C# how to create functions that are interpreted at runtime

Question

I'm making a Genetic Program, but I'm hitting a limitation with C# where I want to present new functions to the algorithm but I can't do it without recompiling the program. In essence I want the user of the program to provide the allowed functions and the GP will automatically use them. It would be great if the user is required to know as little about programming as possible.

I want to plug in the new functions without compiling them into the program. In Python this is easy, since it's all interpreted, but I have no clue how to do it with C#. Does anybody know how to achieve this in C#? Are there any libraries, techniques, etc?

Answer 1

It depends on how you want the user of the program to "provide the allowed functions."

• If the user is choosing functions that you've already implemented, you can pass these around as delegates or expression trees.
• If the user is going to write their own methods in C# or another .NET language, and compile them into an assembly, you can load them using Reflection.
• If you want the user to be able to type C# source code into your program, you can compile that using CodeDom, then call the resulting assembly using Reflection.
• If you want to provide a custom expression language for the user, e.g. a simple mathematical language, then (assuming you can parse the language) you can use Reflection.Emit to generate a dynamic assembly and call that using -- you guessed it -- Reflection. Or you can construct an expression tree from the user code and compile that using LINQ -- depends on how much flexibility you need. (And if you can afford to wait, expression trees in .NET 4.0 remove many of the limitations that were in 3.5, so you may be able to avoid Reflection.Emit altogether.)
• If you are happy for the user to enter expressions using Python, Ruby or another DLR language, you can host the Dynamic Language Runtime, which will interpret the user's code for you.

Hosting the DLR (and IronPython or IronRuby) could be a good choice here because you get a well tested environment and all the optimisations the DLR provides. Here's a how-to using IronPython.

Added in response to your performance question: The DLR is reasonably smart about optimisation. It doesn't blindly re-interpret the source code every time: once it has transformed the source code (or, specifically, a given function or class) to MSIL, it will keep reusing that compiled representation until the source code changes (e.g. the function is redefined). So if the user keeps using the same function but over different data sets, then as long as you can keep the same ScriptScope around, you should get decent perf; ditto if your concern is just that you're going to run the same function zillions of times during the genetic algorithm. Hosting the DLR is pretty easy to do, so it shouldn't be hard to do a proof of concept and measure to see if it's up to your needs.