CHROMOSOME REPRESENTATION LEARNING IN EVOLUTIONARY OPTIMIZATION TO EXPLOIT THE STRUCTURE OF ALGORITHM CONFIGURATION
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United States of America Patent
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Issued Date -
Feb 29, 2024
app pub date -
Aug 31, 2022
filing date -
Aug 31, 2022
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Abstract
Principal component analysis (PCA) accelerates and increases accuracy of genetic algorithms. In an embodiment, a computer generates many original chromosomes. Each original chromosome contains a sequence of original values. Each position in the sequences in the original chromosomes corresponds to only one respective distinct parameter in a set of parameters to be optimized. Based on the original chromosomes, many virtual chromosomes are generated. Each virtual chromosome contains a sequence of numeric values. Positions in the sequences in the virtual chromosomes do not correspond to only one respective distinct parameter in the set of parameters to be optimized. Based on the virtual chromosomes, many new chromosomes are generated. Each new chromosome contains a sequence of values. Each position in the sequences in the new chromosomes corresponds to only one respective distinct parameter in the set of parameters to be optimized. The computer may be configured based on a best new chromosome.
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