What is the difference between hybrid genetic algorithm and genetic algorithm?

What is the difference between hybrid genetic algorithm and genetic algorithm?

Genetic algorithm is a global search algorithm that does not require derivative information of the objective function. It can quickly Search the range where the optimal value is found.

The hybrid genetic algorithm introduces other optimization algorithms (such as algorithms with strong regional optimization capabilities) on the basis of the genetic algorithm to ensure the global performance of the genetic algorithm. Reduce the amount of calculation and improve the convergence speed. Generally introduced algorithms include: traditional gradient algorithms, simplex method, simulated annealing, etc.) These algorithms are easily compatible with genetic algorithms. What is the difference between quantum genetic algorithm and genetic algorithm?

Genetic algorithm (Geic Algorithm) is a computational model that simulates the biological evolution process of natural selection and genetic mechanism of Darwin's biological evolution theory. A method of searching for optimal solutions through natural evolutionary processes. Genetic algorithms start from a population that represents a set of possible potential solutions to the problem, and a population is composed of a certain number of individuals encoded by genes. Each individual is actually a chromosome (chromosome) with characteristic entities. As the main carrier of genetic material, chromosomes are a collection of multiple genes. Its internal expression (i.e. genotype) is a certain combination of genes, which determines the external expression of an individual's shape. For example, the characteristics of black hair are controlled by the chromosomes. A characteristic is determined by a certain combination of genes. Therefore, it is necessary to realize the mapping from phenotype to genotype at the beginning, that is, coding. Since the work of imitating genetic coding is very complicated, we often make simplifications, such as binary coding. After the first generation population is generated, according to the principles of survival of the fittest and survival of the fittest, generation by generation evolution produces better and better approximate solutions. In each generation, individuals are selected according to their fitness in the problem domain, and combined crossover and mutation are performed with the help of genetic operators of natural genetics to produce represents the population of new solution sets. This process will cause the population to evolve naturally, and the subsequent generation population will be more adaptable to the environment than the previous generation. The optimal individual in the last generation population can be decoded and used as an approximate optimal solution to the problem.

Quantum genetic algorithm is the product of the combination of quantum computing and genetic algorithm. At present, research in this field mainly focuses on two types of models: one is the multiverse quantum-derived genetic algorithm (Quantum Inspired Geic Algorithm) based on the characteristics of the quantum multiverse, and the other is based on qubits and quantum state registration. Genetic Quantum Algorithm (GQA) that adds characteristics.

Quantum GeicAlgorithm (QGA). QGA uses multi-state genetic qubit encoding and universal quantum revolving door operations. The introduction of dynamic adjustment of rotation angle mechanism and quantum crossover is more versatile and more efficient than the method in literature [2]. However, this method is still a group evolving alone and does not take advantage of the entanglement characteristics of multiple universes and universes. The efficiency needs to be further improved. Literature [3] proposes a Multiverse Parallel Quantum Geic Algorithm (MPQGA). In the algorithm, all individuals are divided into independent subgroups, called universes, according to a certain topological structure. State gene qubit encoding is used to express individuals in the universe; a universal quantum revolving door strategy and a dynamically adjusted rotation angle mechanism are used to evolve individuals; each universe evolves independently, which can expand the search space and adopt optimal immigration between universes , quantum crossover and quantum mutation operations to exchange information to make the algorithm more adaptable and efficient. What is a genetic algorithm and what is the use of a genetic algorithm?

The genetic algorithm is a search algorithm used to solve optimization problems in computational mathematics and is a type of evolutionary algorithm. The difference between genetic algorithm and ant colony algorithm

Genetic algorithm (Geic Algorithm, GA) was invented by Holland J. H. An optimization method proposed in the 1970s, its search process for the optimal solution simulates Darwin's theory of evolution and the idea of ??"survival of the fittest".

Ant Colony Optimization (ACO) is a probabilistic algorithm used to find optimized paths in graphs.

Both algorithms are conceptually stochastic optimization algorithms. The genetic algorithm is an evolutionary algorithm, mainly through selection, mutation and crossover operators, in which each gene is composed of a binary string. ; Ant colony algorithm is an algorithm based on graph theory, which exchanges information through information element selection.

Genetic algorithm

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Hybrid ant colony genetic algorithm, MATLAB programming

Tell us more, What does parallel machine mean matlab genetic algorithm

function m_main()

clear

clc

Max_gen=100;% execution algebra

pop_size=100;% population size

chromsome=10;% chromosome length

pc=0.9;% crossover probability

pm=0.25;% mutation probability

gen=0;% statistical algebra

% initialization

init=40*rand(pop_size,chromsome)-20;

pop=init;

fit=obj_fitness(pop);

[max_fit,index_max]=max(fit);maxfit=max_fit;

[min_fit,index_min]=min(fit);best_indiv=pop(index_max,:);

%iterative operation

while gen< p> gen=gen+1; bt(gen)=max_fit;

if maxfit

best_indiv_tmp(gen)=pop(index_max);

newpop=ga(pop,pc,pm,chromsome,fit);

fit=obj_fitness(newpop);

[max_fit,index_max]=max(fit);

