Difference between genetic algorithm and other traditional methods for optimisation
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GA are radially different from traditional optimiztion methods.
Differences are
* GA's work with string coding of variables instead of variables.so that coding discretising the search space even though the function is continuous.
* GA's work with population of points instead of single point.
* In GA's previously found good infomation is emphasizedusing reproduction operator and propagated adaptively through crossover and mutation operators.
* GA does not require any auxillary informationexcept yhe objective function values. * GA uses the probabilities in their operators.
This nature of narrowing the search spaceas the search progresses ,is adaptive andis the unique characteristic of Genetic Algorithms.
Differences are
* GA's work with string coding of variables instead of variables.so that coding discretising the search space even though the function is continuous.
* GA's work with population of points instead of single point.
* In GA's previously found good infomation is emphasizedusing reproduction operator and propagated adaptively through crossover and mutation operators.
* GA does not require any auxillary informationexcept yhe objective function values. * GA uses the probabilities in their operators.
This nature of narrowing the search spaceas the search progresses ,is adaptive andis the unique characteristic of Genetic Algorithms.
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