update tests and controller

Author: 0x0f0f0f

README.md

@@ -9,6 +9,7 @@ An open-source implementation of the AlphaEvolve system described in the Google
 OpenEvolve is an evolutionary coding agent that uses Large Language Models to optimize code through an iterative process. It orchestrates a pipeline of LLM-based code generation, evaluation, and selection to continuously improve programs for a variety of tasks.
 
 Key features:
+
 - Evolution of entire code files, not just single functions
 - Support for multiple programming languages
 - Supports OpenAI-compatible APIs for any LLM
@@ -34,6 +35,7 @@ The controller orchestrates interactions between these components in an asynchro
 ### Installation
 
 To install natively, use:
+
 ```bash
 git clone https://github.com/codelion/openevolve.git
 cd openevolve
@@ -51,7 +53,7 @@ from openevolve import OpenEvolve
 
 # Initialize the system
 evolve = OpenEvolve(
-    initial_program_path="path/to/initial_program.py",
+    initial_programs_paths=["path/to/initial_program.py"],
     evaluation_file="path/to/evaluator.py",
     config_path="path/to/config.yaml"
 )
@@ -83,6 +85,7 @@ python openevolve-run.py path/to/initial_program.py path/to/evaluator.py \
 ```
 
 When resuming from a checkpoint:
+
 - The system loads all previously evolved programs and their metrics
 - Checkpoint numbering continues from where it left off (e.g., if loaded from checkpoint_50, the next checkpoint will be checkpoint_60)
 - All evolution state is preserved (best programs, feature maps, archives, etc.)
@@ -145,6 +148,7 @@ python scripts/visualizer.py --path examples/function_minimization/openevolve_ou
 ```
 
 In the visualization UI, you can
+
 - see the branching of your program evolution in a network visualization, with node radius chosen by the program fitness (= the currently selected metric),
 - see the parent-child relationship of nodes and click through them in the sidebar (use the yellow locator icon in the sidebar to center the node in the graph),
 - select the metric of interest (with the available metric choices depending on your data set),
@@ -157,6 +161,7 @@ In the visualization UI, you can
 ### Docker
 
 You can also install and execute via Docker:
+
 ```bash
 docker build -t openevolve .
 docker run --rm -v $(pwd):/app --network="host" openevolve examples/function_minimization/initial_program.py examples/function_minimization/evaluator.py --config examples/function_minimization/config.yaml --iterations 1000
@@ -179,6 +184,7 @@ database:
 ```
 
 Sample configuration files are available in the `configs/` directory:
+
 - `default_config.yaml`: Comprehensive configuration with all available options
 
 See the [Configuration Guide](configs/default_config.yaml) for a full list of options.
@@ -205,18 +211,23 @@ return EvaluationResult(
 ```
 
 The next generation prompt will include:
+
 ```markdown
 ## Last Execution Output
+
 ### Stderr
+
 SyntaxError: invalid syntax (line 15)
 
 ### Traceback
+
 ...
 ```
 
 ## Example: LLM Feedback
 
 An example for an LLM artifact side channel is part of the default evaluation template, which ends with
+
 ```markdown
 Return your evaluation as a JSON object with the following format:
 {{
@@ -226,6 +237,7 @@ Return your evaluation as a JSON object with the following format:
     "reasoning": "[brief explanation of scores]"
 }}
 ```
+
 The non-float values, in this case the "reasoning" key of the json response that the evaluator LLM generates, will be available within the next generation prompt.
 
 ### Configuration
@@ -239,7 +251,7 @@ evaluator:
 
 prompt:
   include_artifacts: true
-  max_artifact_bytes: 4096  # 4KB limit in prompts
+  max_artifact_bytes: 4096 # 4KB limit in prompts
   artifact_security_filter: true
 ```
 
@@ -266,6 +278,7 @@ A comprehensive example demonstrating OpenEvolve's application to symbolic regre
 [Explore the Symbolic Regression Example](examples/symbolic_regression/)
 
 Key features:
+
 - Automatic generation of initial programs from benchmark tasks
 - Evolution from simple linear models to complex mathematical expressions
 - Evaluation on physics, chemistry, biology, and material science datasets

configs/README.md

@@ -5,23 +5,29 @@ This directory contains configuration files for OpenEvolve with examples for dif
 ## Configuration Files
 
 ### `default_config.yaml`
+
 The main configuration file containing all available options with sensible defaults. This file includes:
+
 - Complete documentation for all configuration parameters
 - Default values for all settings
 - **Island-based evolution parameters** for proper evolutionary diversity
 
 Use this file as a template for your own configurations.
 
 ### `island_config_example.yaml`
+
 A practical example configuration demonstrating proper island-based evolution setup. Shows:
+
 - Recommended island settings for most use cases
 - Balanced migration parameters
 - Complete working configuration
 
 ### `island_examples.yaml`
+
 Multiple example configurations for different scenarios:
+
 - **Maximum Diversity**: Many islands, frequent migration
-- **Focused Exploration**: Few islands, rare migration  
+- **Focused Exploration**: Few islands, rare migration
 - **Balanced Approach**: Default recommended settings
 - **Quick Exploration**: Small-scale rapid testing
 - **Large-Scale Evolution**: Complex optimization runs
@@ -34,9 +40,9 @@ The key new parameters for proper evolutionary diversity are:
 
