Controlled Self-Evolution for Algorithmic Code Optimization
Self-evolution methods enhance code generation through iterative "generate-verify-refine" cycles, yet existing approaches suffer from low exploration efficiency, failing to discover solutions with superior complexity within limited budgets. This inefficiency stems from initialization bias trapping evolution in poor solution regions, uncontrolled stochastic operations lacking feedback guidance, and insufficient experience utilization across tasks. To address these bottlenecks, we propose Controlled Self-Evolution (CSE), which consists of three key components. Diversified Planning Initialization generates structurally distinct algorithmic strategies for broad solution space coverage. Genetic Evolution replaces stochastic operations with feedback-guided mechanisms, enabling targeted mutation and compositional crossover. Hierarchical Evolution Memory captures both successful and failed experiences at inter-task and intra-task levels. Experiments on EffiBench-X demonstrate that CSE consistently outperforms all baselines across various LLM backbones. Furthermore, CSE achieves higher efficiency from early generations and maintains continuous improvement throughout evolution. Our code is publicly available at https://github.com/QuantaAlpha/EvoControl.
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Tu Hu
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2026-01-15T15:03:18.320Z
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Tu Hu
Blackteaxxx
user
Blackteaxxx
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Ronghao Chen
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Shuo Zhang
claimed_verified
2026-01-15T15:03:16.329Z
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zhangshuo
mcflurryshuoz
user
mcflurryshuoz
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Jianghao Yin
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Mou Xiao Feng
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Jingping Liu
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Shaolei Zhang
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Wenqi Jiang
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Yuqi Fang
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Sen Hu
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Yi Xu
696855610ac10a06522f69da
Huacan Wang
claimed_verified
2026-01-15T15:03:20.275Z
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/avatars/1569bb60166532317c85e80da722ba1c.svg
Huacan Wang
Huacan-Wang
user
Huacan-Wang
Controlled Self-Evolution for Algorithmic Code Optimization
Self-evolution methods enhance code generation through iterative "generate-verify-refine" cycles, yet existing approaches suffer from low exploration efficiency, failing to discover solutions with superior complexity within limited budgets. This inefficiency stems from initialization bias trapping evolution in poor solution regions, uncontrolled stochastic operations lacking feedback guidance, and insufficient experience utilization across tasks. To address these bottlenecks, we propose Controlled Self-Evolution (CSE), which consists of three key components. Diversified Planning Initialization generates structurally distinct algorithmic strategies for broad solution space coverage. Genetic Evolution replaces stochastic operations with feedback-guided mechanisms, enabling targeted mutation and compositional crossover. Hierarchical Evolution Memory captures both successful and failed experiences at inter-task and intra-task levels. Experiments on EffiBench-X demonstrate that CSE consistently outperforms all baselines across various LLM backbones. Furthermore, CSE achieves higher efficiency from early generations and maintains continuous improvement throughout evolution. Our code is publicly available at https://github.com/QuantaAlpha/EvoControl.
Controlled Self-Evolution method improves code generation through diversified initialization, feedback-guided genetic evolution, and hierarchical memory to enhance exploration efficiency and solution quality.
self-evolution methods generate-verify-refine cycles exploration efficiency initialization bias stochastic operations feedback guidance genetic evolution targeted mutation compositional crossover hierarchical evolution memory LLM backbones EffiBench-X