Genetic programming for pedestrians

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遗传算法遗传算法

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（5）遗传算法在解空间进行高效启发式搜索，而非盲目地穷举或完全随机搜索；
（6）遗传算法对于待寻优的函数基本无限制，它既不要求函数连续，也不要求函数可微，既可以是数学解析式所表示的显函数，又可以是映射矩阵甚至是神经网络的隐函数，因而应用范围较广；
（7）遗传算法具有并行计算的特点，因而可通过大规模并行计算来提高计算速度，适合大规模复杂问题的优化。
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（4）基本遗传算法的运行参数有下述4个运行参数需要提前设定：
M：群体大小，即群体中所含个体的数量，一般取为 20~100； G：遗传算法的终止进化代数，一般取为100~500； Pc：交叉概率，一般取为0.4~0.99；
Pm：变异概率，一般取为0.0001~0.1。
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10.4.2 遗传算法的应用步骤
遗传算法简称GA（Genetic Algorithms）是1962年由美国Michigan大学的Holland教授提出的模拟自然界遗传机制和生物进化论而成的一种并行随机搜索最优化方法。
遗传算法是以达尔文的自然选择学说为基础发展起来的。自然选择学说包括以下三个方面：
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（1）遗传：这是生物的普遍特征，亲代把生物信息交给子代，子代总是和亲代具有相同或相似的性状。生物有了这个特征，物种才能稳定存在。
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（3）生产调度问题在很多情况下，采用建立数学模型的方法难以对生
产调度问题进行精确求解。在现实生产中多采用一些经验进行调度。遗传算法是解决复杂调度问题的有效工具，在单件生产车间调度、流水线生产车间调度、生产规划、任务分配等方面遗传算法都得到了有效的应用。
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（4）自动控制。在自动控制领域中有很多与优化相关的问题需要求
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人工智能遗传算法

人工智能遗传算法英文回答：Genetic Algorithms for Artificial Intelligence.Genetic algorithms (GAs) are a class of evolutionary algorithms that are inspired by the process of natural selection. They are used to solve optimization problems by iteratively improving a population of candidate solutions.How GAs Work.GAs work by simulating the process of natural selection. In each iteration, the fittest individuals in thepopulation are selected to reproduce. Their offspring are then combined and mutated to create a new population. This process is repeated until a satisfactory solution is found.Components of a GA.A GA consists of the following components:Population: A set of candidate solutions.Fitness function: A function that evaluates thequality of each candidate solution.Selection: The process of choosing the fittest individuals to reproduce.Reproduction: The process of creating new individuals from the selected parents.Mutation: The process of introducing random changes into the new individuals.Applications of GAs.GAs have been used to solve a wide variety of problems, including:Optimization problems.Machine learning.Scheduling.Design.Robotics.Advantages of GAs.GAs offer several advantages over traditional optimization methods, including:They can find near-optimal solutions to complex problems.They are not easily trapped in local optima.They can be used to solve problems with multiple objectives.Disadvantages of GAs.GAs also have some disadvantages, including:They can be computationally expensive.They can be sensitive to the choice of parameters.They can be difficult to terminate.中文回答：人工智能中的遗传算法。

人工智能原理MOOC习题集及答案北京大学王文敏

Quizzes for Chapter 11单选(1分)图灵测试旨在给予哪一种令人满意的操作定义得分/总分A.人类思考B.人工智能C.机器智能D.机器动作正确答案：C你选对了2多选(1分)选择以下关于人工智能概念的正确表述得分/总分A.人工智能旨在创造智能机器该题无法得分/B.人工智能是研究和构建在给定环境下表现良好的智能体程序该题无法得分/C.人工智能将其定义为人类智能体的研究该题无法得分/D.人工智能是为了开发一类计算机使之能够完成通常由人类所能做的事该题无法得分/正确答案：A、B、D你错选为A、B、C、D3多选(1分)如下学科哪些是人工智能的基础？得分/总分A.经济学B.哲学C.心理学D.数学正确答案：A、B、C、D你选对了4多选(1分)下列陈述中哪些是描述强AI(通用AI)的正确答案？得分/总分A.指的是一种机器，具有将智能应用于任何问题的能力B.是经过适当编程的具有正确输入和输出的计算机，因此有与人类同样判断力的头脑C.指的是一种机器，仅针对一个具体问题D.其定义为无知觉的计算机智能，或专注于一个狭窄任务的AI正确答案：A、B你选对了5多选(1分)选择下列计算机系统中属于人工智能的实例得分/总分搜索引擎B.超市条形码扫描器C.声控电话菜单该题无法得分/D.智能个人助理该题无法得分/正确答案：A、D你错选为C、D6多选(1分)选择下列哪些是人工智能的研究领域得分/总分A.人脸识别B.专家系统C.图像理解D.分布式计算正确答案：A、B、C你错选为A、B7多选(1分)考察人工智能(AI)的一些应用，去发现目前下列哪些任务可以通过AI来解决得分/总分A.以竞技水平玩德州扑克游戏B.打一场像样的乒乓球比赛C.在Web上购买一周的食品杂货D.在市场上购买一周的食品杂货正确答案：A、B、C你错选为A、C8填空(1分)理性指的是一个系统的属性，即在_________的环境下做正确的事。

得分/总分正确答案：已知1单选(1分)图灵测试旨在给予哪一种令人满意的操作定义得分/总分A.人类思考B.人工智能C.机器智能D.机器动作正确答案：C你选对了2多选(1分)选择以下关于人工智能概念的正确表述得分/总分A.人工智能旨在创造智能机器该题无法得分/B.人工智能是研究和构建在给定环境下表现良好的智能体程序该题无法得分/C.人工智能将其定义为人类智能体的研究该题无法得分/D.人工智能是为了开发一类计算机使之能够完成通常由人类所能做的事该题无法得分/正确答案：A、B、D你错选为A、B、C、D3多选(1分)如下学科哪些是人工智能的基础？得分/总分A.经济学B.哲学C.心理学D.数学正确答案：A、B、C、D你选对了4多选(1分)下列陈述中哪些是描述强AI(通用AI)的正确答案？得分/总分A.指的是一种机器，具有将智能应用于任何问题的能力B.是经过适当编程的具有正确输入和输出的计算机，因此有与人类同样判断力的头脑C.指的是一种机器，仅针对一个具体问题D.其定义为无知觉的计算机智能，或专注于一个狭窄任务的AI正确答案：A、B你选对了5多选(1分)选择下列计算机系统中属于人工智能的实例得分/总分搜索引擎B.超市条形码扫描器C.声控电话菜单该题无法得分/D.智能个人助理该题无法得分/正确答案：A、D你错选为C、D6多选(1分)选择下列哪些是人工智能的研究领域得分/总分A.人脸识别B.专家系统C.图像理解D.分布式计算正确答案：A、B、C你错选为A、B7多选(1分)考察人工智能(AI)的一些应用，去发现目前下列哪些任务可以通过AI来解决得分/总分A.以竞技水平玩德州扑克游戏B.打一场像样的乒乓球比赛C.在Web上购买一周的食品杂货D.在市场上购买一周的食品杂货正确答案：A、B、C你错选为A、C8填空(1分)理性指的是一个系统的属性，即在_________的环境下做正确的事。

