížení a mutace Een genetisch algoritme (GA) is een algoritme ontstaan in de kunstmatige intelligentie, dat gebruikt wordt om oplossingen te vinden voor optimalisatie- en zoekproblemen. Genetische algoritmen zijn een klasse binnen de . Генети́ческий алгори́тм (англ. genetic algorithm) — эвристический алгоритм поиска, используемый для решения задач оптимизации и моделирования путём случайного подбора, комбинирования и вариации искомых параметров с использованием механизмов, аналогичных естественному отбору в природе. Является разновидностью эволюционных вычислений, с помощью которых решаются оптимизационные задачи с использованием методов естественной эволюции, таких как наследование, мутации, отбор и кроссинговер. Отличительной особенностью генетического алгоритма является акцент на использование оператора «скрещивания», который производит операцию рекомбинации решений-кандидатов, роль которой аналогична роли скрещивания в живой природе. Les algorithmes génétiques appartiennent à la famille des algorithmes évolutionnistes. Leur but est d'obtenir une solution approchée à un problème d'optimisation, lorsqu'il n'existe pas de méthode exacte (ou que la solution est inconnue) pour le résoudre en un temps raisonnable. Les algorithmes génétiques utilisent la notion de sélection naturelle et l'appliquent à une population de solutions potentielles au problème donné. La solution est approchée par « bonds » successifs, comme dans une procédure de séparation et évaluation (branch & bound), à ceci près que ce sont des formules qui sont recherchées et non plus directement des valeurs. Генети́чний алгори́тм (англ. genetic algorithm) — це еволюційний алгоритм пошуку, що використовується для вирішення задач оптимізації і моделювання шляхом послідовного підбору, комбінування і варіації шуканих параметрів з використанням механізмів, що нагадують біологічну еволюцію. Особливістю генетичного алгоритму є акцент на використання оператора «схрещення», який виконує операцію рекомбінацію рішень-кандидатів, роль якої аналогічна ролі схрещення в живій природі. «Батьком-засновником» генетичних алгоритмів вважається Джон Голланд (англ. John Holland), книга якого «Адаптація в природних і штучних системах» (англ. Adaptation in Natural and Artificial Systems) є фундаментальною в цій сфері досліджень. Un algoritmo genetico è un algoritmo euristico utilizzato per tentare di risolvere problemi di ottimizzazione per i quali non si conoscono altri algoritmi efficienti di complessità lineare o polinomiale. L'aggettivo "genetico", ispirato al principio della selezione naturale ed evoluzione biologica teorizzato nel 1859 da Charles Darwin, deriva dal fatto che, al pari del modello evolutivo darwiniano che trova spiegazioni nella branca della biologia detta genetica, gli algoritmi genetici attuano dei meccanismi concettualmente simili a quelli dei processi biochimici scoperti da questa scienza. 遗传算法（英語：Genetic Algorithm，GA）是计算数学中用于解决最佳化的搜索算法，是进化算法的一种。进化算法最初是借鉴了进化生物学中的一些现象而发展起来的，这些现象包括遗传、突变、自然选择以及杂交等等。遗传算法通常实现方式为一种计算机模拟。对于一个最优化问题，一定数量的（称为个体）可抽象表示为染色體，使种群向更好的解进化。传统上，解用二进制表示（即0和1的串），但也可以用其他表示方法。进化从完全随机个体的种群开始，之后一代一代发生。在每一代中评价整个种群的适应度，从当前种群中随机地选择多个个体（基于它们的适应度），通过自然选择和突变产生新的生命种群，该种群在算法的下一次迭代中成为当前种群。 In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on biologically inspired operators such as mutation, crossover and selection. Some examples of GA applications include optimizing decision trees for better performance, solving sudoku puzzles, hyperparameter optimization, etc. Un algorisme genètic (GA, de l'anglès Genetic Algorithm) és una tècnica de cerca utilitzada en informàtica per a trobar solucions aproximades a problemes d'optimització i recerca. Els algorismes genètics són una classe particular que utilitzen tècniques inspirades per l'evolució biològica, com l', la mutació, la selecció i l'encreuament (també anomenada recombinació genètica). Els algorismes genètics s'implementen típicament com una simulació informàtica, en la qual una població de representacions abstractes (anomenades cromosomes) de solucions candidates (anomenades individus) a un problema d'optimització evoluciona cap a millors solucions. Tradicionalment, les solucions es representen com sèries binàries de 0 i 1, però les codificacions diferents són també possibles. L'evolució comença des d'una població d'individus completament fortuïts i passa a diferents generacions. En cada generació, l'aptitud de la població sencera s'avalua, se seleccionen múltiples individus de manera de la població actual (basada en la seva aptitud o idoneïtat), i es modifiquen, mutant o recombinant, per formar una nova població. La nova població s'utilitza en la següent iteració de l'algorisme.
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Subject Item: dbr:Facial_composite
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Subject Item: dbr:Quality_control_and_genetic_algorithms
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Subject Item: dbr:Plant_Simulation
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Subject Item: dbr:Rule-based_machine_learning
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Subject Item: dbr:Searching_the_conformational_space_for_docking
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Subject Item: dbr:Evolution_strategy
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Subject Item: dbr:Evolutionary_algorithm
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Subject Item: dbr:Evolutionary_computation
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Subject Item: dbr:Fitness_function
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Subject Item: dbr:Watchmaker_analogy
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Subject Item: dbr:NNPDF
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Subject Item: dbr:Sudoku_solving_algorithms
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Subject Item: dbr:Photonic_metamaterial
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Subject Item: dbr:Peter_J._Fleming
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Subject Item: dbr:Premature_convergence
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Subject Item: dbr:Unorganized_machine
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Subject Item: dbr:ShareThis
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Subject Item: dbr:Two-dimensional_filter
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Subject Item: dbr:Types_of_artificial_neural_networks
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Subject Item: dbr:Genetic_Algorithms
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Subject Item: wikipedia-en:Genetic_algorithm
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