Genetic optimization algorithms applied toward mission computability models

TL;DR

This paper presents genetic optimization algorithms tailored for mission-critical, constraints-aware computational problems, demonstrating their effectiveness in solving complex, real-world tasks efficiently.

Contribution

The paper introduces specialized genetic algorithms designed specifically for mission-critical and constraints-aware computation problems, expanding their application scope.

Findings

Effective in solving mission-critical problems

Low computational cost of the proposed algorithms

Demonstrated suitability for real-world constraints

Abstract

Genetic algorithms are modeled after the biological evolutionary processes that use natural selection to select the best species to survive. They are heuristics based and low cost to compute. Genetic algorithms use selection, crossover, and mutation to obtain a feasible solution to computational problems. In this paper, we describe our genetic optimization algorithms to a mission-critical and constraints-aware computation problem.