Search problems and combinatorial optimization

One common area of applying genetic algorithms is search problems, which have important applications in fields such as logistics, operations, artificial intelligence, and machine learning. Examples include determining the optimal routes for package delivery, designing hub-based airline networks, managing investment portfolios, and assigning passengers to available drivers in a fleet of taxis.

Search algorithms focus on solving a problem through methodic evaluation of states and state transitions, aiming to find a path from the initial state to a desirable final (or “goal”) state. Typically, there is a cost or a gain involved in every state transition, and the objective of the corresponding search algorithm is to find a path that minimizes the cost or maximizes the gain. Since the optimal path is one of many possible ones, this kind of search is related to combinatorial optimization, a topic that involves finding an optimal...