HackerReposA FIELD GUIDE TO OPEN SOURCE
ProSponsor the shelf♧ 0
← Back to top all-time
jchester
ruby-ackley-genetic-algorithm
Ruby2012–2014
jchester / ruby-ackley-genetic-algorithm

ruby-ackley-genetic-algorithm

A Genetic Algorithm for the Ackley Optimisation Problem, written in Ruby.

Libraries & SDKs
Explore on GitHubRead Hacker News discussions
2Distinct people
3Mentions
0Explicit recommenders
0GitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

A Genetic Algorithm for the Ackley Optimisation Problem, written in Ruby.

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

Mentioned in the same Hacker News threads. That is not proof they are substitutes.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 6:01:08 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by month3 dated mentions

Observed mentions: 2012-12-15 05:12:43 UTC to 2014-12-06 21:04:01 UTC.

0122012-12-01 00:00:00 UTC to 2013-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 commentsDec 20122013-01-01 00:00:00 UTC to 2013-02-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2013-02-01 00:00:00 UTC to 2013-03-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2013-03-01 00:00:00 UTC to 2013-04-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2013-04-01 00:00:00 UTC to 2013-05-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2013-05-01 00:00:00 UTC to 2013-06-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2013-06-01 00:00:00 UTC to 2013-07-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2013-07-01 00:00:00 UTC to 2013-08-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2013-08-01 00:00:00 UTC to 2013-09-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2013-09-01 00:00:00 UTC to 2013-10-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2013-10-01 00:00:00 UTC to 2013-11-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2013-11-01 00:00:00 UTC to 2013-12-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2013-12-01 00:00:00 UTC to 2014-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 commentsDec 20132014-01-01 00:00:00 UTC to 2014-02-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2014-02-01 00:00:00 UTC to 2014-03-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2014-03-01 00:00:00 UTC to 2014-04-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2014-04-01 00:00:00 UTC to 2014-05-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2014-05-01 00:00:00 UTC to 2014-06-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2014-06-01 00:00:00 UTC to 2014-07-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2014-07-01 00:00:00 UTC to 2014-08-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2014-08-01 00:00:00 UTC to 2014-09-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2014-09-01 00:00:00 UTC to 2014-10-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2014-10-01 00:00:00 UTC to 2014-11-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2014-11-01 00:00:00 UTC to 2014-12-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2014-12-01 00:00:00 UTC to 2015-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsDec 2014

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
Dec 20122012-12-01 00:00:00 UTC2013-01-01 00:00:00 UTC202
Jan 20132013-01-01 00:00:00 UTC2013-02-01 00:00:00 UTC000
Feb 20132013-02-01 00:00:00 UTC2013-03-01 00:00:00 UTC000
Mar 20132013-03-01 00:00:00 UTC2013-04-01 00:00:00 UTC000
Apr 20132013-04-01 00:00:00 UTC2013-05-01 00:00:00 UTC000
May 20132013-05-01 00:00:00 UTC2013-06-01 00:00:00 UTC000
Jun 20132013-06-01 00:00:00 UTC2013-07-01 00:00:00 UTC000
Jul 20132013-07-01 00:00:00 UTC2013-08-01 00:00:00 UTC000
Aug 20132013-08-01 00:00:00 UTC2013-09-01 00:00:00 UTC000
Sep 20132013-09-01 00:00:00 UTC2013-10-01 00:00:00 UTC000
Oct 20132013-10-01 00:00:00 UTC2013-11-01 00:00:00 UTC000
Nov 20132013-11-01 00:00:00 UTC2013-12-01 00:00:00 UTC000
Dec 20132013-12-01 00:00:00 UTC2014-01-01 00:00:00 UTC000
Jan 20142014-01-01 00:00:00 UTC2014-02-01 00:00:00 UTC000
Feb 20142014-02-01 00:00:00 UTC2014-03-01 00:00:00 UTC000
Mar 20142014-03-01 00:00:00 UTC2014-04-01 00:00:00 UTC000
Apr 20142014-04-01 00:00:00 UTC2014-05-01 00:00:00 UTC000
May 20142014-05-01 00:00:00 UTC2014-06-01 00:00:00 UTC000
Jun 20142014-06-01 00:00:00 UTC2014-07-01 00:00:00 UTC000
Jul 20142014-07-01 00:00:00 UTC2014-08-01 00:00:00 UTC000
Aug 20142014-08-01 00:00:00 UTC2014-09-01 00:00:00 UTC000
Sep 20142014-09-01 00:00:00 UTC2014-10-01 00:00:00 UTC000
Oct 20142014-10-01 00:00:00 UTC2014-11-01 00:00:00 UTC000
Nov 20142014-11-01 00:00:00 UTC2014-12-01 00:00:00 UTC000
Dec 20142014-12-01 00:00:00 UTC2015-01-01 00:00:00 UTC101

What Hacker News says

1 sources
jacques_chester·Dec 6, 2014
I took a course on this stuff in uni, it was so fantastically fun to play with. Simulated annealing has less cachet than GAs (and other evolution-inspired approaches) but tends to perform well enough on lots of problems that you might as well skip GAs. I haven't dug much on the linked GA, but it looks like it converges too quickly. That's a classic problem (exploration vs exploitation, aka coverage vs convergence) in optimisation problems. The problem I practiced on was Ackley's Function[1]. Even in 2 dimensions it's really good at tripping up algorithms that converge too eagerly. Some GA methods actually make the parameters of the system part of the genome, but as I recall it doesn't make a huge difference overall. Fun to think about though. Mind you, the No Free Lunch Theorem means that there's a place for everyone in the heuristic comp
Read the rest
uting tent. And simple random sampling (monte carlo method) and/or graphing can be very helpful for looking for the contours of a solution space. There's still room for humans too. [1] https://github.com/jchester/ruby-ackley-genetic-algorithm
In Grow Your Own Picture – Genetic Algorithms and Generative Art

MAKE YOURSELF AT HOME

A few shortcuts

/
Search the collection
?
Open this guide
Esc
Close dialogs and navigation

Your saved repositories and interests stay in this browser.