[min_fit,index_min]=min(fit);

pop=newpop;

trace(1,gen)=max_fit;

trace(2,gen)=sum(fit)./length(fit);

end

%Execution results

[f_max gen_ct]=max(bt)%The maximum value and algebra found

maxfit

% draw

% bt

hold on

plot(trace(1,:),'.g: ');

plot( trace(2,:),'.r-');

title('Experimental result graph')

xlabel( 'Number of iterations/generation'),ylabel('Best fitness (maximum value)');% coordinate label

plot(gen_ct-1,0:0.1:f_max+1,'c- ');% draw the maximum value

text(gen_ct,f_max+1, 'maximum value')

hold off

function [fitness]=obj_fitness (pop)

% fitness calculation function

[r c]=size(pop);

x=pop;

fitness=zeros(r,1);

for i=1:r

for j=1:c

fitness(i,1)=fitness (i,1)+sin(sqrt(abs(40*x(i))))+1-abs(x(i))/20.0;

end

end

function newpop=ga(pop,pc,pm,chromsome,fit);

pop_size=size(pop,1);

%roulette Select

ps=fit/sum(fit);

pscum=cumsum(ps);%size(pscum)

r=rand(1,pop_size );qw=pscum*ones(1,pop_size);

selected=sum(pscum*ones(1,pop_size)< p> newpop=pop(selected,:);

%cross

if pop_size/2~=0

pop_size=pop_size-1;

end

for i=1:2:pop_size-1

while pc>rand

c_pt=round(8*rand+1) ;

pop_tp1=newpop(i,:);pop_tp2=newpop(i+1,:);

newpop(i+1,1:c_pt)=pop_tp1(1, 1:c_pt);

newpop(i,c_pt+1:chromsome)=pop_tp2(1,c_pt+1:chromsome);

end

end

% variation

for i=1:pop_size

if pm>rand

m_pt=1+round(9*rand) ;

newpop(i,m_pt)=40*rand-20;

end

end What is genetic algorithm and what is the use of genetic algorithm? I hope it’s a little more popular

First of all, there is a very magical phenomenon: the evolution of humans and animals is developing in a good direction. Although some are developing in a bad direction, the overall trend is definitely for the better. direction development. This may not seem surprising, but we know that human genetic combinations are random and have no God-bound constraints. The results of this random process are consistent! Our genetic algorithm is inspired from here! For example, if I ask for the maximum value of y=x1+x2, two variables, I don't use the traditional mathematical method, but use the kindergarten method, bring in all possible values ??and calculate, and then find the largest one! However, sometimes the values ??are consecutive, and that's okay! Discretize it, just like converting analog signals into digital signals! There is another question, what should I do if there are too many values? This is the essence of genetic algorithm!

First of all, I don’t need to take all possible values. I only take dozens or hundreds (determine by myself) and then process them. How to deal with them? Let’s go back to the initial problem of human evolution. Although there is no help from God, we know that nature follows the survival of the fittest and the law of cross-mutation. Although it cannot be digitized, this is a trend! We just mathematicalize this rule! Which of the dozens of values ??taken do I want to be left with? Which ones to discard? Which ones to deal with? We all have to choose ourselves. We must choose the most appropriate value to keep. After a series of processing, a new group will be generated, and then we will process it. We can agree on how many times we will process it, and take out the existing ones. Maximum value

Don’t worry about whether you get the maximum value, because it has been proved mathematically that this method converges, and the probability is 1, so feel free to do it, and refer to the relevant methods for specific methods. Books are not difficult.

The greatest use of genetic algorithms is to solve problems that cannot be solved by mathematical theory! For example, path planning, scheduling problems... What is the difference between ant colony algorithm and genetic algorithm?

They all belong to intelligent optimization algorithms

However, the ant colony algorithm has a certain memory, while the genetic algorithm does not

There are several principles of the ant colony algorithm , such as the foraging principle, the obstacle avoidance principle, etc., the genetic algorithm does not

The ant colony algorithm is a group intelligence optimization algorithm, with parallelism, each particle can actively seek optimization, the genetic algorithm does not

The ant colony algorithm is based on the instructions of pheromones in the environment, and the genetic algorithm is based on the idea of ??biological evolution of survival of the fittest

The genetic algorithm has three operating elements: selection, crossover, and mutation. , each operand has its own different methods. By modifying and matching the operand methods, different improved genetic algorithms can be obtained

The ant colony algorithm is often combined with other intelligent algorithms to obtain The difference between the improved ant colony algorithm ncga and nsga-ii genetic algorithm

1 Initialize the chromosome, this step is no different from the particle swarm initialization

2 Use two or more people In the tournament format, new chromosomal offspring are generated in the matching pool, and the size of the new generation population is half of the original population size.

3. Perform crossover and mutation operations on the new generation population, and decide whether to perform crossover or mutation operations based on probability. Generally speaking, we perform crossover with a larger probability and mutation with a smaller probability. Specifically, The crossover mutation operation is available in the literature, and it is different from the binary genetic algorithm, which I will talk about in a moment. The crossover operation on a randomly selected pair of parental chromosomes will produce a new pair of chromosomes, while the mutation operation only targets a single-parent mutation, so only one new chromosome is produced.

4 Merge the original population and the descendant population, calculate the fitness value, and output the optimal solution space

5 The algorithm ends.