Genetic algorithm is a programming technique for problem solving by mimicking evolution. It has been used extensively in robotics and other applications where you would want to do away with hard coding certain procedures for obtaining solution and leave the program to decide the best solution on its own.
Given any problem to solve, potential solutions are encoded and fed into the program. These potential solutions are called genotypes and the set of all genotypes is called the population of the GA. This population is then 'evolved' using a fitness function and other techniques which changes the population to a new population with better average fitness value. One complete cycle which changes the old population to a new one is taken as one generation.
The 'evolution' is done by using a combination of two techniques which also draw inspiration from biology. The first is the recombination method which is the basically
recombining the genotypes to produce a new genotype. Mutation is the second method involved which is basically mutating the genotype to any possible value permitted by the encoding standard.
This procedure is repeated over many generations. So, in course of time, the GA will optimize its population to one containing comparatively larger number of better solution candidates.
As GA is basically a type of optimization method, it is not suitable for all types of problem. A good description of the types of problems suitable for genetic algorithm can be found here.
A nice general description of GA as well as an example of how a GA solve a problem plus codes in Delphi and java is available here
Given any problem to solve, potential solutions are encoded and fed into the program. These potential solutions are called genotypes and the set of all genotypes is called the population of the GA. This population is then 'evolved' using a fitness function and other techniques which changes the population to a new population with better average fitness value. One complete cycle which changes the old population to a new one is taken as one generation.
The 'evolution' is done by using a combination of two techniques which also draw inspiration from biology. The first is the recombination method which is the basically
recombining the genotypes to produce a new genotype. Mutation is the second method involved which is basically mutating the genotype to any possible value permitted by the encoding standard.
This procedure is repeated over many generations. So, in course of time, the GA will optimize its population to one containing comparatively larger number of better solution candidates.
As GA is basically a type of optimization method, it is not suitable for all types of problem. A good description of the types of problems suitable for genetic algorithm can be found here.
A nice general description of GA as well as an example of how a GA solve a problem plus codes in Delphi and java is available here
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