I would like to start work on a GA framework. This has been on the WishList for some time and I now have a TLS type problem that I am working on that is enough motivation for me personally to get things rolling. Here are some rough ideas on what I have in mind. As always, feedback / suggestions / patches appreciated.
0) Scope - a framework for implementing genetic algorithms, as described in e.g. [1], [2]. 1) Usage / what the framework provides - user classes should implement minimally encoding and objective functions. Stock mutation, crossover and selection functions operating on binary encodings should be provided by the framework, as well as execution of the algorithm, but user classes should be able to plug in custom encoding, mutation, crossover, and selection. Objective and other functions should also be allowed to have parameters, which the framework will somehow gather and pass in to them. 2) Other - the framework should not require that populations be stored in memory and it should support parallelization of population processing functions 3) Package - o.a.c.math.genetic (I am open to putting this inside optimization, but think flatter might be better here). 4) Structure straw man: Chromosomes - Chromosome interface includes mutate(), crossover(Chromosome) and fitness(). AbstractBinaryChromosome includes stock implementations of mutate and crossover for binary encodings. Populations - Population interface provides a Chromosome Iterator, select(long), add(Chromosome), delete(Chromosome). Abstract classes provide Roulette, Tournament, other select implementations for Populations backed / not backed by in memory collections and I/O / storage management. GeneticAlgorithm - implements GA (following "canonical algorithm" as described in [2]) given initial Population and configured sampling rate and stopping criteria (maximum iterations, convergence criteria). Population and Chromosome classes determine the heuristics. Should (eventually at least) support parallel execution by worker threads of breeding operations. This may be naive and I may find that out as I start playing with real code. As I said, feedback / patches welcome. One thing I don't have a clean idea for is how to make optional parameters available to all methods mentioned above. Phil [1] http://en.wikipedia.org/wiki/Genetic_algorithm [2] http://samizdat.mines.edu/ga_tutorial/ga_tutorial.ps --------------------------------------------------------------------- To unsubscribe, e-mail: [EMAIL PROTECTED] For additional commands, e-mail: [EMAIL PROTECTED]
