Genetic Programming in C# [closed]

Answer 1

You might be able to implement genetic programming using LINQ expression trees -- it's more likely to generate something usable than random IL generation.

Answer 2

I would recommend against actually generating assemblies unless you absolutely need to, particularly if you are just getting started with implementing the genetic algorithm.

The genetic algorithm is easiest to implement when the target language is functional and dynamically typed. That is generally why most genetic algorithm research is written in LISP. As a result, if you are going to implement it in C#, you are probably better off defining your own mini "tree language", having the algorithm generate trees, and just interpreting the trees when it comes time to run each iteration of the algorithm.

I did a project like this when I was in college (an implementation of the genetic algorithm in C#), and that was the approach I took.

Doing it that way will give you the advantage of only having 1 representation to work with (the AST representation) that is optimally suited for both execution and the genetic algorithm "reproduction" steps.

Alternatively, if you try to generate assemblies you are probably going to end up adding a large amount of unneeded complexity to the app. Currently, the CLR does not allow an assembly to be unloaded from an App domain unless the entire app domain is destroyed. This would mean that you would need to spin up a separate app domain for each generated program in each iteration of the algorithm to avoid introducing a giant memory leak into your app. In general, the whole thing would just add a bunch of extra irritation.

Interpreted AST's, on the other hand, are garbage collectible just like any other object, and so you wouldn't need to monkey around with multiple app domains. If, for performance reasons you want to code-gen the final result you can add support for that later. However, I you would recommend that you do that using the DynamicMethod class. It will allow you to convert an AST into a compiled delegate dynamically at runtime. That will enable you to deploy a single DLL while keeping the code generation stuff as simple as possible. Also, DynamicMethod instances are garbage collectible so you could end up employing them as part of the genetic algorithm to speed things up there as well.

Answer 3

I maintain a port of ECJ in C#. It's great.

Answer 4

If you're interested in genetic algorithms or heuristic optimization in general you might want to take a look at HeuristicLab. It is developed for several years, 1.5 years since we released the new version. It is programmed in C# 4 and has a nice GUI. There are many algorithms already available like Genetic Algorithm, Genetic Programming, Evolution Strategy, Local Search, Tabu Search, Particle Swarm Optimization, Simulated Annealing and more. There are also several problems implemented like a vehicle routing problem, traveling salesman, real function optimization, knapsack, quadratic assignment problem, classification, regression, and many more. There are tutorials also and we have protocol buffers integrated so you can communicate with external programs for solution evaluation. It is licensed under GPL. In 2009 the software has received the Microsoft innovation award of Microsoft Austria.

We've also written a book on the subject: Genetic Algorithms and Genetic Programming.

Answer 5

You can try GeneticSharp.

It has all classic GA operations, like selection, crossover, mutation, reinsertion and termination.

It's very extensible, you can define your own chromosomes, fitness function, population generation strategy and all cited operations above too.

It can be used in many kind of apps, like C# libraries and Unity 3D games, there is samples running it in a GTK# app and Unity 3D checkers game.

It also works in Win and OSX.

Here is a basic sample how to use the library:

var selection = new EliteSelection();
var crossover = new OrderedCrossover();
var mutation = new ReverseSequenceMutation();
var fitness = new YourFitnessFunction();
var chromosome = new YourChromosome();
var population = new Population (50, 70, chromosome);

var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);

ga.Start();

Answer 6

The Manning book: "Metaprogramming in .NET" dedicates a large section on GP via expression trees.