Module ga 

Real-valued Genetic Algorithm.

A canonical textbook GA over Tensor<B, 2> populations:

1. Evaluate the current population (done externally by the harness).
2. Select parents via crate::ops::selection::tournament_select.
3. Recombine via crate::ops::crossover::blx_alpha or crate::ops::crossover::uniform_crossover.
4. Mutate via crate::ops::mutation::gaussian_mutation.
5. Replace via crate::ops::replacement::elitist or crate::ops::replacement::generational.

Operator variants are enum-selected via the GaConfig to avoid a generic explosion; custom operator mixtures can still be built bottom-up against the Strategy trait directly.

References

• Goldberg (1989), Genetic Algorithms in Search, Optimization, and Machine Learning.
• Deb & Agrawal (1995), Simulated binary crossover for continuous search space.

Structs

GaConfig

Static configuration for a GeneticAlgorithm run.

GaState

Generation-to-generation state carried by GeneticAlgorithm.

GeneticAlgorithm

Real-valued canonical Genetic Algorithm.

Enums

GaCrossover

Crossover algorithm choice.

GaReplacement

Replacement algorithm choice.

GaSelection

Selection algorithm choice.