TheAlgorithms/Python · error · ValueError

{N_POPULATION} must be bigger than {N_SELECTED}

Error message

{N_POPULATION} must be bigger than {N_SELECTED}

What it means

Raised by basic() in genetic_algorithm/basic_string.py when N_POPULATION < N_SELECTED. The selection step picks N_SELECTED parents from the population each generation; selecting more individuals than exist would either crash the random sampler or destroy the gene pool, so the function validates this precondition first.

Source

Thrown at genetic_algorithm/basic_string.py:125

    Traceback (most recent call last):
        ...
    ValueError: ['e'] is not in genes list, evolution cannot converge
    >>> genes.remove("s")
    >>> basic("test", genes)
    Traceback (most recent call last):
        ...
    ValueError: ['e', 's'] is not in genes list, evolution cannot converge
    >>> genes.remove("t")
    >>> basic("test", genes)
    Traceback (most recent call last):
        ...
    ValueError: ['e', 's', 't'] is not in genes list, evolution cannot converge
    """

    # Verify if N_POPULATION is bigger than N_SELECTED
    if N_POPULATION < N_SELECTED:
        msg = f"{N_POPULATION} must be bigger than {N_SELECTED}"
        raise ValueError(msg)
    # Verify that the target contains no genes besides the ones inside genes variable.
    not_in_genes_list = sorted({c for c in target if c not in genes})
    if not_in_genes_list:
        msg = f"{not_in_genes_list} is not in genes list, evolution cannot converge"
        raise ValueError(msg)

    # Generate random starting population.
    population = []
    for _ in range(N_POPULATION):
        population.append("".join([random.choice(genes) for i in range(len(target))]))

    # Just some logs to know what the algorithms is doing.
    generation, total_population = 0, 0

    # This loop will end when we find a perfect match for our target.
    while True:
        generation += 1
        total_population += len(population)

View on GitHub (pinned to f5988cc097)

Solutions

1. Ensure N_POPULATION > N_SELECTED; a common healthy ratio is population 5-10x selected.
2. Re-read the defaults in the module (300 vs 50) before overriding only one of them.
3. Scale both together: if you shrink population to 50, drop selected to ~10.

Example fix

# before
basic('test', ascii_letters, N_POPULATION=50, N_SELECTED=100)  # inverted

# after
basic('test', ascii_letters, N_POPULATION=200, N_SELECTED=50)

if N_POPULATION <= N_SELECTED:
    raise ValueError(
        f'need N_POPULATION > N_SELECTED, got {N_POPULATION} <= {N_SELECTED}'
    )
basic(target, genes, N_POPULATION, N_SELECTED)

try:
    basic('test', genes, pop, sel)
except ValueError as exc:
    if 'must be bigger' in str(exc):
        sel = max(1, pop // 4)
        basic('test', genes, pop, sel)
    else:
        raise

When it happens

Trigger: Calling basic('test', genes, N_POPULATION=100, N_SELECTED=200) or via the module defaults (N_POPULATION=300, N_SELECTED=50 — safe) with custom values where selected exceeds population.

Common situations: Tuning GA hyperparameters and shrinking the population for speed while leaving selection pressure high, or swapping the two values when calling.

Related errors

• {not_in_genes_list} is not in genes list, evolution cannot c
• Expected a_coeffs to have {self.order + 1} elements for {sel
• n must not be negative
• Candidates list should not be empty
• Depth cannot be less than 0

AI-assisted analysis of TheAlgorithms/Python@f5988cc097 (2026-08-14). Data as JSON: /api/errors/aadd245b29336135. Report an issue: GitHub.