基因编辑伦理问题英语作文

基因编辑伦理问题英语作文The many bioengineering ethical issues involved in gene editing are very serious.Humans master the genomes of other species on Earth and serve humans well when CRISPR is used for constructive work.Such as the convenient and efficient development of good food crops to ensure world food security; extracting new drugs or testing new therapies through gene-edited animals; tailoring healthier and more aesthetic pets; and treating human single-gene genetic diseases.But gene editing could also be a double-edged sword. Will changing the genome of living organisms have serious irreversible serious consequences for nature?In addition, gene editing will also involve many sensitive topics if used in controversial aspects.For example, the most controversial editorial babies are intended to breed better offspring, such as eliminating genetic factors during the embryo.But people's greed is infinite, once the technology is legal, there will be more and more people demanding a better offspring, to have a more beautiful appearance, a more intelligent mind.These involve editing the baby's right to know and agree, and the survival of most of the masses at the bottom of the pyramid.Advanced and expensive gene editing technology is likely to be the top of the top of the pyramid, when the living space is compressed, and the offspring will not lose at the starting line, but at the fertilized egg.Class stratification is even more serious, nature has become an artificial nature, and acquired efforts are even more meaningless.基因编辑涉及的诸多生物工程伦理问题是非常严肃的。

外文文献—遗传算法

附录I 英文翻译第一部分英文原文文章来源：书名：《自然赋予灵感的元启发示算法》第二、三章出版社：英国Luniver出版社出版日期：2008Chapter 2Genetic Algorithms2.1 IntroductionThe genetic algorithm (GA), developed by John Holland and his collaborators in the 1960s and 1970s, is a model or abstraction of biolo gical evolution based on Charles Darwin’s theory of natural selection. Holland was the first to use the crossover and recombination, mutation, and selection in the study of adaptive and artificial systems. These genetic operators form the essential part of the genetic algorithm as a problem-solving strategy. Since then, many variants of genetic algorithms have been developed and applied to a wide range of optimization problems, from graph colouring to pattern recognition, from discrete systems (such as the travelling salesman problem) to continuous systems (e.g., the efficient design of airfoil in aerospace engineering), and from financial market to multiobjective engineering optimization.There are many advantages of genetic algorithms over traditional optimization algorithms, and two most noticeable advantages are: the ability of dealing with complex problems and parallelism. Genetic algorithms can deal with various types of optimization whether the objective (fitness) functionis stationary or non-stationary (change with time), linear or nonlinear, continuous or discontinuous, or with random noise. As multiple offsprings in a population act like independent agents, the population (or any subgroup) can explore the search space in many directions simultaneously. This feature makes it ideal to parallelize the algorithms for implementation. Different parameters and even different groups of strings can be manipulated at the same time.However, genetic algorithms also have some disadvantages.The formulation of fitness function, the usage of population size, the choice of the important parameters such as the rate of mutation and crossover, and the selection criteria criterion of new population should be carefully carried out. Any inappropriate choice will make it difficult for the algorithm to converge, or it simply produces meaningless results.2.2 Genetic Algorithms2.2.1 Basic ProcedureThe essence of genetic algorithms involves the encoding of an optimization function as arrays of bits or character strings to represent the chromosomes, the manipulation operations of strings by genetic operators, and the selection according to their fitness in the aim to find a solution to the problem concerned. This is often done by the following procedure:1) encoding of the objectives or optimization functions; 2) defining a fitness function or selection criterion; 3) creating a population of individuals; 4) evolution cycle or iterations by evaluating the fitness of allthe individuals in the population,creating a new population by performing crossover, and mutation,fitness-proportionate reproduction etc, and replacing the old population and iterating again using the new population;5) decoding the results to obtain the solution to the problem. These steps can schematically be represented as the pseudo code of genetic algorithms shown in Fig. 2.1.One iteration of creating a new population is called a generation. The fixed-length character strings are used in most of genetic algorithms during each generation although there is substantial research on the variable-length strings and coding structures.The coding of the objective function is usually in the form of binary arrays or real-valued arrays in the adaptive genetic algorithms. For simplicity, we use binary strings for encoding and decoding. The genetic operators include crossover,mutation, and selection from the population.The crossover of two parent strings is the main operator with a higher probability and is carried out by swapping one segment of one chromosome with the corresponding segment on another chromosome at a random position (see Fig.2.2).The crossover carried out in this way is a single-point crossover. Crossover at multiple points is also used in many genetic algorithms to increase the efficiency of the algorithms.The mutation operation is achieved by flopping the randomly selected bits (see Fig. 2.3), and the mutation probability is usually small. The selection of anindividual in a population is carried out by the evaluation of its fitness, and it can remain in the new generation if a certain threshold of the fitness is reached or the reproduction of a population is fitness-proportionate. That is to say, the individuals with higher fitness are more likely to reproduce.2.2.2 Choice of ParametersAn important issue is the formulation or choice of an appropriate fitness function that determines the selection criterion in a particular problem. For the minimization of a function using genetic algorithms, one simple way of constructing a fitness function is to use the simplest form F = A−y with A being a large constant (though A = 0 will do) and y = f(x), thus the objective is to maximize the fitness function and subsequently minimize the objective function f(x). However, there are many different ways of defining a fitness function.For example, we can use the individual fitness assignment relative to the whole populationwhere is the phenotypic value of individual i, and N is the population size. The appropriateform of the fitness function will make sure that the solutions with higher fitness should be selected efficiently. Poor fitness function may result in incorrect or meaningless solutions.Another important issue is the choice of various parameters.The crossover probability is usually very high, typically in the range of 0.7~1.0. On the other hand, the mutation probability is usually small (usually 0.001 _ 0.05). If is too small, then the crossover occurs sparsely, which is not efficient for evolution. If the mutation probability is too high, the solutions could still ‘jump around’ even if the optimal solution is approaching.The selection criterion is also important. How to select the current population so that the best individuals with higher fitness should be preserved and passed onto the next generation. That is often carried out in association with certain elitism. The basic elitism is to select the most fit individual (in each generation) which will be carried over to the new generation without being modified by genetic operators. This ensures that the best solution is achieved more quickly.Other issues include the multiple sites for mutation and the population size. The mutation at a single site is not very efficient, mutation at multiple sites will increase the evolution efficiency. However, too many mutants will make it difficult for the system to converge or even make the system go astray to the wrong solutions. In reality, if the mutation is too high under high selection pressure, then the whole population might go extinct.In addition, the choice of the right population size is also very important. If the population size is too small, there is not enough evolution going on, and there is a risk for the whole population to go extinct. In the real world, a species with a small population, ecological theory suggests that there is a real danger of extinction for such species. Even the system carries on, there is still a danger of premature convergence. In a small population, if a significantly more fit individual appears too early, it may reproduces enough offsprings so that they overwhelm the whole (small) population. This will eventually drive the system to a local optimum (not the global optimum). On the other hand, if the population is too large, more evaluations of the objectivefunction are needed, which will require extensive computing time.Furthermore, more complex and adaptive genetic algorithms are under active research and the literature is vast about these topics.2.3 ImplementationUsing the basic procedure described in the above section, we can implement the genetic algorithms in any programming language. For simplicity of demonstrating how it works, we have implemented a function optimization using a simple GA in both Matlab and Octave.For the generalized De Jong’s test function where is a positive integer andr > 0 is the half length of the domain. This function has a minimum of at . For the values of , r = 100 and n = 5 as well as a population size of 40 16-bit strings, the variations of the objective function during a typical run are shown in Fig. 2.4. Any two runs will give slightly different results dueto the stochastic nature of genetic algorithms, but better estimates are obtained as the number of generations increases.For the well-known Easom functionit has a global maximum at (see Fig. 2.5). Now we can use the following Matlab/Octave to find its global maximum. In our implementation, we have used fixedlength 16-bit strings. The probabilities of crossover and mutation are respectivelyAs it is a maximization problem, we can use the simplest fitness function F = f(x).The outputs from a typical run are shown in Fig. 2.6 where the top figure shows the variations of the best estimates as they approach while the lower figure shows the variations of the fitness function.% Genetic Algorithm (Simple Demo) Matlab/Octave Program% Written by X S Yang (Cambridge University)% Usage: gasimple or gasimple(‘x*exp(-x)’);function [bestsol, bestfun,count]=gasimple(funstr)global solnew sol pop popnew fitness fitold f range;if nargin<1,% Easom Function with fmax=1 at x=pifunstr=‘-cos(x)*exp(-(x-3.1415926)^2)’;endrange=[-10 10]; % Range/Domain% Converting to an inline functionf=vectorize(inline(funstr));% Generating the initil populationrand(‘state’,0’); % Reset the random generatorpopsize=20; % Population sizeMaxGen=100; % Max number of generationscount=0; % counternsite=2; % number of mutation sitespc=0.95; % Crossover probabilitypm=0.05; % Mutation probabilitynsbit=16; % String length (bits)% Generating initial populationpopnew=init_gen(popsize,nsbit);fitness=zeros(1,popsize); % fitness array% Display the shape of the functionx=range(1):0.1:range(2); plot(x,f(x));% Initialize solution <- initial populationfor i=1:popsize,solnew(i)=bintodec(popnew(i,:));end% Start the evolution loopfor i=1:MaxGen,% Record as the historyfitold=fitness; pop=popnew; sol=solnew;for j=1:popsize,% Crossover pairii=floor(popsize*rand)+1; jj=floor(popsize*rand)+1;% Cross overif pc>rand,[popnew(ii,:),popnew(jj,:)]=...crossover(pop(ii,:),pop(jj,:));% Evaluate the new pairscount=count+2;evolve(ii); evolve(jj);end% Mutation at n sitesif pm>rand,kk=floor(popsize*rand)+1; count=count+1;popnew(kk,:)=mutate(pop(kk,:),nsite);evolve(kk);endend % end for j% Record the current bestbestfun(i)=max(fitness);bestsol(i)=mean(sol(bestfun(i)==fitness));end% Display resultssubplot(2,1,1); plot(bestsol); title(‘Best estimates’); subplot(2,1,2); plot(bestfun); title(‘Fitness’);% ------------- All sub functions ----------% generation of initial populationfunction pop=init_gen(np,nsbit)% String length=nsbit+1 with pop(:,1) for the Signpop=rand(np,nsbit+1)>0.5;% Evolving the new generationfunction evolve(j)global solnew popnew fitness fitold pop sol f;solnew(j)=bintodec(popnew(j,:));fitness(j)=f(solnew(j));if