If the test isn't worth enough to update, then it probably isn't worth using. How many million did it cost to validate the original 10? According to the Wikipedia page the blots were made in 1921 with 400 subjects. That doesn't scream millions to me.
For a few million you might even be able to write software that would generate "inkblots", thereby solving the perceived weakness in the test.
I like Kontact, it has what I need and it's well designed. At any given point, however, I'm working from one of three computers. If I stored my calendars (or email) locally I'd have a problem.
Solution? egroupware on my server and sync Kontact with that.
I still don't have email quite figured out, currently I download it to each computer and then regularly clear off the POP3 account. My other problem is that and KMail doesn't seem to have a search-in-all-messages function.
The Zero Install system makes software installation not merely easy, but unnecessary. Users run their applications directly from the Internet from the software author's pages. Caching makes this as fast as running a normal application after the first time, and allows off-line use.
I missed a bit to respond to previously. You (the parent) cited a paper from 1994. This is too far back in GA history for GAs to be seen as robust searchers. That was aroung the time of the first truly usable population sizing algorithms which clearly showed that some algorithms being passed off as GAs were best-case exponential time algorithms for a class of simple problems (boundedly deceptive problems).
Time marches on and GAs are significantly more useful now than they were in 1994. You can't rely on data from that far back as your sole guide. Even though GAs are definitely worse than hill climbing for some problems (minimize x^2).
No, it is not. We have to pick our problems carefully to avoid running into No-Free-Lunch, but there are problems that hillclimbing is better on, and there are problems that GAs are better on.
If you're interested in toy problems, consider the simple "deceptive" problems of David Goldberg. These tend to be difficult for hill climbers, as the derivatives for decomposable segments lead to local optima. With folded traps, for instance, there are many many local optima. Bad for hill climbers.
Hill climbers are a great first thing to try with real world hard problems. But if they fail, GAs are a good tool to try. The only problem is that there are a lot of variants currently, and some research is not generally applicable. The field is immature.
Simmulated annealing, on the other hand, is a much more mature tool. Also very respectable and very useful.
If I am not being trolled, all I can recommend is reading more or, better yet, hire someone that actually, genuinely knows what they're talking about. Jürgen Branke is a perfect example, he has a very impressive track record.
Misses the point. GAs do not look at search spaces and ask 'is this function too bumpy'. GAs (as well as many other evolutionary algorithms, see GECCO) are more similar to stochastic greedy strategies. I'd suggest looking at Walsh coefficients instead of derivatives as a rule of thumb.
A large part of the reason GAs aren't catching on more is that there aren't enough really good text books on them. All of the really useful information is still in the form of research papers, these things take time. Also, we're only starting (last 5 years) to know enough to be able to routinely apply them. Without that, they're too hard and too expensive to use.
As for random search, good papers look at the scaling factor for their algorithms. The goal is to build GAs that are quadratic. Any paper which suggests an exponential time GA (like random search) is wasting your time. The idea is to have a range of algorithms, greedy search for logarithmic time (or gradient descent), GAs for quadratic, and enumerative search for NP-Hard problems.
The parent is either ignorant, overly jaded, or being a jerk.
It's easy in C++ as well. That's a single homework assignment in most intro to networks classes. Grab stdin, send it to a list of ips, have a single thread selecting on the list of sockets and dump the output to the chat window which also accepts user input.
Writing that code isn't really worth doing as you can just download a pre-written implementation, it's a common example program for many network libraries.
The other reply is, of course, correct. Jabber would be the way to go for this sort of thing.
Slashdot Mirror
If the test isn't worth enough to update, then it probably isn't worth using. How many million did it cost to validate the original 10? According to the Wikipedia page the blots were made in 1921 with 400 subjects. That doesn't scream millions to me.
For a few million you might even be able to write software that would generate "inkblots", thereby solving the perceived weakness in the test.
