Jul 2026· International Conference on Ubiquitous and Future Networks· pp. 527-532· 0 citations· 19 references
Abstract
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.
Logic Gate Networks (LGNs) are an appealing family of artificial intelligence models that efficiently perform inference, by replacing floating-point multiply-accumulate operations with Boolean ones. Despite their performance and efficiency, training still represents a problem: the selection and wiring of logic gates are discrete design choices, making the direct use of gradient-based optimization difficult, at least in its native form. This paper provides an initial investigation on whether Cartesian Genetic Programming (CGP) can be turned into a practical, gradient-free framework for evolving layered LGNs for image classification. To this purpose, we extend a CGP implementation with data handling functions specific for the modified National Institute of Standards and Technology (MNIST) dataset, redundant multi-bit output decoding, custom loss functions, batch-based evaluation, layered connectivity constraints, and adaptive mutation control. Experiments on binarized MNIST show that these design choices substantially improve the search process. In the final configuration, the proposed method reaches more than 80% test accuracy with a fully discrete evolutionary pipeline executed on a single processor core (CPU), providing a concrete indication that non-trivial logical classifiers can be obtained without back-propagation, Graphics Processing Units (GPUs), or continuous relaxations.
Francesco Lubrano, Emanuel Messetti, A. Scionti et al.· Proceedings of the Genetic a...· 0 citations
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.
Jose Guadalupe Hernandez, Jui-Hsuan Chang, A. Saini et al.· 0 citations
This work proposes an LLM-guided migration controller for island-model evolutionary design, where an LLM receives compact summaries of island states including fitness statistics and morphology information and outputs migration decisions, and positions LLMs as coordinators of inter-island information flow in evolutionary robotics.
Emir Buğra Şahin, Ege Demir, Bilgehan Altıntaş et al.· Proceedings of the Genetic a...· 0 citations
According to Mendelian principles of controlled inheritance, Mendel G\"odel Machine (MGM) is introduced, which includes two new types of self-modification that better utilizes evidences accumulated and facilitates a faster and better convergence over single-trajectory baselines.
Changzhi Liu, Yilun Liu, Sikuan Yan et al.· 0 citations
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
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
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