fitness(j)>fitold(j),pop(j,:)=popnew(j,:);sol(j)=solnew(j);end% Convert a binary string into a decimal numberfunction [dec]=bintodec(bin)global range;% Length of the string without signnn=length(bin)-1;num=bin(2:end); % get the binary% Sign=+1 if bin(1)=0; Sign=-1 if bin(1)=1.Sign=1-2*bin(1);dec=0;% floating point.decimal place in the binarydp=floor(log2(max(abs(range))));for i=1:nn,dec=dec+num(i)*2^(dp-i);enddec=dec*Sign;% Crossover operatorfunction [c,d]=crossover(a,b)nn=length(a)-1;% generating random crossover pointcpoint=floor(nn*rand)+1;c=[a(1:cpoint) b(cpoint+1:end)];d=[b(1:cpoint) a(cpoint+1:end)];% Mutatation operatorfunction anew=mutate(a,nsite)nn=length(a); anew=a;for i=1:nsite,j=floor(rand*nn)+1;anew(j)=mod(a(j)+1,2);endThe above Matlab program can easily be extended to higher dimensions. In fact, there is no need to do any programming (if you prefer) because there are many software packages (either freeware or commercial) about genetic algorithms. For example, Matlab itself has an extra optimization toolbox.Biology-inspired algorithms have many advantages over traditional optimization methods such as the steepest descent and hill-climbing and calculus-based techniques due to the parallelism and the ability of locating the very good approximate solutions in extremely very large search spaces.Furthermore, more powerful new generation algorithms can be formulated by combiningexisting and new evolutionary algorithms with classical optimization methods.Chapter 3Ant AlgorithmsFrom the discussion of genetic algorithms, we know that we can improve the search efficiency by using randomness which will also increase the diversity of the solutions so as to avoid being trapped in local optima. The selection of the best individuals is also equivalent to use memory. In fact, there are other forms of selection such as using chemical messenger (pheromone) which is commonly used by ants, honey bees, and many other insects. In this chapter, we will discuss the nature-inspired ant colony optimization (ACO), which is a metaheuristic method.3.1 Behaviour of AntsAnts are social insects in habit and they live together in organized colonies whose population size can range from about 2 to 25 millions. When foraging, a swarm of ants or mobile agents interact or communicate in their local environment. Each ant can lay scent chemicals or pheromone so as to communicate with others, and each ant is also able to follow the route marked with pheromone laid by other ants. When ants find a food source, they will mark it with pheromone and also mark the trails to and from it. From the initial random foraging route, the pheromone concentration varies and the ants follow the route with higher pheromone concentration, and the pheromone is enhanced by the increasing number of ants. As more and more ants follow the same route, it becomes the favoured path. Thus, some favourite routes (often the shortest or more efficient) emerge. This is actually a positive feedback mechanism.Emerging behaviour exists in an ant colony and such emergence arises from simple interactions among individual ants. Individual ants act according to simple and local information (such as pheromone concentration) to carry out their activities. Although there is no master ant overseeing the entire colony and broadcasting instructions to the individual ants, organized behaviour still emerges automatically. Therefore, such emergent behaviour is similar to other self-organized phenomena which occur in many processes in nature such as the pattern formation in animal skins (tiger and zebra skins).The foraging pattern of some ant species (such as the army ants) can show extraordinary regularity. Army ants search for food along some regular routes with an angle of about apart. We do not know how they manage to follow such regularity, but studies show that they could move in an area and build a bivouac and start foraging. On the first day, they forage in a random direction, say, the north and travel a few hundred meters, then branch to cover a large area. The next day, they will choose a different direction, which is about from the direction on the previous day and cover a large area. On the following day, they again choose a different direction about from the second day’s direction. In this way, they cover the whole area over about 2 weeks and they move out to a different location to build a bivouac and forage again.The interesting thing is that they do not use the angle of (this would mean that on the fourth day, they will search on the empty area already foraged on the first day). The beauty of this angle is that it leaves an angle of about from the direction on the first day. This means they cover the whole circle in 14 days without repeating (or covering a previously-foraged area). This is an amazing phenomenon.3.2 Ant Colony OptimizationBased on these characteristics of ant behaviour, scientists have developed a number ofpowerful ant colony algorithms with important progress made in recent years. Marco Dorigo pioneered the research in this area in 1992. In fact, we only use some of the nature or the behaviour of ants and add some new characteristics, we can devise a class of new algorithms.The basic steps of the ant colony optimization (ACO) can be summarized as the pseudo code shown in Fig. 3.1.Two important issues here are: the probability of choosing a route, and the evaporation rate of pheromone. There are a few ways of solving these problems although it is still an area of active research. Here we introduce the current best method. For a network routing problem, the probability of ants at a particular node to choose the route from node to node is given bywhere and are the influence parameters, and their typical values are .is the pheromone concentration on the route between and , and the desirability ofthe same route. Some knowledge about the route such as the distance is often used so that ,which implies that shorter routes will be selected due to their shorter travelling time, and thus the pheromone concentrations on these routes are higher.This probability formula reflects the fact that ants would normally follow the paths with higher pheromone concentrations. In the simpler case when , the probability of choosing a path by ants is proportional to the pheromone concentration on the path. The denominator normalizes the probability so that it is in the range between 0 and 1.The pheromone concentration can change with time due to the evaporation of pheromone. Furthermore, the advantage of pheromone evaporation is that the system could avoid being trapped in local optima. If there is no evaporation, then the path randomly chosen by the first ants will become the preferred path as the attraction of other ants by their pheromone. For a constant rate of pheromone decay or evaporation, the pheromone concentration usually varies with time exponentiallywhere is the initial concentration of pheromone and t is time. If , then we have . For the unitary time increment , the evaporation can beapproximated by . Therefore, we have the simplified pheromone update formula:where is the rate of pheromone evaporation. The increment is the amount of pheromone deposited at time t along route to when an ant travels a distance . Usually . If there are no ants on a route, then the pheromone deposit is zero.There are other variations to these basic procedures. A possible acceleration scheme is to use some bounds of the pheromone concentration and only the ants with the current global best solution(s) are allowed to deposit pheromone. In addition, certain ranking of solution fitness can also be used. These are hot topics of current research.3.3 Double Bridge ProblemA standard test problem for ant colony optimization is the simplest double bridge problem with two branches (see Fig. 3.2) where route (2) is shorter than route (1). The angles of these two routes are equal at both point A and pointB so that the ants have equal chance (or 50-50 probability) of choosing each route randomly at the initial stage at point A.Initially, fifty percent of the ants would go along the longer route (1) and the pheromone evaporates at a constant rate, but the pheromone concentration will become smaller as route (1) is longer and thus takes more time to travel through. Conversely, the pheromone concentration on the shorter route will increase steadily. After some iterations, almost all the ants will move along the shorter route. Figure 3.3 shows the initial snapshot of 10 ants (5 on each route initially) and the snapshot after 5 iterations (or equivalent to 50 ants have moved along this section). Well, there are 11 ants, and one has not decided which route to follow as it just comes near to the entrance.Almost all the ants (well, about 90% in this case) move along the shorter route.Here we only use two routes at the node, it is straightforward to extend it to the multiple routes at a node. It is expected that only the shortest route will be chosen ultimately. As any complex network system is always made of individual nodes, this algorithms can be extended to solve complex routing problems reasonably efficiently. In fact, the ant colony algorithms have been successfully applied to the Internet routing problem, the travelling salesman problem, combinatorial optimization problems, and other NP-hard problems.3.4 Virtual Ant AlgorithmAs we know that ant colony optimization has successfully solved NP-hard problems such asthe travelling salesman problem, it can also be extended to solve the standard optimization problems of multimodal functions. The only problem now is to figure out how the ants will move on an n-dimensional hyper-surface. For simplicity, we will discuss the 2-D case which can easily be extended to higher dimensions. On a 2D landscape, ants can move in any direction or , but this will cause some problems. How to update the pheromone at a particular point as there are infinite number of points. One solution is to track the history of each ant moves and record the locations consecutively, and the other approach is to use a moving neighbourhood or window. The ants ‘smell’ the pheromone concentration of their neighbourhood at any particular location.In addition, we can limit the number of directions the ants can move by quantizing the directions. For example, ants are only allowed to move left and right, and up and down (only 4 directions). We will use this quantized approach here, which will make the implementation much simpler. Furthermore, the objective function or landscape can be encoded into virtual food so that ants will move to the best locations where the best food sources are. This will make the search process even more simpler. This simplified algorithm is called Virtual Ant Algorithm (VAA) developed by Xin-She Yang and his colleagues in 2006, which has been successfully applied to topological optimization problems in engineering.The following Keane function with multiple peaks is a standard test functionThis function without any constraint is symmetric and has two highest peaks at (0, 1.39325) and (1.39325, 0). To make the problem harder, it is usually optimized under two constraints:This makes the optimization difficult because it is now nearly symmetric about x = y and the peaks occur in pairs where one is higher than the other. In addition, the true maximum is, which is defined by a constraint boundary.Figure 3.4 shows the surface variations of the multi-peaked function. If we use 50 roaming ants and let them move around for 25 iterations, then the pheromone concentrations (also equivalent to the paths of ants) are displayed in Fig. 3.4. We can see that the highest pheromoneconcentration within the constraint boundary corresponds to the optimal solution.It is worth pointing out that ant colony algorithms are the right tool for combinatorial and discrete optimization. They have the advantages over other stochastic algorithms such as genetic algorithms and simulated annealing in dealing with dynamical network routing problems.For continuous decision variables, its performance is still under active research. For the present example, it took about 1500 evaluations of the objective function so as to find the global optima. This is not as efficient as other metaheuristic methods, especially comparing with particle swarm optimization. This is partly because the handling of the pheromone takes time. Is it possible to eliminate the pheromone and just use the roaming ants? The answer is yes. Particle swarm optimization is just the right kind of algorithm for such further modifications which will be discussed later in detail.第二部分中文翻译第二章遗传算法2.1 引言遗传算法是由John Holland和他的同事于二十世纪六七十年代提出的基于查尔斯·达尔文的自然选择学说而发展的一种生物进化的抽象模型。