Already available as a bookmarklet: http://pastebin.com/m21e4f5c3
Written by Jesse Ruderman, https://www.squarefree.com/bookmarklets/
It's unfortunate that you chose "Juan" as the name of the worker to be fired whereas the two others where "white" names.
I like Kontact, it has what I need and it's well designed. At any given point, however, I'm working from one of three computers. If I stored my calendars (or email) locally I'd have a problem.
Solution? egroupware on my server and sync Kontact with that.
I still don't have email quite figured out, currently I download it to each computer and then regularly clear off the POP3 account. My other problem is that and KMail doesn't seem to have a search-in-all-messages function.
Zero Install does what I think you're looking for: http://zero-install.sourceforge.net/
The Zero Install system makes software installation not merely easy, but unnecessary. Users run their applications directly from the Internet from the software author's pages. Caching makes this as fast as running a normal application after the first time, and allows off-line use.
Very clever, very underutilized.
A package management system covers the libraries question. For Debian, try Orphaner and apt-cache show.
Frankly, however, who cares about libraries on a destop system? They're so small...
The eric3 Python IDE http://www.die-offenbachs.de/detlev/eric3.html
It uses QT so it's only free on Linux.
It's the reason I don't want to switch from Python, nothing else has as good an IDE.
I missed a bit to respond to previously. You (the parent) cited a paper from 1994. This is too far back in GA history for GAs to be seen as robust searchers. That was aroung the time of the first truly usable population sizing algorithms which clearly showed that some algorithms being passed off as GAs were best-case exponential time algorithms for a class of simple problems (boundedly deceptive problems).
Time marches on and GAs are significantly more useful now than they were in 1994. You can't rely on data from that far back as your sole guide. Even though GAs are definitely worse than hill climbing for some problems (minimize x^2).
No, it is not. We have to pick our problems carefully to avoid running into No-Free-Lunch, but there are problems that hillclimbing is better on, and there are problems that GAs are better on.
If you're interested in toy problems, consider the simple "deceptive" problems of David Goldberg. These tend to be difficult for hill climbers, as the derivatives for decomposable segments lead to local optima. With folded traps, for instance, there are many many local optima. Bad for hill climbers.
Hill climbers are a great first thing to try with real world hard problems. But if they fail, GAs are a good tool to try. The only problem is that there are a lot of variants currently, and some research is not generally applicable. The field is immature.
Simmulated annealing, on the other hand, is a much more mature tool. Also very respectable and very useful.
If I am not being trolled, all I can recommend is reading more or, better yet, hire someone that actually, genuinely knows what they're talking about. Jürgen Branke is a perfect example, he has a very impressive track record.
Misses the point. GAs do not look at search spaces and ask 'is this function too bumpy'. GAs (as well as many other evolutionary algorithms, see GECCO) are more similar to stochastic greedy strategies. I'd suggest looking at Walsh coefficients instead of derivatives as a rule of thumb.
A large part of the reason GAs aren't catching on more is that there aren't enough really good text books on them. All of the really useful information is still in the form of research papers, these things take time. Also, we're only starting (last 5 years) to know enough to be able to routinely apply them. Without that, they're too hard and too expensive to use.
As for random search, good papers look at the scaling factor for their algorithms. The goal is to build GAs that are quadratic. Any paper which suggests an exponential time GA (like random search) is wasting your time. The idea is to have a range of algorithms, greedy search for logarithmic time (or gradient descent), GAs for quadratic, and enumerative search for NP-Hard problems.
The parent is either ignorant, overly jaded, or being a jerk.
It's easy in C++ as well. That's a single homework assignment in most intro to networks classes. Grab stdin, send it to a list of ips, have a single thread selecting on the list of sockets and dump the output to the chat window which also accepts user input.
Writing that code isn't really worth doing as you can just download a pre-written implementation, it's a common example program for many network libraries.
The other reply is, of course, correct. Jabber would be the way to go for this sort of thing.
Python has code to break cycles. Added long ago.
The cyclic collector works separately, but it does collect cycles.