miesmuschel offers both an Optimizer and a Tuner for general
MIES-optimization, as well as all the building blocks for building a custom optimization algorithm that
is more flexible and can be used for research into novel evolution strategies.
The call-graph of the default algorithm in OptimizerMies / TunerMies is as follows, and is shown
here as an overview over the mies_* functions, and how they are usually connected. (Note that only the
exported mies_* functions are shown.) See the help information of these functions for more info.
OptimizerMies$.optimize(inst)
|- mies_prime_operators() # prime operators on instance's search_space
|- mies_init_population() # sample and evaluate first generation
| `- mies_evaluate_offspring() # evaluate sampled individuals
| `- inst$eval_batch() # The OptimInst's evaluation method
`- repeat # Repeat the following until terminated
|- mies_step_fidelity() # Evaluate individuals with changing fidelity
| `- inst$eval_batch() # The OptimInst's evaluation method
|- mies_generate_offspring() # Sample parents, recombine, mutate
| `- mies_select_from_archive() # Use 'Selector' on 'Archive'
| `- mies_get_fitnesses() # Get objective values as fitness matrix
|- mies_evaluate_offspring() # evaluate sampled individuals
| `- inst$eval_batch() # The OptimInst's evaluation method
`- mies_survival_plus() / mies_survival_comma() # survival
`- mies_select_from_archive() # Use 'Selector' on 'Archive'Useful links: