From 74b540ad73bd3b1187ed6e3c89bb8f309ef543fd Mon Sep 17 00:00:00 2001
From: Tony Dang <62843153+Dang-Hoang-Tung@users.noreply.github.com>
Date: Sat, 29 Mar 2025 08:13:47 +0000
Subject: [PATCH] Genetic Algorithm: Fix bug in multi-threading (#12644)
* Fix bug in multi-threading
- Multi-threading (despite being commented out) had a tiny bug: missing target argument (2nd argument).
- Commented out code was also slightly hard to understand, added (Option 1/2) in comments to clarify where a user may choose between 2 implementations.
* Update basic_string.py
---------
Co-authored-by: Maxim Smolskiy <mithridatus@mail.ru>
---
genetic_algorithm/basic_string.py | 6 +++---
1 file changed, 3 insertions(+), 3 deletions(-)
diff --git a/genetic_algorithm/basic_string.py b/genetic_algorithm/basic_string.py
index a906ce85a779..b75491d9a949 100644
--- a/genetic_algorithm/basic_string.py
+++ b/genetic_algorithm/basic_string.py
@@ -144,18 +144,18 @@ def basic(target: str, genes: list[str], debug: bool = True) -> tuple[int, int,
# Random population created. Now it's time to evaluate.
- # Adding a bit of concurrency can make everything faster,
+ # (Option 1) Adding a bit of concurrency can make everything faster,
#
# import concurrent.futures
# population_score: list[tuple[str, float]] = []
# with concurrent.futures.ThreadPoolExecutor(
# max_workers=NUM_WORKERS) as executor:
- # futures = {executor.submit(evaluate, item) for item in population}
+ # futures = {executor.submit(evaluate, item, target) for item in population}
# concurrent.futures.wait(futures)
# population_score = [item.result() for item in futures]
#
# but with a simple algorithm like this, it will probably be slower.
- # We just need to call evaluate for every item inside the population.
+ # (Option 2) We just need to call evaluate for every item inside the population.
population_score = [evaluate(item, target) for item in population]
# Check if there is a matching evolution.