AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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The DG distribution belongs to the exponential family, and it is established that DG-based step-size adaptation arises directly from the statistical structure of the mutation distribution, rather than as a discrete analog of continuous ES mechanisms.
It is argued that real-world deployment demands robustness and introduced MuViS-C, the first multi-domain benchmark of robustness against common sensor failures in learning-based virtual sensing, which covers ten sensor failure modes, from subtle drifts to catastrophic signal dropouts, at multiple severities.
The bidirectional interaction between large Language Models and Evolutionary Computation is reviewed and how their complementary strengths can be leveraged in hybrid intelligent systems is examined.
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.
This work presents LACE, an AutoML framework that instead searches over complete executable pipeline programs: an evolutionary loop maintains a population of scikit-learn-compatible Python classes, and a large language model acts as the variation operator.
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