LLMigrate: Large Language Models as Migration Controllers in Island-Based Evolutionary Design of Soft Robots

Evolving Multi-Agent Systems with Large Language Models: Surrogate Fitness Limitations and Empirical Insights

We investigate the use of large language models (LLMs) as evolutionary operators for optimizing the architecture of multi-agent systems (MAS) - a representative instance of LLM-driven optimization over attributed-graph artifacts that arise in generative design. Given a task description, an initial MAS configuration is generated and then refined on a per-task basis through two optimization strategies: (1) a population-based evolutionary approach where LLMs serve as mutation, crossover, and selection operators, with an LLM-as-a-Judge ensemble providing the surrogate fitness signal; and (2) a simpler iterative regeneration scheme that uses textual feedback from LLM judges to guide single-solution improvement without maintaining a population. We evaluate both approaches on the GAIA benchmark. Our experiments reveal a critical sensitivity to the judge model's quality: a capable judge (Claude 4 Sonnet) yields +14.3 and +6.67 percentage-point accuracy improvements for evolutionary and iterative approaches, respectively, whereas a weaker judge (Gemini-2.5-Flash) provides no benefit or even degrades performance below the unoptimized baseline. Both approaches incur substantial computational costs, raising questions about practical cost-effectiveness. These results highlight fundamental challenges of surrogate fitness functions in LLM-driven evolutionary optimization and offer practical insights for applying evolutionary methods to MAS architecture design.

CCGP: A Cooperative Coevolutionary Cartesian Genetic Programming Algorithm for Finite State Machine Synthesis

The automated synthesis of sequential circuits remains a core challenge in electronic design automation. When synthesizing finite state machines (FSMs), traditional Cartesian genetic programming (CGP) typically relies on a monolithic representation that couples state and output logic into a single high-dimensional decision vector. This approach triggers a search space “curse of dimensionality” and induces significant evaluation noise due to intense variable interactions, severely limiting scalability in complex logic design. To address these issues, we propose a cooperative coevolutionary Cartesian genetic programming (CCGP) algorithm. Using the divide-and-conquer mechanism, CCGP functionally decomposes the FSM into two parallel-evolving subpopulations based on the Mealy machine model: next-state logic (NSL) and output logic (OL). To overcome the subcomponent fitness evaluation challenge in coevolution, we introduce a cross-population elite ensemble mechanism. By evaluating candidate individuals against an “elite committee” sampled from the partner population, this method significantly reduces random fitness variance and stabilizes selection pressure. Experimental evaluations across a diverse set of sequential circuit benchmarks demonstrate that CCGP significantly outperforms standard CGP and genetic programming (GP). Results show substantial improvements in both success rates and convergence stability. These findings prove that CCGP’s functional decoupling effectively prevents evolutionary stagnation caused by functional conflicts, drastically enhancing the discovery of globally optimal solutions and providing an efficient, robust new paradigm for automated sequential logic design.

Beyond Scalar Fitness: Behavioural Feedback for LLM-Driven Metaheuristic Evolution

It is found that neutral framing of behavioural features, reporting the feature value without prescriptive advice, consistently outperforms the prescriptive variants, and that prescriptive feedback steers the median feature value in the advised direction in only 37% of cases despite empirically grounded advice.

Large language model enhanced heuristic synergy orchestrator for adaptive operator evolution

Particle Swarm Optimization with Population Dynamics - Artificial Splitting, Extinction, and Migration

Many Particle Swarm Optimization (PSO) variants often fail to approach the global optimum due to complex fitness landscapes and/or the structural rigidity of these variants. We developed a novel framework named PSO-SEM, centered on macro-evolutionary population management. PSO-SEM introduces three landscape-driven operators: Split (Topological Fission), Extinction (Density-driven Recycling), and Migration (Knowledge Transfer). These operators autonomously regulate the lifecycle of sub-swarms to intensify search in high-potential areas by identifying promising basins and recycling computational resources from stagnant regions. Experimental results show that PSO-SEM achieves a top-tier ranking and demonstrates significant competitiveness against 14 state-of-the-art algorithms. Behavioral monitoring and ablation studies confirm PSO-SEM's ability to maintain autonomous exploration/exploitation balance through landscape-based computational resource re-allocation. Our findings verify that PSO-SEM is an interpretable architecture that meets the diverse requirements of black-box optimization.

LLM-Driven Configuration of Genetic Algorithms for Constraint-Rich Optimization Problems

A framework that combines large language models (LLMs) for problem understanding with a structured Biased Random-Key Genetic Algorithm (BRKGA) configurator for algorithm realization is presented, allowing users to describe optimization problems in natural language and receive executable GPU-accelerated GA implementations.