Findings demonstrate the potential of agentic AI to translate domain knowledge into generating executable components, providing a step toward automated configuration and design of evolutionary systems.
Abstract
We investigate whether agentic artificial intelligence can automate parts of the process of designing genetic programming systems by introducing an agentic framework that identifies and implements parent selection algorithms using large language model (LLM) reasoning and retrieval-augmented generation. Using symbolic regression as a test bed, we first conduct an ablation study across four LLM types to evaluate the effects of agentic reasoning and retrieval on generated algorithm categories, validity, implementation similarity, and downstream performance. Results show that these components substantially influence the types of algorithms generated, but their downstream performance largely depends on the underlying LLM. The strongest configuration, the full agentic setup with 5 mini (5 mini--AR), consistently generated established $\epsilon$-lexicase implementations while maintaining competitive downstream performance. We then benchmark this configuration against fixed implementations of tournament selection and semi-dynamic MAD $\epsilon$-lexicase. Across six symbolic regression problems, 5 mini--AR performed similarly to $\epsilon$-lexicase while generally outperforming tournament selection. These findings demonstrate the potential of agentic AI to translate domain knowledge into generating executable components, providing a step toward automated configuration and design of evolutionary systems.
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.
V. Akhmerov, Dmitry Gilemkhanov, Jerzy Kamiński et al.· GECCO Companion· 0 citations
This research presents an autonomous AI Coding Agent which establishes a connection between LLM-generated content and production-ready software through its organized methodology for decision making through its tailored Monte Carlo Tree Search method.
Pravin Game, V. Ramakrishnan, Prathamesh Wagh· 0 citations
Genetic Programming (GP) can be used to evolve human-interpretable Behavior Tree controllers for realtimestrategy games. Current Behavior Trees-based GP approaches compute fitness only at the end ofeach episode, which does not allow learning from finer-grained tactical decisions during gameplay. In thiswork, we integrate tabular Q-learning within the BT controller to gate actions at terminal nodes based onlearned action-values, and collect additional rewards at each tick of the game that are used to augmentthe final fitness signal. Since the Q-table is reset at the start of each episode, our approach allows thelearned information to affect fitness, while keeping the learned values from one generation separate fromsubsequent generations (a form of Baldwinian learning). Applied to plain-terrain MicroRTS micromanagementchallenge (population of 100 BTs, 2000 generations, against a deterministic rush opponent), ourapproach achieves a maximum fitness of 26.44 compared to GP’s max fitness of 22.0, a relative improvementof 20.2%. Mean best-fitness was also increased by 17.8% compared to the GP-only baseline. Theseresults are reported from a single representative run per configuration; multi-seed replication is identifiedas a priority for future work. The additional per-episode RL reward signal is strongly correlated with elitefitness (r = 0.89), confirming that it provides informative guidance for the evaluation. Unit coordinationexhibited by the hybrid agents is also found to be more structured, with defined front-line and support roles.The agent maintains its interpretable Behavior Tree structure.
Mohamed Amine Chikh Touami, M. Salem, M. Khelfi· Informatica· 0 citations
Developments in artificial intelligence have made it possible for autonomous systems to compose, test, and refine code, but all current methods are based on single-agent designs or weak multi-agent systems with little cooperation and poor validation procedures. Single-agent designs lack internal diversity and poor reasoning, whereas small multi-agent designs are mostly competitive rather than cooperative and lack strong role specialization to facilitate complicated iterative improvement. The work presented here provides a five-agent collaborative architecture to provide continuous and verifiable code optimization by controlled specialization and iterative refinement. The architecture consists of five specialized agents, including the planner agent, the coder agent, the tester agent, the reviewer agent, and the explainer agent. Each agent plays a differentiated role in an integrated process of feedback where code is conceived, developed, tested, examined, and explained in an iterative process until optimality is reached. The improvement process continues until an internally optimal solution is obtained, and the reviewer agent then checks its validity individually and passes it on to the explainer agent for explainability and documentation, else the system goes through the improvement cycle again. This composition unifies independent thinking, responsibility, and transparency in an interdependent environment with lesser bias and more consistency of choice. The five-agent architecture realizes maximum heterogeneous viewpoints and distributed knowledge, producing better-quality, understandable, and verifiably optimal code than current single-agent or smaller multiagent approaches. Experiments show that continuous validation and specialization-based cooperation result in better results in automatically generated code and provides a scalable platform for intelligent, self-adjusting development environments with a 92
Jesalkumari Varolia· Journal of Intelligent Decis...· 0 citations
This work presents AI4AI\mbox{-}Bench, 10 frozen research repositories spanning 10 training algorithm families, and releases the task suite, the evaluators and every scored submission, so that the measurement can be repeated as these systems change.
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.
Siyu Lin, Huikun Huang, Xinyuan Zhang et al.· International Conference on...· 0 citations
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