Representation and Self-adaption in Genetic Algorithms
نویسنده
چکیده
Representation and reproduction operators are important issues in Genetic Algo-rithms(GAs). When optimising numerical functions some researchers advocate using oating point representation instead of bit-string representation. Floating point representation is also used in Evolutionary Strategies(ESs) and Evolutionary Program-ming(EP). We show that it is not the representation that is responsible for the improved performance, but the mutation operator. By using Gaussian mutation with bit-string representation we improve the performance of GAs. We then introduce self-adaption which raises the performance to that of ESs. We also show that keeping bit-string representation can have advantages to the eeciency of GAs.
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