Module evolution_model 

Integration with Fugue’s Model<T> monad

This module provides probabilistic evolutionary models that integrate with Fugue’s inference engine (SMC, MCMC).

Key Concepts

• Evolution as Inference: Treat evolution as posterior sampling
• Fitness as Likelihood: Selection pressure = Bayesian conditioning
• Operators as Kernels: Mutation/crossover define proposal distributions

Structs

EvolutionChainConfig

Configuration for the evolutionary Markov chain

EvolutionModel

A probabilistic model of an evolutionary step

EvolutionStep

A single step in the evolutionary Markov chain

EvolutionarySMC

Sequential Monte Carlo for evolutionary inference

HBGA

Hierarchical Bayesian Genetic Algorithm

HBGAResult

Result of HBGA run

Particle

Particle representation for Sequential Monte Carlo