GP(Genetic Programming)算法

1 2
然而在实际中，我们要限制其数目和其形态大小。比如说，然而在实际中，我们要限制其数目和其形态大小。比如说，我们可限制一棵树的形态大小为W（用最大结点数表示）。我们可限制一棵树的形态大小为（用最大结点数表示）。一旦W给定那么由所有不超过W个结点给定，个结点，一旦给定，那么由所有不超过个结点，且包含特定子树的树的集合是有限的，的树的集合是有限的，即
一旦w给定那么由所有不超过w个结点且包含特定子树的树的集合是有限的即gp算法的模式理论在gp算法中模式的平均适应度简单地定义为该模式中所有个体适应度的平均值即gp算法的模式理论在gp算法中模式因进化而出现的数目增长或衰减取决于模式的平均适应度与群体平均适应度的比值
GP（Genetic Programming）算法（）
f (H , t ) m(H, t +1) ≥ m(H, t)(1−δ ) f (t)
群体平均适应度；式中 f (t ) ――群体平均适应度；群体平均适应度子代模式的平均适应度；子代模式的平均适应度 f (H, t) ――子代模式的平均适应度；子代属于模式的个体数；子代属于模式的个体数 m(H,t) ――子代属于模式的个体数；模式破坏的概率。模式破坏的概率 δ ――模式破坏的概率。
一概述
GP算法求解问题的主要特征如下：算法求解问题的主要特征如下：算法求解问题的主要特征如下 1、产生的结果具有层次化的特点。、产生的结果具有层次化的特点。 2、随着进化的延续，个体不断朝问题答案的方向动态地发展。、随着进化的延续，个体不断朝问题答案的方向动态地发展。 3、不需事先确定或限制最终答案的结构或大小，GP算法将根、不需事先确定或限制最终答案的结构或大小，算法将根据环境自动确定。这样，系统观测物理世界的窗口得以扩大，据环境自动确定。这样，系统观测物理世界的窗口得以扩大，最终导致找到问题的真实答案。最终导致找到问题的真实答案。 4、输入、中间结果和输出是问题的自然描述，无需或少需对输、输入、中间结果和输出是问题的自然描述，入数据的预处理和对输出结果的后处理。入数据的预处理和对输出结果的后处理。由此而产生的计算机程序便是由问题自然描述的函数组成的。机程序便是由问题自然描述的函数组成的。

pedestrian-detection

Seville
We now have 100 examples. We run learning, and the results improve.
Korea-France SafeMove Workshop
Seville
We test another sequence. We collect in the same way more examples. We re-run the relearning and continue.
Korea-France SafeMove Workshop
Seville
We test another sequence. We collect in the same way more examples. We re-run the relearning and continue.
Korea-France SafeMove Workshop
No
Yes
Yes
NO
Yes
Weak1 0.2
Weak2 0.2
Weak3 0.7
WeakeMove Workshop
AdaBoost at a Glance (Cont.)
The output of AdaBoost is a called a strong classifier. AdaBoost was used for face, cars and pedestrian detection by viola and Jones (2000).
Korea-France SafeMove Workshop
European project CAMELLIA European union
CAMELLIA

