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GeneHunter 4
遺傳演繹應用軟體
Genetic algorithm methodology.
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Overview

 

 

GeneHunter is a powerful software solution for optimization problems which utilizes a state-of-the-art genetic algorithm methodology. GeneHunter includes an Excel Add-In which allows the user to run an optimization problem from Microsoft Excel, as well as a Dynamic Link Library of genetic algorithm functions that may be called from programming languages such as Microsoft® Visual Basic or C.

 

What Are Genetic Algorithms?Genetic algorithms (GAs) seek to solve optimization problems using the methods of evolution, specifically survival of the fittest. In a typical optimization problem, there are a number of variables which control the process, and a formula or algorithm which combines the variables to fully model the process. The problem is then to find the values of the variables which optimize the model in some way. If the model is a formula, then we will usually be seeking the maximum or minimum value of the formula. There are many mathematical methods which can optimize problems of this nature (and very quickly) for fairly "well-behaved" problems. These traditional methods tend to break down when the problem is not so "well-behaved."

Evolution and Genetic AlgorithmsBefore describing how a GA can be applied to an optimization problem, let us draw the evolutionary parallel. The theory is that a population of a certain species will, after many generations, adapt to live better in its environment.

For example, if the species is an animal that lives mainly in a swampy area, it may eventually evolve with webbed feet. The reason is that the members of this population, which we call individuals, will tend to die if they are poor swimmers which cannot easily get food, and live to reproduce if they are good swimmers. The offspring of two good swimmers will probably be good swimmers because they will usually carry genetic traits of their parents, such as slight webbing between their toes. These genetic traits are carried in the chromosomes of the individuals.

How Does GeneHunter Work?GeneHunter solves optimization problems in the same way. It will create a population of possible solutions to the problem. The individuals in this population will carry chromosomes which are the values of variables of the problem.

GeneHunter actually solves your problem by allowing the less fit individuals in the population to die, and selectively breeding the most fit individuals. The process is called selection, as in selection of the fittest. GeneHunter takes two individuals and mates them (crossover), the offspring of the mated pair will receive some of the characteristics of the mother and some of the father.

In nature, offspring often have some slight abnormalities, called mutations. Usually, these mutations are disabling and inhibit the ability of the offspring to survive, but once in a while, they improve the fitness of the individual. GeneHunter occasionally causes mutations to occur.

As GeneHunter mates fit individuals and mutates some, the population undergoes a generation change. The population will then consist of offspring plus a few of the older individuals which GeneHunter allows to survive to the next generation. These are the most fit in the population, and we will want to keep them breeding. These most fit individuals are called elite individuals. After dozens or even hundreds of "generations," a population eventually emerges wherein the individuals will solve the problem very well. In fact, the most fit (elite) individual will be an optimum or close to optimum solution.