January 2020
Intermediate to advanced
346 pages
9h 8m
English
Content preview from Hands-On Genetic Algorithms with Python
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Solution representation
The objective of our algorithm is to find a subset of features that yield the best performance. Therefore, a solution needs to indicate which features are chosen and which are dropped. One obvious way to go about this is to represent each individual using a list of binary values. Every entry in that list corresponds to one of the features in the dataset. A value of 1 represents selecting the corresponding feature, while a value of 0 means that the feature has not been selected. This is very similar to the approach we used in the knapsack 0-1 problem we described in Chapter 4, Combinatorial Optimization.
The presence of each 0 in the solution will be translated into dropping the corresponding feature's data column from ...
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