Evolutionary_Computation

Hybrids of GP and other methods that better handle numbers:
◦ Least squares methods ◦ Gradient based optimizers ◦ Genetic algorithms, other evolutionary
◦ Selection probability of individual = individual’s fitness/sum of fitness
Rank based selection
◦ Example: decreasing arithmetic/geometric series ◦ Better when fitness range is very large or small
Tournament selection
◦ Virtual tournament between randomly selected individuals using fitness
Point crossover (classical)
◦ Parent1=x1,x2,x3,x4,x5,x6 ◦ Parent2=y1,y2,y3,y4,y5,y6 ◦ Child =x1,x2,x3,x4,y5,y6
Coarse-grained GA at high level Global parallel GA at low level
Coarse-grained GA at high level Coarse-grained GA at low level
Introduced (officially) by John Koza in his book (genetic programming, 1992)

关于教务管理中的自动排课技术可行性的研究

关于教务管理中的自动排课技术可行性的研究发表时间：2013-08-27T08:59:27.827Z 来源：《中国商界》2013年9期供稿作者：杨鹰[导读] 当前国内很多学校还采用计算机手工排课办法，基本上使用计算机系统里的字表处理软件，比如word和excel等软件。

杨鹰/江西景德镇陶瓷学院【摘要】随着科技，信息技术的快速发展，运用计算机技术进行教务管理中的自动排课已经成为大势所趋。

众所周知，排课在高校的教务管理中是一件非常复杂，麻烦的工作。

且随着高校的连年扩招，班级人数的增多，教学规模的不断扩大，再涉及教室资源冲突，开课情况，教师资源等环节，人工排课已经难以满足准确排课的需求。

这就需要借助于计算机，着眼于排课的设计和实现，进行自动排课。

本文介绍了教务管理中自动排课系统的设计和实现，介绍了几种常用的排课算法等内容来验证教务管理中自动排课技术的可行性。

希望自动排课技术可以普及各个高校，让排课不再成为难题。

【关键词】自动排课技术；技术可行性高校的排课工作是为了安排各个课程安排上课地点，时间，授课老师，使得学校的教学工作可以按计划，顺利的开展，是教务管理工作中是一项相当繁琐复杂的工作。

当前国内很多学校还采用计算机手工排课办法，基本上使用计算机系统里的字表处理软件，比如word和excel等软件。

随着学校的扩招，教学任务的增多以及各种不确定事件的发生，这种工作量巨大且效率低下的排课方法很容易产生课程之间的冲突。

为了防止这种情况的激增，现在已经有部分高校采取自动排课技术进行排课，但依旧存在许多地方的不足，在教室，授课教师和资源设计方面还存在问题，适用范围过大，操作也较复杂。

而且大多的院校都是根据自己需求对自动排课系统进行设计的，较难满足特殊教学安排，有一定的局限性。

针对这些问题，下面就让我们来介绍几种常用的排课算法，设计实现改进的算法来验证教务管理中自动排课技术的可行性。

一、常用自动排课算法介绍常用的自动排课算法有Genetic Algorithms法，Constraint Logic Prongramming（CLP）法，相似算法等，下面让我们来依次介绍一下这三种方法。

人工智能进化计算

根本遗传算法的流程如下所述：
根本遗传算法
initialize the population loop until the termination criteria is satisfied for all individuals of population
sum of fitness += fitness of this individual end for for all individuals of population
9.2 遗传算法
遗传算法〔Genetic Algorithms，简称GA〕是由密歇根大学的约翰·亨利·霍兰德〔John Henry Holland〕和他的同事于二十世纪六十年代在对细胞自动机〔cellular automata〕进展研究时率先提出[2]。在二十世纪八十年代中期之前，对于遗传算法的研究还仅仅限于理论方面，直到在匹兹堡召开了第一届世界遗传算法大会。随着计算机计算能力的开展和实际应用需求的增多，遗传算法逐渐进入实际应用阶段。1989 年，纽约时报作者约翰·马科夫〔John Markoff〕写了一篇文章描述第一个商业用途的遗传算法—进化者〔Evolver〕。之后，越来越多种类的遗传算法出现并被用于许多领域中，财富杂志500强企业中大多数都用它进展时间表安排、数据分析、未来趋势预测、预算、以及解决很多其他组合优化问题。
f(x) C 0max g(x)
当 g( xC )max
其他情况
其中，f(x)为转换后的适应度，g(x)为原适应度，Cmax 为足够所以
首先要有一个解码〔decode〕的过程，
即将二进制串解码为十进制的实数，这
也被称为从基因型〔genotype〕f (x)到 x表2 现

【目标管理】张砦-遗传算法在多目标优化中的应用

(3) 发展期(90年代以后) 90年代，遗传算法不断地向广度和深度发展。
• 1991年，wrence出版《Handbook of Genetic Algorithms》一书，详尽地介绍遗传算法的工作细节。 • 1996年 Z.Michalewicz的专著《遗传算法 + 数据结构 = 进化程序》深入讨论了遗传算法的各种专门问题。同年，T.Back的专著《进化算法的理论与实践：进化策略、进化规划、遗传算法》深入阐明进化算法的许多理论问题。 • 1992年，Koza出版专著《Genetic Programming:on the Programming of Computer by Means of Natural Selection》，该书全面介绍了遗传规划的原理及应用实例，表明遗传规划己成为进化算法的一个重要分支。 • 1994年，Koza又出版第二部专著《Genetic Programming Ⅱ:Automatic Discovery of Reusable Programs》，提出自动定义函数的新概念，在遗传规划中引入子程序的新技术。同年， K.E.Kinnear主编《Advances in Genetic Programming》，汇集许多研究工作者有关应用遗传规划的经验和技术。
(1) 生物的所有遗传信息都包含在其染色体中，染色体决定了生物的性状；
(2) 染色体是由基因及其有规律的排列所构成的，遗传和进化过程发生在染色体上；
(3) 生物的繁殖过程是由其基因的复制过程来完成的； (4) 通过同源染色体之间的交叉或染色体的变异会产生新的物种，使生物呈现新的性状。 (5) 对环境适应性好的基因或染色体经常比适应性差的基因或染色体有更多的机会遗传到下一代。
1.1 遗传算法的生物学基础

