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This article mainly introduces how to use matlab genetic algorithm to solve the minimum value of the relevant knowledge, detailed and easy to understand, simple and fast operation, with a certain reference value, I believe that everyone reading this article how to use matlab genetic algorithm to solve the minimum value of the article will have some gains, let's take a look at it.

%% Clear environment variables

clc

clear

%% Initialize Genetic Algorithm Parameters

% initialization parameters

maxgen=100; % evolution generation, i.e. number of iterations

sizepop=20; % population size

pcross=[0.4]; % crossover probability choice, between 0 and 1

pmutation=[0.2]; % variation probability choice, between 0 and 1

lenchrom=[1 1]; % String length of each variable, or 1 if floating

bound=[-5 5;-5 5]; % data range

individuals=struct ('fitness ',zeros(1,sizepop), ' chrom',[]); % Defines population information as a structure

avgfitness=[]; % Average fitness of population per generation

bestfitness=[]; % Optimal fitness of population per generation

bestchrom=[]; % best-fit chromosome

%% Initialize Population Calculation Fitness Value

% Initialize Population

for i=1:sizepop

% Random generation of a population

individuals.chrom(i,:)=Code(lenchrom,bound);

x=individuals.chrom(i,:);

% Calculated fitness

individuals.fitness =fun(x); % chromosome fitness

end

% Find the best chromosome

[bestfitness, bestindex]=min(individuals.fitness);

bestchrom=individuals.chrom(bestindex,:); % best chromosomes

avgfitness=sum(individuals.fitness)/sizepop; % Average fitness of chromosomes

% Record the best fitness and average fitness in each generation evolution

trace=[avgfitness bestfitness];

%% Iterative optimization

% Evolution Start

for i=1:maxgen

% Select

individuals=Select(individuals,sizepop);

avgfitness=sum(individuals.fitness)/sizepop;

% Crossover

individuals.chrom=Cross(pcross,lenchrom,individuals.chrom,sizepop,bound);

% variation

individuals.chrom=Mutation(pmutation,lenchrom,individuals.chrom,sizepop,[i maxgen],bound);

% Calculated fitness

for j=1:sizepop

x=individuals.chrom(j,:); % decode

individuals.fitness(j)=fun(x);

end

% Find chromosomes with minimum and maximum fitness and their position in the population

[newbestfitness,newbestindex]=min(individuals.fitness);

[worestfitness,worestindex]=max(individuals.fitness);

% replaces the best chromosome in the last evolution

if bestfitness>newbestfitness

bestfitness=newbestfitness;

bestchrom=individuals.chrom(newbestindex,:);

end

individuals.chrom(worestindex,:)=bestchrom;

individuals.fitness(worestindex)=bestfitness;

avgfitness=sum(individuals.fitness)/sizepop;

trace=[trace;avgfitness bestfitness]; % Record the best fitness and average fitness in each generation evolution

end

% Evolution End

%% Results Analysis

[r, c]=size(trace);

plot([1:r]',trace(:,2),'r-');

title ('fitness curve','fontsize', 12);

xlabel ('evolution algebra','fontsize', 12);ylabel ('fitness','fontsize', 12);

axis([0,100,0,1])

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