Genetic Algorithm Summary

2.11 Reviews on Genetic Algorithm approach in warehouse

Genetic Algorithms were invented to mimic some of the processes observed in natural evolution. Many people, biologists included, are astonished that life at the level of complexity that we observe could have evolved in the relatively short time suggested by the fossil record. The idea with GA is to use this power of evolution to solve optimization problems. The father of the original Genetic Algorithm was John Holland who invented it in the early 1970's.

Genetic Algorithms (GAs) are adaptive heuristic search algorithm based on the evolutionary ideas of natural selection and genetics. As such they represent an intelligent exploitation of a random search used to solve optimization problems.

Parents are selected to mate, on the basis of their fitness, producing offspring via a reproductive plan. Consequently highly fit solutions are given more opportunities to reproduce, so that offspring inherit characteristics from each parent. As parents mate and produce offspring, room must be made for the new arrivals since the population is kept at a static size. Individuals in the population die and are replaced by the new solutions, eventually creating a new generation once all mating opportunities in the old population have been exhausted. In this way it is hoped that over successive generations better solutions will thrive while the least fit solutions die

Last decade has witnessed many exciting advances in the use of genetic algorithms (GAs) to solve optimization problems in process control systems. Genetic algorithms (GAs) are the solution for optimization of hard problems quickly, reliably and accurately. As the complexity of the real-time controller increases, the genetic algorithms (GAs) applications have grown in more than equal measure. One of the most fundamental principal in our world is the search for an optimal state. Optimization is the process of modifying the inputs or characteristics of a device, mathematical process to obtain minimum or maximum of the output. The input to the optimization process is the cost function, objective function or fitness function and the output is the fitness function of the system. Optimization is a primary tool, needed to tackle the unsolvable or hard problems. Optimization algorithms can be characterized into five categories. In trial and error optimization, the processes affect the output without knowing about the constraints, responsible to produce the output. A mathematical formula describes the objective function for optimization. One dimensional optimization contains one variable and a problem having more than one variable requires multi-dimensional approach. As the number of