遗传规划GeneticProgramming

2个自变量 2 1 0 0
考虑：若函数有偶个自变量则该函数返回“T”否则“NIL”：
( N O T ( D O ) A N D N O T ( D I ) ) O R D O A N D D I
OR
AND
AND
NOT
NOT D0
D1
D0
D1
二、初始群体的生成
a、初始个体生成原理用随机方法产生所需解决问题的各种符号表达式
长将个体分为两组Ⅰ、Ⅱ。（Ⅰ 组适应度高，Ⅱ组适应
度低）80﹪的时间在Ⅰ组选择，20﹪的时间在Ⅱ组选
择。
（3）级差选择法：
按适应度将个体分成等级，个体根据级别进行选择。
（4）竞争选择法：
首先从群体中随机选取一组个体，然后从该组中
选出具有较高适应度的个体。
b、交换
交换的实现方法：①在父代群体随机选取两个个体。
子代t 在某一个体i：
适应度：r(i,t) Nc s(i, j)c(j) j1 s ( i , j ) ：个体i在适应度计算试例j下的返回值； N c ：适应度计算试例数； c ( j ) ：适应度试例j的实测值或正确值；
b、标准适应度（Standardized Fitness）适应度最大越好的问题：
（1）随机产生初始群体，即产生众多函数和变量随机组成的计算机程序。
（2）运行群体中的每一个计算机程序（个体），计算适应度。
（3）生成下一代群体。
①根据适应度随机复制新一代个体
②通过在双亲个体随机选定的部位进行交换产生新的个体，双亲个体也依据适应度随机选定。
（4）迭代执行（2）（3）直到终止准则满足为止。
M
n(k,t) 1 i 1
四、基本算子

遗传算法-1

1 遗传算法简介
1.1 生物进化理论和遗传学的基本知识
遗传学基本概念与术语 ➢ 基因型（genotype）：遗传因子组合的模型，染
色体的内部表现； ➢ 表现型（phenotype）：由染色体决定性状的外
部表现，基因型形成的个体；
1111111
1.1 生物进化理论和遗传学的基本知识
交换部分基因产生一组新解的过程编码的某一分量发生变化
例1 用遗传算法求解优化问题
max f (x) x2 ,0 x 31
其中 x 为整数。
产生群体:
p(
xi
)
fitness( xi )
fitness( x j
)
j
x1 (00000 ) x2 (11001)
x3 (01111) x4 (01000 )
1 遗传算法简介
1.1 生物进化理论和遗传学的基本知识
遗传学基本概念与术语 ➢ 变异（mutation）：在细胞进行复制时可能以很
小的概率产生某些复制差错，从而使DNA发生某种变异，产生出新的染色体，这些新的染色体表现出新的性状; ➢ 编码（coding）：表现型到基因型的映射； ➢ 解码（decoding）：从基因型到表现型的映射。
司同时完成
生物遗传学基础
群体
变异
子群
竞争
淘汰的群体
婚配种群
群体
淘汰
遗传基因重组过程
变异
选择
淘汰的个体
新种群
交配
种群
父代染色体1
父代染色体2
生物进化过程
子代染色体1
子代染色体2
1 遗传算法简介
1.1 生物进化理论和遗传学的基本知识遗传学基本概念与术语 ➢ 染色体（chromosome）：遗传物质的载体； ➢ 脱氧核糖核酸（DNA）：大分子有机聚合物，

关于科技伦理的英文作文

The Ethical Dimensions of Technology AdvancementsIn the relentless march of technological progress, humanity has witnessed unprecedented leaps forward in fields ranging from artificial intelligence to biotechnology, space exploration, and renewable energy. These advancements have undeniably enriched our lives, enhancing productivity, fostering innovation, and expanding our understanding of the universe. However, the rapid pace of technological development also poses profound ethical challenges that necessitate careful consideration and ongoing dialogue.The Dual-Edged Sword of TechnologyTechnology, like any powerful tool, is inherently dual-edged. On one hand, it holds the promise of solving some of the world's most pressing problems, such as eradicating diseases, mitigating climate change, and fostering global connectivity. On the other hand, the misuse or over-reliance on technology can lead to unintended consequences, including privacy violations, job displacement, and even existential threats to humanity.Ethical Dilemmas in Artificial IntelligenceOne of the most pressing ethical debates surrounds artificial intelligence (AI). As AI systems become increasingly sophisticated, questions arise about their decision-making processes, accountability, and potential biases. For instance, if an autonomous vehicle is faced with a situation where it must choose between saving the lives of its passengers or pedestrians, who should bear the responsibility for the outcome? Furthermore, the use of AI in areas like hiring, lending, and criminal justice raises concerns about algorithmic bias, potentially exacerbating existing social inequalities.Biotechnology and Genetic ManipulationAdvancements in biotechnology, particularly in the field of genetic engineering, have opened up new avenues for treating genetic diseases and enhancing human capabilities. However, these breakthroughs also raise ethical questions about the boundaries of human intervention. Should we alter the genetic makeup of future generations to prevent disease or enhance abilities? What are the long-term implications of such interventions on biological diversity and human identity?Data Privacy and SurveillanceThe digital age has brought about a paradigm shift in the way we collect, store, and analyze data. While this has facilitated personalization, convenience, and targeted interventions, it has also led to concerns about privacy invasion and unchecked surveillance. The balance between leveraging data for societal good and protecting individual privacy is a delicate one, requiring robust legal frameworks and ethical guidelines.Addressing Ethical ChallengesConfronting these ethical dilemmas requires a multifaceted approach involving collaboration among scientists, policymakers, ethicists, and the public. Firstly, there must be a commitment to transparency and accountability in the development and deployment of technologies. Secondly, international agreements and regulations need to be established to ensure that technological advancements align with universal ethical principles. Thirdly, education and public awareness campaigns are crucial to foster informed debate and empower citizens to make informed choices about technology's role in their lives.ConclusionThe future of technology is shaped by the choices we make today. As we continue to push the boundaries of what is possible, it is imperative that we also navigate the ethical implications of our actions. By engaging in open dialogue, fostering interdisciplinary collaboration, and upholding ethical principles, we can harness the power of technology to create a more equitable, sustainable, and prosperous world for all.。

05计算智能2

16
5.2 进化策略
5.2.1 进化策略的算法模型
❖寻求与函数极值关联的实n维矢量x。 ❖随机选择父矢量的初始群体。 ❖父先矢选量择xxi的, i=标1准,…偏,p差产来生产子生代子矢代量矢xi以量及xi预。 ❖对误差 (i=1,…,p)排序以选择和决定保持
哪些矢量，最小误差为选择对象。 ❖继续产生新的试验数据以及选择最小误
❖评价函数，表示基因的适用能力，也就是基因的优劣程度
❖要有效地反映每个染色体与问题的最有解染色体之间的差距
6
遗传操作
❖选择操作：也称为复制操作，根据个体的适应度函数值所度量的优劣程度决定在下一代是被淘汰还是被遗传，赌轮选择机制
❖交叉操作：串上某位数码
22
❖随机产生的计算机程序种群开始，这些程序适合于问题空间领域的函数组成
❖适应度来评价各个函数 ❖智能行为：预测周围的环境，并且将预
测转化为实现预计目标的适当的反应。该环境可以取自有限字符数字集合符号序列，从而可以采用状态变化来反应环境，从而可以采用有限状态机来表达
23
5.3 进化编程
5.3.2 进化编程的步骤
择将进入下一代的个体; (4) 按概率Pc进行交叉操作; (5) 按概率Pc进行突变操作; (6) 若没有满足某种停止条件，则转第(2)步，
否则进入下一步。 (7) 输出群体中适应度值最优的染色体作为问题的
满意解或最优解。停止条件：进化代数限制；没有明显进化；
9
开始初始化种群计算适应度值
选择操作交叉操作变异操作终止条件初始化种群
(3) 把在后代中出现的最好的个体字符串指定为遗传算法的执行结果，这个结果可以表示问题的一个解。
11
GEN:=0

