What is the difference between heuristic search, global preferential search, and local preferential

It's just a matter of data and nothing else.

These algorithms all use heuristic functions, but the specific strategies for selecting the best search node are different. For example, the local merit-based search method is to select the "best node" in the search process and discard the other brother nodes, the father node, and keep searching. The result of this search is obvious, because the other nodes are discarded, the best nodes may also be discarded, because the best node to solve is only the best at that stage and not necessarily the best of the whole world.

It's much smarter to have priority, and he doesn't discard the node (unless the node is a dead node) when searching, and compares the current node with the previous node in each step of the valuation to get the "best node". This can effectively prevent the loss of the "best node". So what kind of algorithm is the A* algorithm?

It's impossible for anyone to tell you, that's NetEase's secret.

Advantages: 1. The genetic algorithm uses the coding of decision variables as the operation object, and can directly operate on the structural objects such as sets, sequences, matrices, trees, and graphs. On the one hand, this method helps to simulate the process of genes, chromosomes and genetic evolution of organisms, and facilitates the application of genetic manipulation operators.

On the other hand, genetic algorithms have a wide range of applications, such as function optimization, production scheduling, automatic control, image processing, machine learning, data mining and other fields.

2. The genetic algorithm directly uses the objective function value as the search information. It only uses the fitness function value to measure the goodness of an individual, and does not involve the process of deriving and differentiating the value of the objective function. Because in reality, many objective functions are difficult to derive, or even have no derivatives, so this also makes the genetic algorithm show a high degree of superiority.

3. The genetic algorithm has the characteristics of group search. The search process starts from an initial population p(0) with multiple individuals, which on the one hand can effectively avoid searching for some points that do not have to be searched.

On the other hand, because the traditional single-point search method is easy to fall into the extreme point of a local single-peak when searching the search space of multimodal distribution, the population search characteristic of the genetic algorithm can avoid such a problem, so it can reflect the parallelization and better global search performance of the genetic algorithm.

5. Genetic algorithm is scalable and easy to mix with other technologies. The above points are the advantages of genetic algorithm as an optimization algorithm.

Disadvantages: 1. The genetic algorithm is prone to non-standard and inaccurate problems when coding.

2. Since a single genetic algorithm coding cannot fully represent the constraints of the optimization problem, it is necessary to consider the threshold for the infeasible solution, which increases the workload and solution time.

3. The efficiency of genetic algorithm is usually lower than that of other traditional optimization methods.

4. Genetic algorithm is prone to the problem of premature convergence.