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Genetic algorithms, evolutionarily stable strategies, and the loser/winner effects

(A long time ago, around the year 2000) I spent some time working on the loser/winner effects. This is a problem that requires game theory (what is a good strategy depends on what your opponents do) but I could not find simple analytical solutions. So I used genetic algorithms, which seems natural enough here since we are dealing with the evolution of behavioral strategies.

There are several libraries for genetic algorithms. I started using galib, a very nice library. However, I found it hard to use of it for my problem, where fitness is the result of repeated interactions between the genotypes (and not something you evaluate in a sweep over the population at each generation); this is doable with galib, but I found it hard and awkward. Thus, to learn more C++ and to have more control, I wrote a set classes and methods for genetic algorithms (ga.cpp, ga.h). And the code for the loser/winner part (fighting.cpp), plus a few helper functions, etc.

Please note that the documentation is non-existent (you'll need to read the comments), that there are a few comments in Spanish and spanglish, and that indentation and line width are "peculiar" (set to fit my monitor and usage of XEmacs). Its been a while since I worked on these issues. But I'd appreciate that, if you use this code, you let me know.

To run it you will need to install Blitz++ and libRmath, the stand-alone math library from R. If you use Debian GNU/Linux, this is (was) as easy as:

apt-get install blitz
apt-get install r-mathlib

I've also run it with other GNU/Linux distributions.

Download the code. This software is released under the GNU GPL.


Genetic algorithms, evolutionarily stable strategies, and the loser/winner effects




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