Author: Zen and the Art of Computer Programming
1 Introduction
The development of artificial intelligence has driven the rapid development of information technology and economic fields. But at the same time, the reliance on artificial intelligence systems is increasing, and the number, scale, and complexity of artificial intelligence models are also growing rapidly. How to automatically discover, select, train and deploy artificial intelligence models is an important topic. This article will elaborate on automated model search through a series of technical articles, including research on machine learning, deep learning, optimization, statistics and other aspects. This article will start from the key technologies and concepts of artificial intelligence models, introduce the principles and implementation solutions of automated model search methods, and give recommendations for related tools and platforms. Hope it can be helpful to readers!
2. Explanation of basic concepts and terms
2.1 Model
In artificial intelligence, a model refers to a series of computing logic and its parameters used to make predictions or decisions about the real world. Models can be abstract or concrete. For example, decision tree models, support vector machine (SVM) models, and neural network models are all artificial intelligence models.
2.2 Dataset
Dataset is a collection of data used to train the model. Generally speaking, a data set consists of input (Input) and output (Output). Each sample represents a piece of data. The input includes features (Feature), and the output corresponds to the predicted target value (Label). Commonly used classification data sets include IRIS, MNIST, CIFAR-10, etc.
2.3 Cost function
The cost function measures the prediction accuracy of the model on the data set. A lower cost function value means that the model fits the data set more accurately and vice versa. Commonly used cost functions include squared error, cross-entropy loss, F1 score, etc.
2.4 Parameters
Model parameters (Parameters) refer to the parameters used to control the behavior of the model. Commonly used model parameters include weight, bias, hyperparameter, etc.
2.5 Hyperparameters
Hyperparameters refer to parameters that affect model training and generalization performance. Commonly used hyperparameters include learning rate, regularization coefficient, hidden layer size, etc.
2.6 Automated model search
Automated model search (AutoML) refers to the process of automatically discovering, selecting, training and deploying artificial intelligence models. It can greatly improve efficiency and effectiveness and reduce labor costs. Automated model search methods are mainly based on two perspectives:
- In the learning process, different models should have different learning strategies;
- When deployed, different models should have different prediction strategies.
Currently, there are many methods for automated model search, such as genetic algorithms, evolutionary algorithms, Bayesian optimization, prior knowledge, etc. Among them, genetic algorithms and evolutionary algorithms overlap in their application scope. This article will focus on the genetic algorithm because it has the highest practicability.
3. Explanation of core algorithm principles, specific operating steps and mathematical formulas
3.1 Concept
Genetic Algorithm (GA) is a genetic algorithm based on the population fitness function (Fitness Function).
3.2 Concept
Population refers to the collection of individuals generated by the algorithm from the initial population to the final one.
3.3 Concept
Individual refers to one or more variable combinations that solve a certain problem, that is, Chromosome.
3.4 Concept
Chromosome means that each individual is composed of several genes (Gene), and genes can be 0 or 1.
3.5 Concept
Initial Population refers to a collection of individuals randomly initialized by the algorithm from a certain distribution.
3.6 Concept
Mutation refers to the process of mutation of individuals before reproduction.
3.7 Concept
Crossover refers to the process of crossing individuals after reproduction with the purpose of producing new offspring.
3.8 Concept
Fitness Function refers to a function that evaluates the quality of the current solution based on its quality.
3.9 Specific operation steps
Initialize the population: Randomly generate the initial population and bind the chromosomes to the fitness values. The initial length of the population is generally set to 100 to 500.
Splicing and selection: In each generation round, two individuals are randomly selected from the parent population for splicing operation (splicing into two single-parent small populations), and then the individual with the smallest fitness value is selected between the two populations to enter the next generation. The splicing operation is similar to hybridization, exchanging genetic information and increasing the diversity of the population.
Mutation: In each generation, individuals are randomly selected from the population and one or more genes in the individuals are randomly changed to increase the diversity of the individuals.
Termination condition: Stop running when the algorithm meets convergence or the number of iterations reaches a certain value.
3.10 Mathematical formulas
Chromosome definition: $x \in {0,1}^n$
Fitness value definition: $\phi(x) = f(\theta^T x)$
Individual probability definition: $P_c(x) = P_{cr} (x) P_{fit}(x)$, $P_{cr} (x_i, x_j)$ is the crossover probability, $P_{fit}$ is the fitness value probability density.
Crossover probability definition: $P_{cr}(x_i, x_j) = \frac{1}{N} \sum_{k=1}^{N}\left{[k!= i & k!= j]~p_ {cross}(X_i^{a}, X_j^{a}, X_k^{b})\right}$, $X^{a}$ is the own chromosome, $X^{b}$ is another individual Chromosomes, $N$ is the total number of populations.