TSP问题的遗传算法求解

TSP问题的遗传算法求解一、问题描述假设有一个旅行商人要拜访N个城市，要求他从一个城市出发，每个城市最多拜访一次，最后要回到出发的城市，保证所选择的路径长度最短。

二、算法描述（一）算法简介遗传算法（GeneticAlgorithm）是模拟达尔文生物进化论的自然选择和遗传学机理的生物进化过程的计算模型，通过模拟自然进化过程搜索最优解。

遗传算法是从代表问题可能潜在的解集的一个种群（population）开始的，初代种群产生之后，按照适者生存和优胜劣汰的原理，逐代（generation）演化产生出越来越好的近似解，在每一代，根据问题域中个体的适应度（fitness）大小选择个体，并借助于自然遗传学的遗传算子（geneticoperators）进行组合交叉（crossover）和变异（mutation），产生出代表新的解集的种群。

这个过程将导致种群像自然进化一样的后生代种群比前代更加适应于环境，末代种群中的最优个体经过解码（decoding），可以作为问题近似最优解。

（摘自百度百科）。

（二）遗传算子遗传算法中有选择算子、交叉算子和变异算子。

选择算子用于在父代种群中选择进入下一代的个体。

交叉算子用于对种群中的个体两两进行交叉，有Partial-MappedCrossover、OrderCrossover、Position-basedCrossover等交叉算子。

变异算子用于对种群中的个体进行突变。

（三）算法步骤描述遗传算法的基本运算过程如下：1.初始化：设置进化代数计数器t=0、设置最大进化代数T、交叉概率、变异概率、随机生成M个个体作为初始种群P2.个体评价：计算种群P中各个个体的适应度3.选择运算：将选择算子作用于群体。

以个体适应度为基础，选择最优个体直接遗传到下一代或通过配对交叉产生新的个体再遗传到下一代4.交叉运算：在交叉概率的控制下，对群体中的个体两两进行交叉5.变异运算：在变异概率的控制下，对群体中的个体两两进行变异，即对某一个体的基因进行随机调整6.经过选择、交叉、变异运算之后得到下一代群体P1。

笛卡尔遗传规划CartesianGeneticProgramming（CGP）简单理解（1）

笛卡尔遗传规划CartesianGeneticProgramming（CGP）简单理解（1）初识遗传算法Genetic Algorithm(GA) 遗传算法是计算数学中⽤于解决最优化的搜索算法，是进化算法的⼀种。

进化算法借鉴了进化⽣物学中的⼀些现象⽽发展起来的，这些现象包括遗传、突变、⾃然选择以及杂交等，是⼀个通过计算机模拟解决最优化问题的过程，遗传算法从代表问题可能存在的⼀个解集的⼀个种群(population)开始的,⼀个种群由⼀定数量的候选解也称为个体(individual)组成，个体由基因(gene)编码⽽成，基因的表现形式实际上是每个个体上带有的染⾊体(chromosome) 染⾊体即为基因的集合，应⽤遗传算法的⼀般步骤是：1.需要实现表现形到基因型的编码⼯作，常⽤编码⽅法有⼆进制编码、格雷码编码、浮点编码和符号编码。

2.进化从随机个体(初代种群)的种群开始，之后⼀代⼀代进化。

按照优胜劣汰的准则在每⼀代中，评价整个种群的适应度(fitness)，从当前种群中选择(selection)多个个体(基于它们的适应度)。

3.借助于⾃然遗传学的遗传算⼦(genetic operators)进⾏组合交叉(crossover)和变异(mutation) 产⽣新的种群，该种群在算法的下⼀次迭代中成为新的种群。

4.在末代种群中的最优个体通过解码(decoding)产⽣最优解笛卡尔遗传规划介绍笛卡尔遗传规划源⾃ Miller 等⼈对进化数字电路的发展。

1999 年出现了专门研究笛卡尔遗传规划的团队2000 年由 Miller 等⼈发表了Cartesian Genetic Programming，正式提出了笛卡尔遗传规划的⼀般形式。

笛卡尔遗传规划由⼀个带索引节点的有向图表⽰，这是⼀个有n 个输⼊m个输出的有向图(directed graph)，其中输⼊节点的下标为 0 到 n-1，输出由最后⼀列的m个节点得到。