 ```yaml
 database:
-  num_islands: 5                      # Number of separate populations
-  migration_interval: 50              # Migrate every N generations  
-  migration_rate: 0.1                 # Fraction of top programs to migrate
+  num_islands: 5 # Number of separate populations
+  migration_interval: 50 # Migrate every N generations
+  migration_rate: 0.1 # Fraction of top programs to migrate
 ```
 
 ### Parameter Guidelines
@@ -66,8 +72,8 @@ Then use with OpenEvolve:
 ```python
 from openevolve import OpenEvolve
 evolve = OpenEvolve(
-    initial_program_path="program.py",
-    evaluation_file="evaluator.py", 
+    initial_program_paths=["program.py"],
+    evaluation_file="evaluator.py",
     config_path="my_config.yaml"
 )
 ```

openevolve/cli.py

@@ -19,11 +19,15 @@ def parse_args() -> argparse.Namespace:
     """Parse command-line arguments"""
     parser = argparse.ArgumentParser(description="OpenEvolve - Evolutionary coding agent")
 
-    parser.add_argument("initial_program", help="Path to the initial program file", default=None)
-
     parser.add_argument(
         "evaluation_file", help="Path to the evaluation file containing an 'evaluate' function"
     )
+    parser.add_argument(
+        "initial_program",
+        nargs="?",
+        help="Path to the initial program file",
+        default=None,
+    )
 
     parser.add_argument("--config", "-c", help="Path to configuration file (YAML)", default=None)
 

openevolve/controller.py

@@ -250,7 +250,13 @@ async def run(
 
         if should_add_initial:
             logger.info("Adding initial programs to database")
-            for code in self.initial_programs_code:
+
+            if len(self.initial_programs_code) > len(self.database.islands):
+                raise ValueError(
+                    "Number of initial programs exceeds number of islands."
+                )
+
+            for i, code in enumerate(self.initial_programs_code):
                 initial_program_id = str(uuid.uuid4())
 
                 # Evaluate the initial program
@@ -267,7 +273,7 @@ async def run(
                 )
 
                 # TODO. Should the island be incremented and reset here?
-                self.database.add(initial_program)
+                self.database.add(initial_program, 0, i)
         else:
             logger.info(
                 f"Skipping initial program addition (resuming from iteration {start_iteration} "

tests/test_checkpoint_resume.py

@@ -86,7 +86,7 @@ async def run_test():
                 mock_evaluator_class.return_value = mock_evaluator
 
                 controller = OpenEvolve(
-                    initial_program_path=self.test_program_path,
+                    initial_programs_paths=[self.test_program_path],
                     evaluation_file=self.evaluator_path,
                     config=self.config,
                     output_dir=self.test_dir,
@@ -127,7 +127,7 @@ async def run_test():
                 mock_evaluator_class.return_value = mock_evaluator
 
                 controller = OpenEvolve(
-                    initial_program_path=self.test_program_path,
+                    initial_programs_paths=[self.test_program_path],
                     evaluation_file=self.evaluator_path,
                     config=self.config,
                     output_dir=self.test_dir,
@@ -169,7 +169,7 @@ async def run_test():
                 mock_evaluator_class.return_value = mock_evaluator
 
                 controller = OpenEvolve(
-                    initial_program_path=self.test_program_path,
+                    initial_programs_paths=[self.test_program_path],
                     evaluation_file=self.evaluator_path,
                     config=self.config,
                     output_dir=self.test_dir,
@@ -219,7 +219,7 @@ async def run_test():
                 mock_evaluator_class.return_value = mock_evaluator
 
                 controller = OpenEvolve(
-                    initial_program_path=self.test_program_path,
+                    initial_programs_paths=[self.test_program_path],
                     evaluation_file=self.evaluator_path,
                     config=self.config,
                     output_dir=self.test_dir,
@@ -269,7 +269,7 @@ async def run_test():
                 mock_evaluator_class.return_value = mock_evaluator
 
                 controller = OpenEvolve(
-                    initial_program_path=self.test_program_path,
+                    initial_programs_paths=[self.test_program_path],
                     evaluation_file=self.evaluator_path,
                     config=self.config,
                     output_dir=self.test_dir,