Question 1

How do I set up Radiate for Python with custom builds?

Accepted Answer

Radiate uses Just for building from source; run 'just develop' or 'just release' with optional Python version arguments. For standard use, 'pip install radiate' works, but custom builds require installing Just, which adds complexity.

Question 2

Can Radiate be used for neuroevolution like NEAT?

Accepted Answer

Yes, Radiate supports neuroevolution with a graph-based representation similar to NEAT, as stated in the features. It allows evolving neural networks for tasks like reinforcement learning, but you'll need to define the network structure and fitness functions.

Question 3

What's the difference between Radiate and DEAP for genetic algorithms?

Accepted Answer

Radiate is Rust-based for higher performance and has built-in parallelism, while DEAP is Python-based with a larger community and more examples. Radiate offers novelty search and speciation out-of-the-box, but DEAP might be easier for quick prototyping in Python.

Question 4

How does Radiate handle multi-objective optimization?

Accepted Answer

Radiate provides support for optimizing multiple conflicting objectives, likely using algorithms like NSGA-II, though specific methods aren't detailed in the README. Check the user guide for implementation examples and configuration options.

Question 5

Is there GPU acceleration in Radiate?

Accepted Answer

The README doesn't mention GPU support; performance relies on Rust's speed and CPU parallelism. For GPU-accelerated evolutionary algorithms, you might need to extend the library or use other tools, as this isn't a built-in feature.

Question 6

How to track evolution metrics in Radiate?

Accepted Answer

Radiate offers first-class metric tracking via APIs that monitor fitness, diversity, and other statistics during evolution. Refer to the documentation or examples for code snippets on setting up and logging these metrics in real-time.

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