Genetic Algorithms (GAs)

Genetic Algorithms (GAs)

Abstract
Genetic Algorithms (GAs) are adaptive heuristic search algorithm premised on the evolutionary ideas of natural selection and genetic. The basic concept of GAs is designed to simulate processes in natural system necessary for evolution, specifically those that follow the principles first laid down by Charles Darwin of “survival of the fittest”. As such they represent an intelligent exploitation of a random search within a defined search space to solve a problem.

First pioneered by John Holland in the 60s, Genetic Algorithms has been widely studied, experimented and applied in many fields in engineering worlds. Not only does GAs provide an alternative methods to solving problem, it consistently outperforms other traditional methods in most of the problems link. Many of the real world problems My seminar report_2involved finding optimal parameters, which might prove difficult for traditional methods but ideal for GAs. However, because of its outstanding performance in optimisation, GAs have been wrongly regarded as a function optimiser. In fact, there are many ways to view genetic algorithms. Perhaps most users come to GAs looking for a problem solver, but this is a restrictive view.

The Genetic Algorithm can solve problems that do not have a precisely-defined solving method, or if they do, when following the exact solving method would take far too much time. There are many such problems; actually, all still-open, interesting problems are like that. Such problems are often characterised by multiple and complex, sometimes even contradictory constraints, that must be all satisfied at the same time. Examples are crew and team planning, delivery itineraries, finding the most beneficial locations for stores or warehouses, building statistical models, etc.