Research status of quantum genetic algorithm at home and abroad

1. The computational model of biological evolution process that simulates the natural selection and genetic mechanism of Darwin's biological evolution theory is a method to search for the optimal solution by simulating the natural evolutionary process. The genetic algorithm starts with a population that represents the set of possible potential solutions to the problem;

2. Domestic academic journals such as CNKI and Wanfang have provided a large number of literatures. If you are in school, you can ** these documents for free, and then summarize the development status of genetic algorithms, if you are not in school, then you can go to Douding.com, Encyclopedia, and Daoke Baba.

Summary. The most significant difference between quantum genetic algorithm and traditional genetic algorithm is the difference in search space. In the traditional genetic algorithm, the search space is a discrete and finite solution space, while the quantum genetic algorithm is optimized based on quantum theory, which can find the global optimal solution more quickly and effectively when the search space is larger.

The most significant difference between quantum genetic algorithm and transmission genetic algorithm lies in the difference in search space. In the traditional genetic algorithm, the search space is a discrete and finite solution space, while the quantum genetic algorithm is optimized based on quantum theory, which can find the global optimal solution more quickly and effectively when the search space is larger. In addition, quantum genetic algorithm takes advantage of the characteristics of quantum superposition and quantum entanglement, which can explore multiple possible solutions at the same time, thereby improving the search ability and convergence speed of the algorithm.

Can you add, I don't quite understand it.

The biggest difference between quantum genetic algorithm and traditional genetic algorithm is the search space, the former uses quantum theory for optimization, and can find the global optimal solution more quickly and effectively in a larger search space. The quantum genetic algorithm also takes advantage of the characteristics of quantum stacking and quantum entanglement, which can explore multiple possible solutions at the same time, so as to improve the search ability and convergence speed.

The contribution of the former is to introduce the concept of quantum multiverses into the genetic algorithm, use the parallel search of multiple universes to increase the search range, and use the joint intersection between the universes to realize the exchange of information, so as to improve the search efficiency of the algorithm as a whole. However, the multi-universe in the algorithm is obtained by generating multiple populations separately, and does not use quantum states, so it still belongs to the conventional genetic algorithm. The latter introduces quantum state vector expression into genetic coding, and uses the quantum revolving door to realize the evolution of chromosomes, which achieves better results than conventional genetic algorithms.

However, this algorithm is mainly used to solve the 0-1 backpack problem. The encoding scheme and the evolution strategy of the quantum revolving door are not universal, especially since all individuals evolve towards a single goal, and if there is no cross-operation, it is very likely to fall into a local optimum.

QGA was improved in Ref. [1] and a quantum genetic algorithm (QGA) was proposed. QGA uses multi-state gene qubit encoding and general-purpose quantum revolving door operation. The introduction of the dynamic adjustment of rotation angle mechanism and quantum crossing is more versatile and efficient than the method in Ref. [2].

However, this method is still a group that evolves independently, and the efficiency of multi-universe and inter-universe entanglement without using Yingon information needs to be further improved. A multiuniverse parallel quantum genetic algorithm (MPQGA) was proposed in Ref. [3], in which all individuals are divided into independent subgroups according to a certain topological structure, which is called the universe. Multi-state gene qubit coding is used to express individuals in the universe; The general quantum revolving door strategy and the dynamic adjustment of the rotation angle mechanism were used to evolve the individual. Each universe evolves independently, which expands the search space, and the optimal migration, quantum crossing, and quantum mutation operations are used to exchange information between universes, making the algorithm more adaptable and efficient.

Quantum genetic algorithm is the product of the combination of quantum computing and genetic algorithm. Liang Xian's research in this field mainly focuses on two types of models: one is the multi-universe quantum inspired genetic algorithm based on the characteristics of the quantum multiverse, and the other is the genetic quantum algorithm (GQA) based on the characteristics of qubits and quantum states.