Hybrid gene operation method

Abstract

The invention provides a hybrid gene operation method. The hybrid gene operation method comprises the following steps: receiving a pursuit target to randomly generate a plurality of solution paths; converting each solution path into a first adaptive target; comparing the first adaptive target with the pursuing target, and when a non-feasible solution is generated, performing a gene operation on the first adaptive target to generate a plurality of second adaptive targets; performing a single-point search operation and a comparison operation on the second adaptive target to generate a pluralityof first evaluation paths and second evaluation paths; converting each first evaluation path and each second evaluation path into a mixed adaptation target; and when the hybrid adaptation target conforms to the pursuing target, regarding the hybrid adaptation target as an optimization solution, thereby achieving the purposes of rapid convergence and reduction of calculation time.

Description

technical field [0001] The invention relates to a calculation method, in particular to a hybrid gene calculation method. Background technique [0002] Genetic Algorithm (GA) is a search algorithm used to solve optimization in computational mathematics. Genetic algorithm retains better offspring through the process of evolution and iteration of the initial parent. [0003] Genetic algorithm process: randomly generate n chromosomes at the beginning; use the fitness function to calculate the fitness value of all chromosomes; repeat the above steps 2 to 4 times, the actions of performing the aforementioned steps 2 to 4 times are called 1 iteration until convergence, wherein the condition for convergence is that the number of iterations reaches a certain number of times or all chromosomes are very similar. [0004] Although the genetic algorithm has the ability of global search, it can search out all solutions in the solution space without falling into the problem of rapid decli...

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Problems solved by technology

[0004] Although the genetic algorithm has a global search ability, it can search all the solutions in the solution space without falling into the problem of rapid decline of local solutions; however, the local search ability of the genetic algorithm is poor, which makes the simple genetic algorithm more time-consuming , the search efficiency is low in the later stage of evolution, and in practical applications, the genetic algorithm is prone to premature convergence problems, not necessarily finding the optimal solution and converging, resulting in miscalculation problems

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