Optimisation

Optimisers summary

To solve the optimisation problems 4 optimisers are available in GEFEST - 1 native and 2 based on GOLEM. All of them have a single interface and can be imported from gefest.tools.optimizers.

Optimizers comparation
	`BaseGA`	`StandardOptimizer`	`SurrogateOptimizer`
Backend	GEFEST	GOLEM	GOLEM
Muti objective	`Yes`	`Yes`	`Yes`
Evolutionary schemes	`No`	`Yes`	`Yes`
Adaptive mutation strategies	`No`	`Yes`	`Yes`
Surrogate optimisation	`No`	`No`	`Yes`

BaseGA implements the base genetic algorithm, that performs sampling of the initial population, crossover and mutation operations, fitness estimation and selection. Each of the steps is encapsulated in a separate executor, which allows you to change the logic of individual steps. Thus, BaseGA essentially only implements the sequence of their call.

StandardOptimizer is a wrapper for GOLEM`s EvoGraphOptimizer. It allows to use different evolutionary schemes, adaptive mutation strategies and some other features. To use multiobjective optimisation set golem_selection_type in OptimizationParams config to ‘spea2’.

SurrogateOptimizer is the extension of StandardOptimizer with the ability to use a surrogate model to evaluate fitness along with the main estimator.

Selectors summary

OptimizationParams have 3 parameters to configure selection strategy:

golem_selection_type defines which selection function will be used by GOLEM optimisers. Available values: ‘spea2’ for multi objective and ‘tournament’ for single objective problems.
selector defines which selection function will be used by GEFEST for single objective problems and also for multi objective fitnesses if it possible. Available values: ‘tournament_selection’ and ‘roulette_selection’.
multiobjective_selector defines which selection function will be used by GEFEST for multiobjective problems. Available values: ‘spea2’ and ‘moead’.

How to optimise

Easiest way to run optimiser described in Quickstart.

If you want to get some more control you can do it in your code:

from gefest.tools.optimizers BaseGA, StandardOptimizer, SurrogateOptimizer

from gefest.core.configs.optimization_params import OptimizationParams
from gefest.core.geometry.datastructs.structure import Structure

opt_params: OptimizationParams = ...

optimizer = BaseGA(opt_params)
optimized_population = optimizer.optimize(n_steps=42)

By default initial population generates automatically with sampler from opt_params. It also can be provided as optional argument for optimiser constructor.