Maad M. Mijwel computer science, 2016 maadalnaimiy@yahoo.com  Abstract— Genetic Algorithms (GAs) are adaptive methods that can be used to solve search and optimization problems. They are based on the genetic process of living organisms. Throughout the generations, the populations evolve in nature in accordance with the principles of natural selection and the survival of the strongest, postulated by Darwin. By imitating this process, Genetic Algorithms are able to create solutions for real world problems. The evolution of these solutions towards optimal values of the problem depends to a large extent on an adequate coding of them. A genetic algorithm consists of a mathematical function or a software routine that takes the specimens as inputs and returns as outputs which of them must generate offspring for the new generation. More complex versions of genetic algorithms generate an iterative cycle that directly takes the species and creates a new generation that replaces the old one a number of times determined by its own design. One of its main characteristics is that of perfecting its own heuristic in the execution process, so it does not require long periods of specialized training by the human being, the main defect of other methods to solve problems, such as Expert Systems. Keyword— Genetic Algorithms, Natural evolution, Mutation, Population, Optimization. I. INTRODUCTION he genetic algorithm is a search technique based on Darwin's theory of evolution, which has gained tremendous popularity around the world in recent years. The basic concepts that are required to address it will be presented here, as well as some simple examples that allow readers to understand how to apply it to the problem of their choice. In recent years, the international scientific community has shown a growing interest in a new search technique based on the theory of evolution and known as the genetic algorithm. This technique is based on the selection mechanisms used by nature, according to which the most fit individuals of a population are those that survive, by adapting more easily to the changes that occur in their environment. Nowadays it is known that these changes are made in the genes of an individual (basic unit of codification of each of the attributes of a living being), and that its most desirable attributes (i.e., those that allow it to adapt better to its environment) are transmitted to their descendants when they reproduce sexually. A researcher at the University of Michigan called John Holland was aware of the importance of natural selection, and in the late 60s developed a technique that allowed incorporating it into a program. His goal was to make computers learn by themselves. The technique that Holland invented was originally called "reproductive plans", but became popular under the name "genetic algorithm" after the publication of his book in 1975. A fairly complete definition of a genetic algorithm is the one proposed by John Koza:"It is a highly parallel mathematical algorithm that transforms a set of individual mathematical objects with respect to time using operations modeled according to the Darwinian principle of reproduction and survival of the fittest, and after having presented naturally a series of genetic operations from among the which emphasizes sexual recombination. Each of these mathematical objects is usually a string of characters (letters or numbers) of fixed length that fits the model of the chains of chromosomes, and they are associated with a certain mathematical function that reflects their fitness. " In nature, the individuals of a population compete with each other in the search for resources such as food, water and shelter. Even members of the same species often compete in the search for a partner. Those individuals who are more successful in surviving and attracting partners are more likely to generate a large number of offspring. On the other hand, less gifted individuals will produce fewer descendants. This means that the genes of the best adapted individuals will spread in successive generations to a growing number of individuals. The combination of good characteristics from different ancestors can sometimes produce "super individual" descendants, whose adaptation is much greater than that of any of their ancestors. In this way, characteristics increasingly better adapted to the environment in which they live. Genetic Algorithms use a direct analogy with natural behavior. They work with a population of individuals, each of which represents a feasible solution to a given problem. Each individual is assigned a value or score, related to the goodness of said solution. In nature this would be equivalent to the degree of effectiveness of an organism to compete for certain resources. The greater the adaptation of an individual to the problem, the greater the probability that he will be selected to reproduce, crossing his genetic material with another individual selected in the same way. This crossing will produce new individuals. descendants of the previous ones. which share some of the characteristics of their parents. The smaller the adaptation of an individual, in this way a new population of possible solutions is produced, which replaces the previous one and verifies the interesting property that contains a greater proportion of good characteristics compared to the previous population. Thus throughout the generations good characteristics spread through the population. By favoring the crossing of the best adapted individuals, the most promising areas of the search space are being explored. If the Genetic Algorithm has been well designed, the population will converge towards an optimal solution of the problem. II. ADVANTAGE OF GA Some of the things that include the advantages of the Genetic Algorithm are as follows:  Optimizing with continuous or discrete variables,  Does not require derivative information,  Simultaneously searching from an extensive sampling on the cost surface,  With regard to a large number of variables,  Good for parallel computers ,  Optimizing variable surfaces at very complex cost (GA can jump from local minimum), Genetic Algorithm Optimization by Natural Selection Maad M. Mijwel Department of computer science, college of science, Baghdad University, Baghdad, Iraq maadalnaimiy@yhaoo.com Augusts,2016 T