# Fast, Lightweight, and Efficient Cybersecurity Optimization for Tactical-Operational Management
## 1 Introduction
The characteristics of an individual, which are defined by the alleles of each gene, are more or less beneficial to the individual depending on the specific value of the alleles. The level of adaptation of an individual within a species to their surrounding environment is determined by their particular characteristics, which are defined by the alleles of each of their genes. Individuals with better characteristics are more likely to reproduce and give rise to new individuals, while those who are less adapted are likely to become extinct without reproducing. It is common for the offspring of well-adapted individuals to have even better characteristics, resulting in a better-adapted population through the process of reproduction and genetic exchange. Another way in which a population can evolve is through mutation. While gene mutation in the natural world can often have fatal consequences, in certain cases, it can lead to a beneficial characteristic that enables an individual to unexpectedly prosper and become better adapted. This natural evolutionary process has been transferred to the field of computing by designing algorithms, called genetic algorithms [14], that mimic the way nature works in order to solve complex optimization problems. To do so, a problem is usually defined as the context to which individuals (potential solutions to the problem) must adapt, and mechanisms similar to those existing in nature are applied [15]: mutation, crossover, adaptation, etc. Each individual is defined by a set of genes and alleles (variables and their respective values) that provide specific characteristics and determine their level of adaptation to the problem. In this context, being better adapted means being a better solution to the problem, while being less adapted means the opposite. Through a computationally accelerated process, genetic algorithms enable obtaining high-quality solutions to the proposed problem in a short amount of time in multiple applications. Genetic algorithms are metaheuristic techniques useful in solving complex optimization problems [16], such as tactical-operational cybersecurity management. These problems involve a large search space and a multitude of constraints that must be satisfied simultaneously. Genetic algorithms are useful tools to manage the processes of the organization and decision-making in different areas as presented in [17], in which the authors review the operations management problems solved by genetic algorithms and suggest future research directions from the point of view of researchers and practitioners. Furthermore, [18] focuses on the application of genetic algorithms in the eight processes of supply chain management. In the field of cybersecurity, [19] presents a decision support system using a genetic algorithm to calculate uncertain cyberattack risk and determine the optimal combination of security countermeasures based on threat rates, costs, and asset impacts, whereas [20] introduces an approach to optimize cyber security investments using various methods for risk-averse organizations, aiming to reduce the cost of cyber insurance while improving self-protection. Finally, it is worth mentioning [21], which introduces a semi-automated approach based on Pareto optimality for selecting appropriate cybersecurity controls to minimize risks and address conflicting goals among stakeholders. To the best of our knowledge, there have been no prior studies employing genetic algorithms to support decision-making in the tactical and operational management of cybersecurity, specifically in the selection of cybersecurity actions applicable to business assets, from a holistic and cross-functional perspective. By applying genetic algorithms to the exposed cybersecurity management optimization problem, organizations can improve their ability to choose faster, more accurately, and more easily the required cybersecurity actions to detect and respond to cyber threats, reduce vulnerability, and minimize risk.