Function optimization algorithm based on genetic
algorithm and nonlinear programming
Theoretical basis
July 7,2019
1 Definition
1.1 Theoretical basis
Nonlinear programming function: Nonlinear programming is to study the ex-
tremum problem of an n-ary real function under the constraints of a set of
equality or inequality. The nonlinear programming function is based on an esti-
mated value and searches for the maximum value of the nonlinear multivariate
function under constraint conditions.
1.2 The basic idea of genetic algorithm
The search starts from a randomly generated initial solution, and iteratively
iterates through select, cross, and mutate operations to generate a new solution.
Each of the individuals in the group represents a solution to the problem, called
chrom. The quality of the chrom is measured by fitness. According to the
degree of fitness, a certain number of excellent individuals are selected from the
previous generation, and the next generation group is obtained through cross
mutation. After several generations of evolution, the algorithm converges to the
best chrom, which is the optimal solution or suboptimal solution to the problem.
1.3 Algorithm combination idea
Most of the classical nonlinear programming algorithms are solved by the gra-
dient descent method. The local search ability is strong but the overall search
ability is poor. The genetic algorithm uses the select, cross and mutate opera-
tions to search. The overall search ability is strong, but the local search ability
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