Definition of fitness value probability density: $p_{fit}(x) = \frac{\exp(-\frac{(\theta^Tx - y)^2}{2\sigma_y^2})}{\sqrt{ 2\pi\sigma_y^2}}$.
The $m$th generation population definition: $X_m = [x_1^{m},...,x_{M_m}^{m}]$, $M_m$ is the number of populations in each generation.
4. Specific code examples and explanations
4.1 Code examples
import numpy as np
class GeneticAlgorithm:
def __init__(self, MU, LAMBDA, NGEN, sigma):
self.MU = MU # 种群大小
self.LAMBDA = LAMBDA # 小种群大小
self.NGEN = NGEN # 迭代次数
self.sigma = sigma
def init_population(self, dim):
pop = []
for _ in range(self.MU):
chromosome = np.random.randint(2, size=(dim)) # 生成染色体
fitness = function(chromosome) # 计算适应度值
pop.append((chromosome, fitness)) # 将染色体与适应度值绑定起来
return pop
def select_parents(self, population):
parents = []
while len(parents) < 2:
idx = np.random.choice(range(len(population)), replace=False, p=[x[1] for x in population])
if not any([np.array_equal(idx, x[0]) for x in parents]):
parents.append(population[idx])
return parents
def crossover(self, parent1, parent2):
if np.random.rand() < 0.8:
point = np.random.randint(low=0, high=len(parent1)-1)
child1 = np.concatenate((parent1[:point], parent2[point:]))
child2 = np.concatenate((parent2[:point], parent1[point:]))
return child1, child2
else:
return parent1, parent2
def mutation(self, chrom):
mask = np.random.binomial(size=chrom.shape, n=1, p=0.2)[0] # 设置突变概率为0.2
chrom[mask==1] = abs(chrom[mask==1]-1) # 对变异基因进行变异
return chrom
def run(self, data):
dim = len(data[0][0]) # 获取维度
population = self.init_population(dim) # 初始化种群
for generation in range(self.NGEN):
offspring = []
# 生殖子代
while len(offspring)<self.LAMBDA:
parent1, parent2 = self.select_parents(population)
child1, child2 = self.crossover(parent1[0], parent2[0])
offspring += [(child1, None), (child2, None)]
# 变异
for i in range(int(self.LAMBDA/2)):
rand_index = np.random.randint(len(offspring))
mutated_chrom = self.mutation(offspring[rand_index][0])
offspring[rand_index] = (mutated_chrom, None)
# 更新种群
new_population = []
new_population += sorted(population+offspring, key=lambda x:-x[1])[0:self.MU] # 插入父代和子代
population = new_population
print("Generation:", generation+1, "Best Fitness", max([x[1] for x in population])) # 打印当前结果
best_individual = sorted(population, key=lambda x:-x[1])[0] # 获取最优个体
return best_individual
def function(chromosome):
'''
根据染色体求适应度值
'''
pass
if __name__ == '__main__':
ga = GeneticAlgorithm(MU=50, LAMBDA=20, NGEN=50, sigma=1) # 初始化遗传算法
data = load_data() # 加载数据
result = ga.run(data) # 执行遗传算法
print("Best Chromosome:", "".join(map(str,result[0]))) # 打印最优染色体
print("Best Fitness Value:", result[1]) # 打印最优适应度值4.2 Explanation
First import the corresponding library, here we use the numpy library. Then a class is defined GeneticAlgorithm, which initializes various parameters in the genetic algorithm, including population size, small population size, number of iterations, and gene mutation rate.
The member functions of the class are as follows:
__init__: Constructor to initialize the genetic algorithm instance.init_population: Initialize the population and return the population list.select_parents: Select two individuals from the population as parents and return the two parent individuals.crossover: Reproduction process, generating two individuals and returning two offspring individuals.mutation: Gene mutation process, returning the mutated chromosome.run: Execute the genetic algorithm and return the optimal individual.
Finally, call runthe function, input the data, and obtain the optimal chromosome and its fitness value.
At this point, we have completed the code implementation of the genetic algorithm.
5. Future development trends and challenges
The development process of genetic algorithms can be divided into early stage, intermediate stage and late stage. The early stage focuses on rough partitioning to achieve maximum likelihood estimation, while the middle stage focuses on multiple decodings, including local search and simulated annealing, to approximate the global optimal solution; the late stage focuses on global search, including parameter tuning, model compression, and distribution. algorithms, etc., to solve practical problems.
There is currently no specific directional research on the future development of genetic algorithms. There are already some relatively mature genetic algorithms, such as network structure search based on simulated annealing, hyperparameter optimization based on genetic programming, automatic model selection based on evolutionary strategies, etc. These algorithms have relatively good results in certain fields. For those problems that are far from being fully explored, we need to continue research and find more and better algorithms and application scenarios.