Higher-Order Quantum-Inspired Genetic Algorithms
This work addresses optimization challenges in combinatorial problems, but it appears incremental as it builds on existing quantum-inspired genetic algorithms.
The paper tackles the problem of improving genetic algorithm efficiency for deceptive combinatorial optimization by introducing higher-order quantum-inspired genetic algorithms, specifically QIGA2, which outperforms the old QIGA algorithm on a benchmark of 20 problems.
This paper presents a theory and an empirical evaluation of Higher-Order Quantum-Inspired Genetic Algorithms. Fundamental notions of the theory have been introduced, and a novel Order-2 Quantum-Inspired Genetic Algorithm (QIGA2) has been presented. Contrary to all QIGA algorithms which represent quantum genes as independent qubits, in higher-order QIGAs quantum registers are used to represent genes strings which allows modelling of genes relations using quantum phenomena. Performance comparison has been conducted on a benchmark of 20 deceptive combinatorial optimization problems. It has been presented that using higher quantum orders is beneficial for genetic algorithm efficiency, and the new QIGA2 algorithm outperforms the old QIGA algorithm which was tuned in highly compute intensive metaoptimization process.