Adaptive genetic algorithm based on population evolution process

Abstract

The invention discloses a self-adaptive genetic algorithm based on the population evolution process, including the first step, setting the parameters of the BAGA algorithm, setting the number of iterations of the algorithm, the number of populations in each generation, the discrete precision of the independent variable, and the total number of shooting times , a constant; the second step is to use binary code to generate the initial population; the third step is to judge whether the maximum number of iterations is satisfied, and if so, output the optimal individual of the last generation, which is the optimal value found, otherwise turn to the fourth step; The fourth step is to establish the relationship between the objective function and the fitness function, and then calculate the fitness of each individual and the average fitness of contemporary individuals, save the individual with the largest contemporary fitness, and calculate the evolutionary degree of the contemporary population, the degree of population aggregation, and Balance factor, crossover probability and mutation probability; the fifth step, selection, crossover and mutation operations to generate new populations, the selection operator uses roulette technology, the crossover operation uses univariate crossover, and the mutation operation uses basic bit mutation; the sixth step, Find the best individual in the contemporary population, keep it, and then go to the second step.

Description

[0001] 【Technical field】 [0002] The invention relates to an adaptive genetic algorithm based on population evolution process. [0003] 【Background technique】 [0004] Genetic Algorithm (Genetic Algorithm-GA) is the product of the intersection and mutual penetration of life science and engineering science. It imitates the biological evolution of natural selection and is a random method that imitates the process of biological evolution. Its essence is a highly parallel global search algorithm for solving problems, which can automatically acquire and accumulate knowledge about the search space during the search process, and adaptively control the search process to obtain the optimal solution. [0005] More and more practice shows that genetic algorithm shows more and more superiority in solving some complex problems, but there are still some shortcomings in some aspects, such as: the premature problem of the algorithm and the convergence; Aiming at these shortcomings of SGA, a ...

AI Technical Summary

Problems solved by technology

However, although these adaptive genetic algorithms have improved the performance of the algorithm to a certain extent and improved the convergence of the algorithm, it is still easy to fall into local optimum for more complex functions, especially for multimodal functions.

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