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Understanding the Code

Let's break down what each part does:

1. Task Creation

task = ScientificRegressionTask(dataset_name="bactgrow")

A Task defines what problem you're solving and how to evaluate solutions. ScientificRegressionTask is a built-in task for discovering mathematical equations from real scientific datasets. The datasets are automatically downloaded on first use (lazy loading).

2. Interface Creation

interface = EvoEngineerPythonInterface(task)

An Interface connects your task to a specific evolutionary algorithm. Here we use EvoEngineerPythonInterface for the EvoEngineer algorithm.

3. LLM Configuration

llm_api = HttpsApi(
    api_url="https://api.openai.com/v1/chat/completions",
    key="your-api-key-here",
    model="gpt-4o"
)

This sets up the LLM API client that will generate and improve code solutions. Replace your-api-key-here with your actual API key.

4. Solving the Problem

result = evotoolkit.solve(
    interface=interface,
    output_path='./results',
    running_llm=llm_api,
    max_generations=5,
    max_sample_nums=10,
    pop_size=5
)

The evotoolkit.solve() function:

  • Runs the evolutionary algorithm for 5 generations
  • Uses a population size of 5
  • Samples up to 10 LLM responses per generation
  • Saves results to ./results/

Next: Exploring the Results