Quantum-Inspired Genetic Algorithms for Combinatorial Optimization Problems
A. Mansori, Sarah Key Nguyeni
Abstract
Quantum-Inspired Genetic Algorithms (QIGAs) are a trailblazing force in the ever-evolving field of optimization, combining traditional genetic algorithms with quantum concepts to solve challenging combinatorial problems. By contrasting QIGAs with traditional Genetic Algorithms (GAs) in the setting of the Traveling Salesman Problem (TSP), this study explores the potential of QIGAs. The research reveals the transformational potential of quantum-inspired techniques through a thorough investigation of convergence speed, solution quality, and scalability.
Topics & Concepts
Travelling salesman problemComputer scienceScalabilityQuantumGenetic algorithmQuantum computerCombinatorial optimizationConvergence (economics)Mathematical optimizationAlgorithmField (mathematics)Theoretical computer scienceMathematicsMachine learningEconomicsDatabaseEconomic growthQuantum mechanicsPure mathematicsPhysicsQuantum Computing Algorithms and Architecture