Any users new to the GA world are encouraged to read David Goldberg's "Genetic Algorithms in Search, Optimization and Machine Learning," Addison-Wesley, 1989.
My favorite technique is what I call the “securGA”, but I also still recommend using the older micro-GA technique (microga=1) with uniform crossover (iunifrm=1). However, if possible, I strongly suggest that you use values of nposibl of 2^n (2, 4, 8, 16, 32, 64, etc.). While my test function works fine for other values of nposibl, I have encountered problems where the uniform crossover micro-GA has difficulty with parameters having long bit strings and a non-2^n value of nposibl, e.g. nposibl=1000, will have 10 bits assigned (for this case I would suggest running nposibl=1024 rather than 1000).
For more conventional GA techniques I recommend using:
- Binary coding (only option with my GA, but it could be changed to floating point coding)
- Tournament selection (only option with my GA)
- Uniform crossover (iunifrm=1)
- Creep mutations (icreep=1)
- Niching or sharing (iniche=1)
- Elitism (ielite=1)