Basic concepts of genetic algorithms

Basic concepts of genetic algorithms - enetic operators for...

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Basic concepts of genetic algorithms
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ey elements Key elements lobal optimization achieved by population of Global optimization achieved by population of designs that explore simultaneously different regions in design space. Discrete ‘genes’ that allow modeling integer and discrete variables. In global optimization you usually do not know when to stop Better to perform multiple short runs than one very long one.
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rgon Jargon esign variables become “genes” Design variables become genes Design becomes “chromosomes” Combining design attributes from two designs becomes “crossover” Introducing random changes becomes “mutation” Objective function becomes “fitness”
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Unformatted text preview: enetic operators for creation of Genetic operators for creation of child designs • Crossover: portions of strings of the two parents are exchanged • Mutation: the value of one bit (gene) is changed at random • Permutation: the order of a portion of the chromosome is reversed • Addition/deletion: one gene is added to/removed from the chromosome lgorithm of standard GA Algorithm of standard GA 40 100 30 reate initial alculate 70 Select parents Create initial population Calculate fitness Create children...
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Basic concepts of genetic algorithms - enetic operators for...

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