AgentGA: Evolving Code Solutions in Agent-Seed Space

We present AgentGA, a framework that evolves autonomous code-generation runs by optimizing the agent seed: the task prompt plus optional parent archives that initialize a fresh workspace. The outer loop searches over reusable starting conditions rather than editing code directly. Each generation launches a fresh autonomous run in an isolated workspace, while selected parent archives provide inherited artifacts that descendants can inspect and reuse. AgentGA couples a population-level genetic algorithm with long-horizon agents, deterministic 1:1 elite tournaments, and online operator allocation through a modified Hedge controller. We evaluate the approach for tabular AutoML on the 16-competition Weco-Kaggle Lite benchmark, including model-matched AIDE and AutoGluon comparisons. AgentGA with DeepSeek V4 Flash (0731) achieves 71.13% mean Exceeds % of Human, compared with 57.61% for model-matched AIDE and 57.89% for AutoGluon, outranking AIDE on 14/16 tasks. These descriptive results support agent-seed optimization as a practical design choice for autonomous code-search systems, without isolating the causal contribution of inheritance.