# Evolutionary Search

In modular assembly the robot's task order must respect assembly precedence and be reordered when conditions change after work begins. Constraint-Aware Genetic Task Planning (CGTP) keeps initialization and variation inside the precedence-feasible region and, on replanning, freezes completed operations and re-optimizes only the remaining task graph. On an exhaustively verifiable eight-task benchmark it returned the exact best feasible suffix in all 128 structurally recoverable scenarios and flagged all 3 unrecoverable ones; on a 90-instance corpus, per-class recovery ran from 0.881 to 1.000 with no budget-conditional search failures.

In modular assembly the robot's task order must respect assembly precedence and be reordered when conditions change after work begins. Constraint-Aware Genetic Task Planning (CGTP) keeps initialization and variation inside the precedence-feasible region and, on replanning, freezes completed operations and re-optimizes only the remaining task graph. On an exhaustively verifiable eight-task benchmark it returned the exact best feasible suffix in all 128 structurally recoverable scenarios and flagged all 3 unrecoverable ones; on a 90-instance corpus, per-class recovery ran from 0.881 to 1.000 with no budget-conditional search failures.

Workflow selection for LLM agents is recast as a contextual bandit over a reusable pool, reaching an 81.37% average across five benchmarks while cutting MATH training-phase token overhead by at least 40.55%.

Workflow selection for LLM agents is recast as a contextual bandit over a reusable pool, reaching an 81.37% average across five benchmarks while cutting MATH training-phase token overhead by at least 40.55%.

The work introduces SEDIMA, a persistent hierarchical insight memory for LLM-driven evolutionary search agents that distills raw traces into natural-language insights, clusters them by semantic similarity using attention-weighted centroids, and retrieves relevant guidance to condition future mutations, accumulating transferable knowledge across runs and problems; as a drop-in module that leaves search operators unmodified, it improves average final performance by 5.5% on AlgoTune and 6.6% on ALE-Bench LITE under a fixed budget of 100 evaluated candidates, and under OpenEvolve requires 32.3% fewer iterations on average to reach baseline-best performance across the five evaluated backbones.

The work introduces SEDIMA, a persistent hierarchical insight memory for LLM-driven evolutionary search agents that distills raw traces into natural-language insights, clusters them by semantic similarity using attention-weighted centroids, and retrieves relevant guidance to condition future mutations, accumulating transferable knowledge across runs and problems; as a drop-in module that leaves search operators unmodified, it improves average final performance by 5.5% on AlgoTune and 6.6% on ALE-Bench LITE under a fixed budget of 100 evaluated candidates, and under OpenEvolve requires 32.3% fewer iterations on average to reach baseline-best performance across the five evaluated backbones.