# Applying Incremental Learning in Binary-Addition-Tree Algorithm for Dynamic Binary-State Network Reliability

arXiv: https://arxiv.org/abs/2409.15721 · HTML: https://mlsanity.com/paper/2409.15721/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 42 references that are in the map.
- First posted on arXiv 24 Sep 2024.

## Builds on (most cited first, 6 of 6)

- [Novel Binary-Addition Tree Algorithm (BAT) for Binary-State Network Reliability Problem](https://mlsanity.com/paper/2004.08238/) (2020)
- [Novel Algorithm for Computing All-Pairs Homogeneity-Arc Binary-State Undirected Network Reliability](https://mlsanity.com/paper/2105.01500/) (2021)
- [Novel Binary Addition Tree Algorithm (BAT) for Calculating the Direct Lower-Bound of the Highly Reliable Binary-State Network Reliability](https://mlsanity.com/paper/2110.13390/) (2021)
- [New Binary-Addition Tree Algorithm for the All-Multiterminal Binary-State Network Reliability Problem](https://mlsanity.com/paper/2111.10818/) (2021)
- [A New BAT and PageRank algorithm for Propagation Probability in Social Networks](https://mlsanity.com/paper/2202.13033/) (2022)
- [Novel Recursive Inclusion-Exclusion Technology Based on BAT and MPs for Heterogeneous-Arc Binary-State Network Reliability Problems](https://mlsanity.com/paper/2207.00169/) (2022)

## Abstract (arXiv)

This paper presents a novel approach to enhance the Binary-Addition-Tree algorithm (BAT) by integrating incremental learning techniques. BAT, known for its simplicity in development, implementation, and application, is a powerful implicit enumeration method for solving network reliability and optimization problems. However, it traditionally struggles with dynamic and large-scale networks due to its static nature. By introducing incremental learning, we enable the BAT to adapt and improve its performance iteratively as it encounters new data or network changes. This integration allows for more efficient computation, reduced redundancy without searching minimal paths and cuts, and improves overall performance in dynamic environments. Experimental results demonstrate the effectiveness of the proposed method, showing significant improvements in both computational efficiency and solution quality compared to the traditional BAT and indirect algorithms, such as MP-based algorithms and MC-based algorithms.