Evolution Search

OptunaCmaEsSearch

Solver name is “OptunaCmaEs”.

class flopt.solvers.optuna_searches.OptunaCmaEsSearch

Covariance Matrix Adaptation Evolution Strategy (CMA-ES) search of Optuna. https://optuna.readthedocs.io/en/latest/reference/samplers/generated/optuna.samplers.CmaEsSampler.html

Parameters:

• sigma0 –

• n_startup_trials –

• independent_sampler –

• warn_independe_sampling –

• seed –

Examples

import flopt

x = flopt.Variable("x", lowBound=-1, upBound=1, cat="Continuous")
y = flopt.Variable("y", lowBound=-1, upBound=1, cat="Continuous")

prob = flopt.Problem()
prob += 2*x*x + x*y + y*y + x + y

status, log = prob.solve(solver="OptunaCmaEs", msg=True, timelimit=1)

print("obj =", flopt.Value(prob.obj))
print("x =", flopt.Value(x))
print("y =", flopt.Value(y))
>>> obj = -0.2857142857142857
>>> x = -0.14285714185152507
>>> y = -0.4285714299429902

OptunaNSGAIISearch

Solver name is “OptunaNSGAII”.

class flopt.solvers.optuna_searches.OptunaNSGAIISearch

Multi-objective sampler using the NSGA-II algorithm of Optuna. This solver can solve constrainted problems, but it cannot handle equal constraints well.

Parameters:

• name (str) – name

• population_size (int) –

• mutation_prob (float) –

• crossover –

• crossover_prob (float) –

• swapping_prob (float) –

• seed (float) – seed of random generater

Examples

import flopt

x = flopt.Variable("x", lowBound=-1, upBound=1, cat="Continuous")
y = flopt.Variable("y", lowBound=-1, upBound=1, cat="Continuous")

prob = flopt.Problem()
prob += 2*x*x + x*y + y*y + x + y
prob += x + y >= 0

status, log = prob.solve(solver="OptunaNSGAII", msg=True, timelimit=1)

print("obj =", flopt.Value(prob.obj))
print("x =", flopt.Value(x))
print("y =", flopt.Value(y))