(少儿有声英语点读本)Causes and Causal Inference

Causes and Causal InferenceContentsSection 1：What is a cause?Section 2：Causal theories and causal models Section 3：A Model of Sufficient Cause and Component Causes Section 4：Causal criteria/viewpoints0102034Objectives•To understand some basic theories of causal inference.•To define the concepts of “necessary” and “sufficient” in the context of causal relationships.•To present guidelines for judging whether an association is causal.•To differentiate between real and spurious associations in observational studies.Section 1.What is a cause?Cause•Cause: general refers to etiology•Other definitions:–an antecedent event, condition, or characteristic that was necessary for the occurrence of the disease at the moment it occurred, given that other conditions are fixed.–a factor or event that affect the upcoming diseases, and once it changes, the probability of the disease changes accordingly.•Causal models penetrate the concepts, ideas, theories and methods of epidemiology.•Understand causal inference is premise of public health.Cause and causal inference•Causal inference: the quantification of the causal effect of an exposure or treatment on an outcome.•Three important conditions for causal inference:–Temporal sequence•B always follow A–Association•A and B should have an association, statistically–Consequential change•the changes in A will cause a subsequent change in Bassociations1.Single cause-single effect: A single cause can lead to a singleeffect–for example,smallpox and a radiator leak can cause the car to overheat;2.Multiple causes-single effect: Many different causes can be seenas leading to one effect–for example, smoking, diabetes, hypertension and hypercholesterolemia can lead to MI or CHD.3.Single cause-multiple effects: A single cause can be seen asproducing numerous effects.–For example, car accident can cause deaths, bone fracture, or injury.associations•Direct cause and indirect cause–Direct cause: can lead to the occurrence of the disease directly.–Indirect cause: lead to the disease via at least one other risk factors.–Causal associations can be direct or indirect. Intervene the causal pathway can effectively achieve disease prevention and control.A B DIndirect cause Direct causeSection 2Causal theories and causalmodelsTheories of disease causation•Miasma theory.•Germ theory.•Epidemiological triangle.•Web of causation•The word "miasma" comes fromancient Greek and means "pollution".•Miasma was considered to be a poisonous vapor or mist filled with particles from decomposed matter (miasmata) that caused illnesses.•In the 1850s, miasma was used to explain the spread of cholera in London and in Paris.and the dead air in the SouthernChinese mountains. They thought that insects’ waste polluted the air, water.•The miasma theory was consistent with observations that disease wasassociated with poor sanitation.•As a result of the miasma theory…•Although proved to be scientifically unsound, it led to sanitary reforms that resulted in enormous progress in public health.•Health problems were believed to be the product ofliving organisms which entered the body through food, water, air or the bites of insects or animals.•It was believed that each disease has a single and a specific cause (mono-causal approach).•As a result of the germ theory…•Research was moved from the community tothe laboratory and concentrated on theidentification of agents for a given disease.•Medical practice became devoted to thedestruction or eradication of the agent fromindividuals already affected. Robert Koch: a Germanphysician and microbiologist.Koch's four postulates1.The organism must always be present, in every case of the disease.2.The organism must be isolated from a host containing the disease and grown in pure culture.3.Samples of the organism taken from pure culture must cause the same disease when inoculated into a healthy, susceptible animal in the laboratory.4.The organism must be isolated from the inoculated animal and must be identified as the same original organism first isolated from the originally diseased host.Epidemiological triangle•According to this theory,exposure to an agent does notnecessarily lead to disease.•It was believed that disease isthe result of an interactionbetween agent, host and theenvironment.Bacteria Viruses Fungi Age Sex Health Behavior Immunity Genetic makeup Air Water SanitationNoiseEpidemiological triangle•As a result of the epidemiological triangle theory…•It was believed that diseases can be prevented by modifying factors which influence exposure and susceptibility.•This is useful in understanding infectious disorders, but less useful in dealing with non-communicable chronic diseases (NCD) such as heart diseases and diabetes.•For NCDs there is no specific agent that could be identified against which individual and population may be protected.Web of causation•According to this concept, disorders are developed through complex interaction of many factors.•These factors maybe biophysical, social or psychological and may promote or inhibit the disease at more than one point in the causalprocess. •Ultimately, they determine the level of disease in a community.CauseCauseCauseCauseEffect \DiseaseWeb of causation•As a result of this theory…•Based on this theory, it is believed that prevention offers a better prospects for health than cure, since many of these factors can be modified.•Since several factors contributes to several diseases, community efforts were shifted to factors modification (prevention) rather than disease treatment.•This approach is not so much concerned with the causes of disease, rather it seeks to identify the broad factors (ie, social factors) that make and keep people healthy .•It is concerned with the population rather than individuals. •The Dahlgren-Whitehead 'rainbow model’:developed by Göran Dahlgren and MargaretWhitehead in 1991, maps the relationshipbetween the individual, their environmentand health.Ecological model•As a result of this theory:•Health actions shifted from the individual to the community as a whole.•Improving health requires political and regulatory actions to modify social, economical and physical environment.–i.e., non-smoking area, nutrition label, ‘Care For Girls’…Section 3. A Model of Sufficient Cause and Component Causes•Definition of “cause”–Any event, act, or condition–preceding disease or illness–without which disease would not have occurred or would have occurred at a later time•Introduce in 1976 by Kenneth Rothman.Disease results from the cumulative effects of multiple causes acting together (causal interaction)Kenneth Rothman (contemporary epidemiologist)• E.g., depiction of the constellation of component causes that constitute a sufficient cause for hip fracture for a particular person at a particular time.•In the diagram:–A represents poor weather,–B represents an uncleared path for pedestrians,–C represents a poor choice of footgear,–D represents the lack of a handrail,–U represents all of the unknow factors•Contributing cause: needed in some cases•Sufficient cause: the constellation of necessary & contributing causes that make disease inevitable in anindividual•Causal complement: the set of factors that completes a sufficient causal mechanism •Example: tuberculosis•Necessary agent: Mycobacterium tuberculosis•Causal complement: “Susceptibility”M.tuberculosis SusceptibilityCategories Necessary Sufficient ExampleNecessary andsufficient++Smallpox virus and smallpox [*rare]Necessary butnot sufficient+-Agents of all infectious diseasesSufficient but not necessary -+Aircraft crashes and deaths, butthere are multiple causes of death.Not necessaryand not sufficient --Hyperlipidemia and coronary heartdisease, as well as the causes ofalmost all chronic non-communicable diseasesSection 4Causal criteria/viewpointsOrigins of Bradford Hill’s viewpoint•Proposed by Sir Austin Bradford Hill•Causality between smoking and lungcancer in the 1964 Report of the AdvisoryCommittee to the US Surgeon General:Smoking and Health•Rationale: A finding satisfy several criteriawas more likely to be causal than one thatsatisfied only a few or noneHill’s criteria/viewpoints1.Strength2.Consistency3.Specificity4.Temporality5.Biologic Gradient6.Plausibility7.Coherence8.Experimental Evidence9.AnalogyStrength•Strong associations are more likely to suggest causal relation because you will need to have very strong confounding to bias your estimates away from null•Example–An odds ratio of 10+ for smoking and lung cancer.Strength (to be noted)•Strong associations are more likely to suggest causal relation because you will need to have very strong confounding to bias your estimates away from null•Comments:• A weak association does not rule out causality–Smoking and cardiovascular diseases• A strong association can still be confounded–Down syndrome and birth rank, potentially confounded by maternal age•The repeated observation of an association in different populations under circumstances•Example:–Consistent results obtained by replicating the studies on smoking and lung cancer in different populations•The repeated observation of an association in different populations under circumstances•Comments:•Lack of consistency can be due to effect modification –Particular disease only occurs in individuals with specific genetic background.A further note on consistency•Lack of statistical significance in some studies does not automatically mean inconsistency–Difference in statistical significance can be driven by many factorsalthough the underlying results from these studies could all be the same •Sample sizes•Standard errors•Other differences–Specific circumstances of exposure–Design and analytic strategies Odds ratioStudy 1Study 21Specificity•Two variants:–A cause leads to single effect, not multiple effects–An effect has one cause, not multiple causes•Examples–The association of smoking to disease seems “quantitatively specific” to lung cancerSpecificity•Two variants:–A cause leads to single effect, not multiple effects–An effect has one cause, not multiple causes•Comments:–Smoking affects the risk of many diseases including cancers,cardiovascular diseases–Similarly, an effect can be due to multiple causes such as alcohol use, smoking on stroke mortality•The necessity that the cause precedes the effect in time •Very straightforward•Example–Smoking must be present before the lung cancer develops.Smoking Lung cancer•The necessity that the cause precedes the effect in time•Very straightforward•Comments–the correct time sequence does not guarantee a causal relationship.–i.e., Every morning the cock crows before the sun rises. Can we say that the cock's crowing produces the sunrise?•The presence of a dose-response or exposure-response relation •Linear relation•Example–More smoking means more carcinogen exposure and more tissuedamage, hence more opportunity for carcinogenesis.Risk of diseaseExposure level•The presence of a dose-response or exposure-response relation •Linear relation•Comments–Does linear relation with the outcome means causal?•Birth rank and Down syndrome, confounded by increasing maternal age –Does exposure have to have a linear relationship with the outcome?•Different possible combinations•However, deviation from linear relation can be a sign of bias OutcomeExposureOutcomeExposureOutcomeExposureSmoking and lung cancer Alcohol and CVD DES and adenocarcinoma ofthe vagina•Scientific plausibility of an association•Would need input from other disciplines such as toxicology, pharmacology, basic biology or other disciplines•Example: Moderate alcohol use and lower cardiovascular diseases, where moderate alcohol use increases HDL cholesterol (the “good” cholesterol)HDLAlcohol use CVD•Scientific plausibility of an association•Would need input from other disciplines such as toxicology, pharmacology, basic biology or other disciplines •Comments:–What is biologically plausible depends upon the biological knowledge of the day.–Examples include•Miasma vs germ theory in cholera outbreak•HDL cholesterol and cardiovascular diseasesCoherence•The cause and effect interpretation does not conflict with what is known of the natural history and biology of the disease.•Example–Difference in lung cancer incidence by sex, if smoking causes cancer –Very difficult to distinguish from biologic plausibilityExperimental evidence •The presence of experimental evidencemay refer to clinical trials, experimental studies on animals.•Hill’s own interpretation: Obtained from reducing or eliminating a putatively harmful exposure and seeing if the frequency of disease subsequently declines•Example–An intervention to reduce smoking and see ifcancer incidence declinesInterventionTime Diseaseincidence。