An adaptive quantum evolution algorithm for 0–1 knapsack problem
Valerii M. Tkachuk · System research and information technologies · 2018
Quantum Genetic Algorithm (QGA) has a number of advantages in comparison with its classical version: operating speed, small population size and auto-balance between the global search and the local search. It is based on the ideas of traditional evolutionary algorithms, applied to the quantum computations technology, which operate with quantum bits, superposition of states and quantum measurements. This paper proposes a QGA with a new adaptive quantum gate operator and a restoring technology for the quantum chromosome during the process of solving combinatorial problems with constraints. Meta-optimization of the primary algorithm parameters is used for providing the algorithm efficiency. The productiveness of the suggested approach is proven by the model studies, carried out using a wide range of test 0–1 knapsack problems.