The paper investigates the ability of evolutionary computation to discover monotone Boolean functions with high nonlinearity, demonstrating that genetic programming is a highly effective encoding for…

The paper introduces CHECKMATE, a novel framework that uses code evolution to automatically generate and optimize algorithms for complex combinatorial problems, outperforming state-of-the-art solvers.

The paper empirically and theoretically demonstrates that incorporating Lamarckian and Baldwinian mechanisms into evolutionary algorithms significantly outperforms standard Darwinian evolution, especi…

AI-PROPELLER introduces a novel interprocedural code layout optimization system that uses an agentic evolutionary workflow to achieve significant, measurable performance gains in large-scale, real-wor…

This paper introduces the first LLM-generated, domain-independent heuristics for symbolic AI planning, using evolutionary search to surpass the performance of hand-engineered state-of-the-art methods.

BenchEvolver introduces a solution-centric evolutionary framework to automatically transform saturated coding benchmarks into significantly harder, high-quality, and diverse evaluation suites.

The paper proposes CoEvo-AHD, an LLM-driven co-evolutionary framework that co-evolves two coupled operator populations to design effective heuristics for combinatorial optimization problems with stron…

The paper introduces Atomic Decomposition and Recombination (ADR), a novel framework that generates genuinely novel and challenging verifiable code tasks, significantly improving the scalability of Re…

The paper proposes BaSE, a multi-armed bandit approach, to optimally allocate a fixed budget of LLM calls across parallel evolutionary search trajectories, significantly improving mean fitness and rel…

The paper demonstrates that using Reinforcement Learning from Verifiable Rewards (RLVR) significantly improves small language models' functional correctness in code generation, particularly when combi…

This paper enhances a genetic algorithm approach for solving the Shortest Vector Problem (SVP) in lattices by incorporating domain-informed representation, thereby extending its applicability to modul…

This paper enhances a genetic algorithm approach for solving the Shortest Vector Problem (SVP) in both integral and module lattices by incorporating domain-informed representation and crossover.

This paper analyzes the performance and explainability requirements of evolutionary algorithms when applied to complex, real-world physics-informed optimization problems, identifying a gap between cur…

The paper introduces an LLM-guided evolutionary workflow that successfully discovers and certifies a large number of novel bivariate quantum error-correcting codes, demonstrating the utility of LLMs i…

EvoGens is an evolution-inspired framework that treats scientific idea generation as an evolutionary search, significantly boosting the novelty and diversity of generated research ideas compared to ex…

StepPRM-RTL is a novel framework that enhances LLM-based RTL code generation for digital hardware designs.

The paper introduces AlphaEvolve, an evolutionary search framework that automates the optimization of Fully Homomorphic Encryption (FHE) kernels on TPUs, achieving significant speedups over human-engi…