Optimization Algorithm

[chriskelly]

Goal

Looking to create algorithm for optimizing adjustable parameters. This would help find high-performing parameter combinations faster than manually trying out combinations.

Requirements

• Existing Monte Carlo simulator would provide the judgement result of the parameter combination.
• Should be able to define unique range for each parameter and interval qty/spacing

  Considerations

• Some parameters won't cause any changes depending on the state of a different parameter (useless genes). For example, if the 'Life Cycle' allocation mode is not chosen, then altering 'Equity Target' parameter would cause no changes.

  Options

  Genetic Algorithm

  A genetic algorithm treats the parameters as genes that can be mutated in a hyper-volume of possibilities. Different algorithms implement different approaches with evolutionary traits such as cross-breeding, parenting, and mutation.

  DIY

  Create my own algorithm specialized for this project

• Full control of evolutionary traits
• May not need to modify parameter management

  PyGAD

  Open source algorithm with many options for evolution

• Documentation is not clean on how 'genes' need to be fed to the library
• Unclear if I'll be able to clearly provide max, min, and intervals of each parameter
• Unlikely to be able to prevent running useless gene combinations.

[chriskelly]

Built my own for now: genetic.py