The aim of this paper is , we focus on one such non-traditional optimization method which takes the ion's share of all non-traditional optimization methods. This so-called 'evolutionary algorithm (EA)' mimics the natural evolutionary principles on randomly-picked solutions from the search space of the problem and iteratively progresses towards the optimum point. Nature's ruthless selective advantage to interest individuals and creation of new and fit individuals using re-combinative and mutative genetic processing with generations is well- mimicked artificially in a computer algorithm to be played on a search space where good and bad solutions to the underlying problem coexist. The task of an evolutionary optimization algorithm is then to avoid the bad solutions in the search space, take clues from good solutions and eventually reach close to the best solution, similar to the genetic processing in natural systems.
Optimization is an activity which does not belong to any particular discipline and is routinely used in almost all fields of science, engineering and commerce. The Chambers dictionary describes optimization as an act of 'making the most or best of anything'. Theoretically speaking, performing an optimization task in a problem means finding the most or best suitable solution of the problem. Mathematical optimization studies spend a great deal of effort in trying to describe the properties of such an ideal solution. Engineering or practical optimization studies, on the other hand, thrive to look for a solution which is as similar to such an ideal solution as possible. Although the ideal optimal solution is desired, the restrictions on computing power and time often make the practitioners happy with an approximate solution. Serious studies on practical optimization begun as early as the Second World War, when the need for efficient deployment and resource allocation of military personnel and accessories became important. Most development in the so-called 'classical' optimization field was made by developing step-by-step procedures for solving a particular type of an optimization problem. Often fundamental ideas from geometry and calculus were borrowed to reach the optimum in an iterative manner. Such optimization procedures have enjoyed a good 50 years of research and applications and are still going strong. However, around the middle of eighties, completely unorthodox and less-mathematical yet intriguing optimization procedures have been suggested mostly by computer scientists. It is not surprising because these 'non-traditional' optimization methods exploit the fast and distributed computing machines which are for finding suggestions.
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