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Partially mapped crossover

As explained in Evolutionary Deep Learning: Genetic algorithms and neural networks

Partially matched crossover recombines indexed parent sequences while preserving matching positional relationships between exchanged segments.

Partially mapped crossover recombines indexed parent sequences while matching genes by position. It is useful when alignment matters because it preserves important index sequences instead of disrupting evolved paths. section 3.4

Use the operator with these practical distinctions:

  • Apply it primarily to indexed sequence genes; it may not work as expected with other gene types. section 3.4
  • Prefer the uniform partially matched variant when a more balanced exchange of parent sequences is useful, while recognizing that early evolution may become difficult. section 3.4
  • Compare results with ordered crossover, which preserves order while allowing rotation, and avoid generic one- or two-point crossover for indexed sequences. section 3.4

Inspecting crossover outcomes

Use genealogy plots to check whether the chosen crossover produces viable, fit offspring and whether evolution flows from less-fit to more-fit generations. Comparing genealogy plots helps reveal differences between crossover operations. section 3.4

How Evolutionary Deep Learning: Genetic algorithms and neural networks covers it

How deep it goes
Defines the operator, compares variants, and evaluates offspring through genealogy plots without detailing implementation code.
Examples
Uses indexed gene sequences, contrasting crossover operators, and genealogy plots showing offspring across generations.
What it assumes
Assumes genetic-algorithm populations and gene sequences while distinguishing indexed genes from other gene types.
What you can do after
Evaluate crossover choices with genealogy plots and select operators suited to indexed sequences.
How it connects
Connects genetic algorithms with indexed sequences, uniform crossover, ordered crossover, point crossover, and genealogy plots.

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Related concepts

This explainer was generated by Manning's knowledge graph from the text of Evolutionary Deep Learning: Genetic algorithms and neural networks. The section links point to the parts of the book it draws on.