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@DuTy^ da, il ajut eu. Uite ce trebuie sa faci @msttman Am uitat sa zic ca pasta veche se curata cu alcool izopropilic.

solutia ...

copi rromi vrea sa merge la scoale

Incerc sa nu judec, dar uneori e necesar pentru binele speciei umane. Te rog incearca sa investi sa scrii si sa citesti. Esti la pamant in momentul de fata. Lumea n-o sa te ia niciodata in serios dupa cum te exprimi. Si porcaria aia de metin o sa te prosteasca si mai tare. Nu te sabota singur.

Dar unde-ai gasit parola aia?

Optimization by natural selection Evolutionary algorithms are a heuristic-based approach to solving problems that cannot be easily solved in polynomial time, such as classically NP-Hard problems, and anything else that would take far too long to exhaustively process. When used on their own, they are typically applied to combinatorial problems; however, genetic algorithms are often used in tandem with other methods, acting as a quick way to find a somewhat optimal starting place for another algorithm to work off of. The premise of an evolutionary algorithm (to be further known as an EA) is quite simple given that you are familiar with the process of natural selection. An EA contains four overall steps: initialization, selection, genetic operators, and termination. These steps each correspond, roughly, to a particular facet of natural selection, and provide easy ways to modularize implementations of this algorithm category. Simply put, in an EA, fitter members will survive and proliferate, while unfit members will die off and not contribute to the gene pool of further generations, much like in natural selection. Context In the scope of this article, we will generally define the problem as such: we wish to find the best combination of elements that maximizes some fitness function, and we will accept a final solution once we have either ran the algorithm for some maximum number of iterations, or we have reached some fitness threshold. This scenario is clearly not the only way to use an EA, but it does encompass many common applications in the discrete case. Initialization In order to begin our algorithm, we must first create an initial population of solutions. The population will contain an arbitrary number of possible solutions to the problem, oftentimes called members. It will often be created randomly (within the constraints of the problem) or, if some prior knowledge of the task is known, roughly centered around what is believed to be ideal. It is important that the population encompasses a wide range of solutions, because it essentially represents a gene pool; ergo, if we wish to explore many different possibilities over the course of the algorithm, we should aim to have many different genes present. Selection Once a population is created, members of the population must now be evaluated according to a fitness function. A fitness function is a function that takes in the characteristics of a member, and outputs a numerical representation of how viable of a solution it is. Creating the fitness function can often be very difficult, and it is important to find a good function that accurately represents the data; it is very problem-specific. Now, we calculate the fitness of all members, and select a portion of the top-scoring members. Multiple objective functions EAs can also be extended to use multiple fitness functions. This complicates the process somewhat, because instead of being able to identify a single optimal point, we instead end up with a set of optimal points when using multiple fitness functions. The set of optimal solutions is called the Pareto frontier, and contains elements that are equally optimal in the sense that no solution dominates any other solution in the frontier. A decider is then used to narrow the set down a single solution, based on the context of the problem or some other metric. Genetic Operators This step really includes two sub-steps: crossover and mutation. After selecting the top members (typically top 2, but this number can vary), these members are now used to create the next generation in the algorithm. Using the characteristics of the selected parents, new children are created that are a mixture of the parents’ qualities. Doing this can often be difficult depending on the type of data, but typically in combinatorial problems, it is possible to mix combinations and output valid combinations from these inputs. Now, we must introduce new genetic material into the generation. If we do not do this crucial step, we will become stuck in local extrema very quickly, and will not obtain optimal results. This step is mutation, and we do this, quite simply, by changing a small portion of the children such that they no longer perfectly mirror subsets of the parents’ genes. Mutation typically occurs probabilistically, in that the chance of a child receiving a mutation as well as the severity of the mutation are governed by a probability distribution. Termination Eventually, the algorithm must end. There are two cases in which this usually occurs: either the algorithm has reached some maximum runtime, or the algorithm has reached some threshold of performance. At this point a final solution is selected and returned. Example Now, just to illustrate the result of this process I will show an example of an EA in action. The following gif shows several generations of dinosaurs learning to walk by optimizing their body structure and applied muscular forces. From left to right the generation increases, so the further right, the more optimized the walking process is. Despite the fact that the early generation dinosaurs were unable to walk, the EA was able to evolve the dinosaurs over time through mutation and crossover into a form that was able to walk. Sursa: https://towardsdatascience.com/introduction-to-evolutionary-algorithms-a8594b484ac

