Genetic algorithms using Golang Generics

Package genetic

Package genetic implements genetic algorithms using Golang's Generics support.

What are Genetic Algorithms?

Genetic algorithms are a form of machine learning software inspired by the process of natural selection. GA's can quickly approximate solutions for problems with very large search spaces. They are often used to tackle NP problems whose solutions can vary in levels of quality. In these kinds of problems, measuring a solution's validity and quality are fast, but yet there is no easy way to determine the optimal solution without guessing and checking. Genetic Algorithms provide a means to get a pretty good solution to any such problem. Examples include:

  • Transportation routing & logistics
  • Storage Optimization (Knapsack Problem)
  • 'Travelling Salesman' problems
  • Machine Learning meta-parameter tuning
  • Hardware and Engineering Design optimization
  • Scheduling
Owner
Konnor Klashinsky
Konnor Klashinsky
https://github.com/kklash/genetic
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Comments
  • feat: FitnessFunc can be dynamic with changing gene pool

    feat: FitnessFunc can be dynamic with changing gene pool

    Summary

    Currently the fitness function signature only allows for static fitness functions which are deterministically derived from the genome itself. But in situations where genomes must compete for scarce resources, like natural selection, the fitness of any one genome is mutually dependent on the fitness of every other genome.

    Details

    This PR changes the function signature of FitnessFunc[T].

    Old:

    type FitnessFunc[T any] func(genome T) int

    New:

    type FitnessFunc[T any] func(allGenomes []T, fitnesses []int)

    This way, fitness functions now have full context on the population, and can calculate fitness of each genome in a dynamic and customizable way. The results should be stored in the given fitnesses slice.

    For old-fashioned static fitness functions, a utility is available to convert them to the new FitnessFunc[T] type, optimizing to avoid recalculating the fitness of any cached elite genomes.

    // StaticFitnessFunc is a utility which maps a static non-competitive fitness function,
// whose output is not dependent on other competing genomes, into a FitnessFunc[T].
// Use this if your genomes' fitnesses are measured independently of the wider population.
func StaticFitnessFunc[T any](fitness func(T) int) FitnessFunc[T]

    Compatibility

    This change is backwards and forwards incompatible with master; this will need a new major version release.

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