What Is The Genetic Algorithm?

Genetic algorithms (also referred to as adaptive computation) refer to a variety of problems solving techniques that are conceptually based on the method that living organism use to adapt to their environment the process of evolution. They are programmed to work the way population solve problems by changing and reorganized their component parts using processes such as reproduction, mutation, and natural selection.

Thus genetic algorithms promote the evolution of solution to particular problems, controlling the generation, variation, adaptation, and selection of possible solutions using genetically based processes. When solutions alarms and combine, the world ones are rejected and the better ones survive to go on to production even better solutions. Genetic algorithms strain programs that solve troubles that solve troubles even when no person can fully appreciate their construction.

Like neural networks, genetic algorithms are ideal applications for massively parallel computers. Each processor can be assigned a single sting. Thus, the entire population of genetic algorithms can be processed in parallel, offering growing potential for solving problems of enormous complexity.

Solutions to certain types of problems in areas of optimization, product design, and the monitoring of industrial systems are especially appropriate for genetic algorithms. Many business problems require optimization because they deal with issues such as minimization of costs, maximization of profits, efficient scheduling, and use of resources. If these situations are very dynamic and complex.

It is a whole class on this,but I will make it short.

Genetic algorithm is a search technique used in computing to find a true or an approximate solutions. I am assuming you maths,applied, further maths, pure maths,additional maths,  and calculus from 1 through 6. There are many different algorithms the computer can do for you,but there are some the computer cannot help you.