In evolution strategies, the individuals are coded as vectors of real numbers. On reproduction, parents are selected randomly and the fittest offsprings are selected and inserted in the next generation. ES individuals are self-adapting. The step size or “mutation strength” is encoded in the individual, so good parameters get to the next generation by selecting … Read more
If you are sure you want to do this, you want genetic programming, rather than a genetic algorithm. GP allows you to evolve tree-structured programs. What you would do would be to give it a bunch of primitive operations (while($register), read($register), increment($register), decrement($register), divide($result $numerator $denominator), print, progn2 (this is GP speak for “execute two … Read more
This is essentially a sequence prediction problem, so you want Recurrent neural networks or hidden Markov models. If you only have a fixed time to look back, time window approaches might suffice. You take the sequence data and split it into overlapping windows of length n. (eg. you split a sequence ABCDEFG into ABC, BCD, … Read more
It’s been a few years since i’ve done this myself, however the following pseudo code was found easily enough on google. for all members of population sum += fitness of this individual end for for all members of population probability = sum of probabilities + (fitness / sum) sum of probabilities += probability end for … Read more
This is something I have been considering in my own research, and I’d say there are many reasons: The vast majority of research in the GP field has focused on producing formulas rather than the sort of software that gets produced by most programmers. There are plenty of computer scientists in the field, but very … Read more
After developing my own Genetic Programming didactic application, I found a complete Genetic Programming Framework called AForge.NET Genetics. It’s a part of the Aforge.NET library. It’s licensed under LGPL.
A genetic algorithm is a class of evolutionary algorithm. Although genetic algorithms are the most frequently encountered type of evolutionary algorithm, there are other types, such as Evolution Strategy. So, evolutionary algorithms encompass genetic algorithms, and more.
Not homework. My first job as a professional programmer (1995) was writing a genetic-algorithm based automated trading system for S&P500 futures. The application was written in Visual Basic 3 [!] and I have no idea how I did anything back then, since VB3 didn’t even have classes. The application started with a population of randomly-generated … Read more