Genetic Algorithms are search algorithms that mimic Darwinian biological evolution in order to select and propagate better solutions.

A **Concatenated Skip Connection** is a type of skip connection that seeks to reuse features by concatenating them to new layers, allowing more information to be retained from previous layers of the network. This contrasts with say, residual connections, where element-wise summation is used instead to incorporate information from previous layers. This type of skip connection is prominently used in DenseNets (and also Inception networks), which the Figure to the right illustrates.

Concatenated Skip Connection

Genetic Algorithms and the Traveling Salesman Problem a historical Review

**TorchBeast** is a platform for reinforcement learning (RL) research in PyTorch. It implements a version of the popular [IMPALA](https://paperswithcode.com/method/impala) algorithm for fast, asynchronous, parallel training of RL agents.

Source	Genetic Algorithms and the Traveling Salesman Problem a historical Review
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com