Simple subpackage

The metaheuristic_designer.simple module contains ready‑to‑run functions that return a configured Algorithm. All functions follow a strict naming pattern so you can call any algorithm without looking up a separate catalogue.

Naming convention

Every function is named like this:

simple.<algorithm>_<encoding>(objfunc, **kwargs) # kwargs passed to Algorithm

• <algorithm> – the algorithm family (e.g. genetic_algorithm).

• <encoding> – the problem representation you want to use:

  • real – continuous variables

  • binary – bit‑strings

  • discrete – integer variables

  • permutation – permutations of integers

The table below lists every algorithm family, the encodings it supports, and the corresponding function names. You can copy any function name directly into your code.

Available algorithms

Algorithm family	Function names (“simple.” prefix omitted)	Short description
Random Search	random_search_real, random_search_binary, random_search_discrete, random_search_permutation	Independent random sampling each generation.
Hill Climbing	hill_climb_real, hill_climb_binary, hill_climb_discrete, hill_climb_permutation	Single‑solution greedy local optimisation.
Local Search	local_search_real, local_search_binary, local_search_discrete, local_search_permutation	Multiple perturbations around a single solution per iteration.
Simulated Annealing	simulated_annealing_real, simulated_annealing_binary, simulated_annealing_discrete, simulated_annealing_permutation	Accepts worse solutions with a decaying temperature.
Evolution Strategy (ES)	evolution_strategy_real, evolution_strategy_binary, evolution_strategy_discrete, evolution_strategy_permutation	(μ+λ) / (μ,λ) strategy with mutation, optional crossover.
Genetic Algorithm (GA)	genetic_algorithm_real, genetic_algorithm_binary, genetic_algorithm_discrete, genetic_algorithm_permutation	Tournament selection, crossover, mutation, elitism.
Differential Evolution (DE)	differential_evolution_real, differential_evolution_binary, differential_evolution_discrete	Population‑based method using difference vectors. No permutation variant.
Particle Swarm (PSO)	particle_swarm_real, particle_swarm_binary, particle_swarm_discrete	Swarm intelligence with inertia weight and acceleration coefficients. No permutation variant.
Bayesian Optimisation	bayesian_optimization_real	Gaussian‑process surrogate with Expected Improvement. Only continuous.

Example

The following snippet minimises the 5‑dimensional Sphere function using a real‑coded Genetic Algorithm:

from metaheuristic_designer.benchmarks import Sphere
from metaheuristic_designer import simple, check_random_state

objfunc = Sphere(dimension=5, mode="min")
rng = check_random_state(42)

algo = simple.genetic_algorithm_real(
    objfunc,
    population_size=100,
    max_iterations=100,
    reporter="tqdm",
    random_state=rng,
)
population = algo.optimize()
solution, obj = population.best_solution()
print(f"Best objective: {obj:.6g}")

All additional keyword arguments (such as max_iterations, reporter, real_time_limit, …) are passed directly to the Algorithm constructor. See the Algorithm Configuration page for a full description of what you can configure.

Each function also accepts algorithm‑specific hyper‑parameters (mutation strength, population size, number of mutated components, …).

For a complete list of available arguments, use Python’s built‑in help, e.g. help(simple.genetic_algorithm_real).