Plm, toti samsarii din Vitan vand jafuri aduse din Germania cu kilometrajul dat inapoi si nu sunt obligati sa plateasca impozit. De puscarie pentru inselaciune nici nu se pune problema. Eu zic sa ne organizam si maine sa iesim in fata Guvernului. #DreptatePentru@Andreiasul

Se copiaza lista de mai jos in: Windows: C:\Windows\System32\drivers\etc Fisierul hosts Linux: /etc/hosts ( sudo nano /etc/hosts sau gksu gedit /etc/hosts)

Lucrezi la service? Pare ca te pricepi... https://ro.wikipedia.org/wiki/Chipset Citim cu totii, sa fim neprosti!

Este foarte putin probabil sa se strice o placa video integrata, sau procesoul in sine. Eu iti recomand sa il desfaci din nou poate ai uitat vreun cablu. Cum deja e "stricat", putin rau ai putea sa mai faci. Dupa ce il desfaci, scoate procesorul si vezi daca atunci cand l-ai bagat ai indoit un pin ceva, daca nu incearca sa conectezi cat mai putine chestii, cum ar fi fara cd-rom, hdd sau boxe bla bla. Incearca decat cu ecranul, placa in sine, un modul de ram, incarcatorul ( alimentatia) si butonul de aprins, fara sa il montezi de tot, asigurate ca placa nu atinge ceva gen haine sau un surub, sau ai lasat furculita de la ciorba de la bunica pe ea (ca sa nu faca scurt) si aprindel, daca nu se aprinde, verifica de 6-7 ori ca ai conectat ce am zis, dupa mai dai odata, daca iti face la fel ca la inceput, sa nu iti dea video, schimba memoria ram de slot, si dai din nou, daca tot nu merge, scoate memoria aia si puneo pe cealalta, si fa acelasi lucru ca si cu cealalta (schimbi de slot daca nu merge). Dupa ce iti porneste bine, pui toate saraciile la loc (hdd, cd-rom, vibratorul) pe rand, dupa fiecare piesa pusa il aprinzi si vezi daca merge. Dupa ce le ai pe toate conectate il inchizi de tot si imi trimiti o bere xD O sa fiu atent la topic, sa imi zici ce ai facut

Sa nu fac alt post, cei incepatori in php uitati-va si aici:

Limbaj de asamblare Asamblare: http://www42.zippyshare.com/v/sWlPXGkJ/file.html Continut: Constructia unui procesor de 32 de biti Constructia fisierelor executabile Windows Instructiunile procesorului Instructiunile coprocesorului Variabile globale Functii si variabile locale

Deci stai asa! Cum?!

Salut ! Am vazut aceste functii si pe filme-online.eu.com. Buna discutia ..

Mda stiu nu vorbesc romana dar am nevoie de parola aia ..si scuze ca nu m-am expirat bine pentru ca nu stiM ce forum e roman sau englez...

Mă poate ajuta și pe mine cineva legat de aceste 2 parole ? nu le pot decrypta nici cum.. *4B9CEB419949314957DCDE8160082681A2D05849 sau asta : *CBA1B8CE72EB4BDE27AE5E7C2C7E960AFA9DBC0C https://gyazo.com/346ba3235c84b8c1d7be3b0bb00d6cdb

Nu pot decrypt parolele astea ... ma ajuta cineva?

Sunt parolele mele din metin2 am jucat inainte metin2 si am avut si sv si m-am facut eu parola si m-am puso peste tot si nu o mai stiu si e una dintre astea 2 si nu le pot decrypta...

Hallo please for me help?? i dont .. *4B9CEB419949314957DCDE8160082681A2D05849 or *CBA1B8CE72EB4BDE27AE5E7C2C7E960AFA9DBC0C Thanks / Vorbesc romana. nu stiu dece nu merge sa descriptez parola nu stiu engleza imi pare rau !