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    evolutionary algorithm

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    papers like [28] are use univariate EDAs in continuous environments and [38] is another paper that uses EDAs in discrete environments. Besides, variant Particle Swarm Optimization (PSO) algorithms proposed on the DOPs provide good results. Therefore, to compare the results of MAMEDA we use [29] and [62]. PSO-CP algorithm [29] are utilized a new PSO model, called PSO with composite particles to address DOPs. In [62] is proposed a MA which hybridizes PSO with a fuzzy cognition local search technique on

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    real world Mathematics. In nowadays discrete Mathematics is one of the core components of Mathematics at the undergraduate level. This branch of Mathematics is very useful for people to understand and have a background in Computing Science like algorithms and programming in computing. There is a big question set about the combination of Mathematics and Computing Science. The answer can be withdrawn from the fact that digital computers, referred to hereafter simply as "computers" are discrete machines

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    As discussed in Section 1.3, there are many scheduling algorithms, each with its own parameters. As a result, selecting an algorithm can be difficult. The first problem is defining the criteria to be used in selecting an algorithm. The criteria are often defined in terms of CPU utilization, waiting time, response time, or throughput. To select an algorithm, we must first define the relative importance of these elements. Our criteria may include several measures, such as these: • Maximizing CPU utilization

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    Genetic Algorithms

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    Abstract Genetic algorithms are a randomized search method based on the biological model of evolution through mating and mutation. In the classic genetic algorithm, problem solutions are encoded into bit strings which are tested for fitness, then the best bit strings are combined to form new solutions using methods which mimic the Darwinian process of "survival of the fittest" and the exchange of DNA which occurs during mating in biological systems. The programming of genetic algorithms involves little

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    Genetic algorithms

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    Genetic algorithms for timetabling The possibilities and advantages of the meta-heuristic methods to timetabling problems have been studied extensively within the timetabling research community. It is a well established fact that methods incorporating domain specific heuristics can give acceptable solutions very quickly, but they often lack the optimization capabilities of the more intensive search methods provided by meta-heurists- tics. This observation generated the motivation to use a range of

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    Definition: “Time Complexity of an algorithm signifies the total time required by the program to run to completion.The Time complexity of algorithm is most commonly expressed using the big O notation”. “Time Complexity is most commonly estimated by counting the number of elementary functions performed by the algorithm.” Big O Notation: Big O notation is an upper bound, the worst-case time; Big O notation is required to run an algorithm on various inputs. Example:

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    The ID3 Algorithm

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    The ID3 Algorithm Abstract This paper details the ID3 classification algorithm. Very simply, ID3 builds a decision tree from a fixed set of examples. The resulting tree is used to classify future samples. The example has several attributes and belongs to a class (like yes or no). The leaf nodes of the decision tree contain the class name whereas a non-leaf node is a decision node. The decision node is an attribute test with each branch (to another decision tree) being a possible value of

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    Genetic Algorithms

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    ABSTRACT Genetic algorithm sounds like terminology from a B-rated sci-fi movie. Just what is a genetic algorithm? Is it human? Is it a computer? Is it alive? Is it the mutant offspring from some defunct Government experiment? All of these questions, and more, will be answered within the pages of this paper. The adventure will begin with a trip back in time to the roots of genetic algorithms. From there, the journey will press on to the inventor, or the father of genetic algorithms, Dr. John H. Holland

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    An algorithm, according to the Random House Unabridged Dictionary, is a set of rules for solving a problem in a finite number of steps. One of the fundamental problems of computer science is sorting a set of items. The solutions to these problems are known as sorting algorithms and rather ironically, “the process of applying an algorithm to an input to obtain an output is called a computation” [http://mathworld.wolfram.com/Algorithm.html]. The quest to develop the most memory efficient and

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    Euclidean Algorithm

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    Euclid was one of the world’s most famous and influential Mathematicians in history. He was born about 365 BC in Alexandria, Egypt, and died about 300 BC. His full name is not known but Euclid means “good glory”. Little was ever written about Euclid and much of the information known are from authors who wrote about his books. He studied in Plato’s ancient school in Athens and later went to Alexandria in Egypt, where he discovered a well-known division of math, known as Geometry. Thus, he was named

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