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Sponsored Design Multi-Agent AI Systems Using MCP and A2A: Engineer your own Pyt Design Multi-Agent AI Systems Using MCP and A2A: Engineer your own Pyt

Author

Szymon Łukasik

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Book Open access Jul 2026

A Probabilistic L-System Inspired Designer for Neural Architecture Search: A Graph Neural Network Case Study on Node Classification

A probabilistic L-system-inspired encoding for architecture search, where each individual is a small set of production rules and repeated rewriting generates a symbol sequence that is translated into a GNN.

Maciej Krzywda, Szymon Łukasik, Amir H. Gandomi · 0 citations
Book Open access Jul 2026

Self-Evolving Graph Neural Network Design Inspired by Neural Cellular Automata

A self-evolving neural architecture search framework inspired by NCA, in which a population of NCA-based controllers autonomously generates and improves candidate GNN architectures over successive generations, enabling progressive adaptation of the architecture distribution without hand-crafted design rules.

Maciej Krzywda, M. Wermiński, Szymon Łukasik et al. · 0 citations
Book Open access Jul 2026

TAPE-JB: Trait-Aligned Prompt Evolution for Jailbreaking Large Language Models

TAPE-JB (Trait-Aligned Prompt Evolution for Jailbreaking LLMs), a genotype-guided evolutionary framework for adversarial prompt generation that searches over structured prompt traits while preserving the original harmful intention is introduced, highlighting the value of structured evolutionary search for systematic re...

Karolina Seweryn, Anna Wróblewska, Szymon Łukasik · 0 citations
Book Open access Jul 2026

Surrogate-Assisted Linear Genetic Programming for Evolving Graph Neural Networks for Node Classification

A pairwise surrogate model is introduced that predicts whether an offspring architecture is likely to outperform its parent using genotype-derived features and early training signals, enabling full evaluation only for promising candidates within the evolutionary loop.

Maciej Krzywda, Szymon Łukasik, Amir H. Gandomi · 0 citations
Book Open access Jul 2026

SA-DCGP: Surrogate-Assisted Cartesian Genetic Programming with Dynamic Operator Scheduling for Contrastive Graph Clustering

Experimental results on benchmark graph datasets show that SA-DCGP discovers compact and high-performing architectures while significantly reducing computational overhead, demonstrating the effectiveness of surrogate-assisted evolutionary search for graph-based representation learning.

Maciej Krzywda, Szymon Łukasik, Amir H. Gandomi · 0 citations

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