第 4 章 遗传算法（ 1）内容： 遗传算法简介、生物背景、搜寻空间；遗传算法、遗传算子(包括选择算子、交叉算子和变异算子)、遗传算 法参数；重点、难点：遗传算法、遗传算子(包括选择算子、交叉算子和变异算子)1IntroductionGenetic algorithms are a part of evolutionary computing , which is a rapidly growing area of artificial intelligence. As you can guess, genetic algorithms are inspired by Darwins theory about evolution. Simply said, solution to a problem solved by genetic algorithms is evolved.Idea of evolutionary computing was introduced in the 1960s by I. Rechenberg in his work Evolution strategies (Evolutionsstrategiein original). His idea was then developed by other researchers. Genetic Algorithms (GAs) were invented by John Holland and developed by him and his students and colleagues. This lead to Hollands book Adaption in Natural and Artificial Systems published in 1975.2、 Biological BackgroundChromosome 染色体All living organisms consist of cells. In each cell there is the same set of chromosomes. A chromosome consist of genes 基因, blocks of DNA. Each gene encodes a particular protein 蛋白质. Basically can be said, that each gene encodes a trait 特征, for example color of eyes. Possible settings for a trait (e.g. blue, brown) are called alleles 等位基因. Each gene has its own position in the chromosome. This position is called locus 所在 地.Each cell con tai ns in herited gen etic in forrait ion, packaged in the formof gen es. A gene is made of a len gth of DNA (deoxyrib onu clei acid) that has a message en coded in ts chemical structure. Genes are the in structi ons that give orga nism their particular characteristics - for example, your genes code for your hair colour and eye colourComplete set of genetic material (all chromosomes) is called genome . Particular set of genes in genome is called genotype 基因型. The genotype is with later development after birth base for the organisms phenotype 显 型 , its physical and mental characteristics, such as eye color, intelligence etc.Phenotype spaceGenotype space(inverse representation)ReproductionDuring reproduction, first occurs recombination (or crossover). Genes from parents form in some way the whole new chromosome. The new created offspring can then be mutated. Mutation means, that the elements of DNA are a bit changed. These changes are mainly caused by errors in copying genes from parents.The fitness of an organism is measured by success of the organism in its life.3、 Search SpaceSearch SpaceIf we are solving some problem, we are usually looking for some solution, which will be the best among others. The space of all feasible solutions (it means objects among those the desired solution is) is calledsearch space (also state space). Each point in the search space represent one feasible solution. Each feasible solution can be marked by its value or fitness for the problem. We are looking for our solution, which is one point (or more) among feasible solutions - that is one point in the search space.The looking for a solution is then equal to a looking for some extreme (minimum or maximum) in the search space. The search space can be whole known by the time of solving a problem, but usually we know only a few points from it and we are generating other points as the process of finding solution continues.The problem is that the search can be very complicated. One does not know where to look for the solution and where to start. There are many methods, how to find some suitable solution (ie. not necessarily the best solution ), for example hill climbing, tabu search, simulated annealing and genetic algorithm. The solution found by these methods is often considered as a good solution, because it is not often possible to prove what is the real optimum.4、Genetic AlgorithmBasic DescriptionGenetic algorithms are inspired by Darwins theory about evolution. Solution to a problem solved by genetic algorithms is evolved.Algorithm is started with a set of solutions (represented by chromosomes) called population. Solutions from one population are taken and used to form a new population. This is motivated by a hope, that the new population will be better than the old one. Solutions which are selected to form new solutions (offspring) are selected according to their fitness - the more suitable they are the more chances they have to reproduce.This is repeated until some condition (for example number of populations or improvement of the best solution) is satisfied.Outline of the Basic Genetic Algorithm1Start Generate random population of n chromosomes (suitable solutions for the problem)2Fitness Evaluate the fitness f(x) of each chromosome x in the population3New population Create a new population by repeating following steps until the new population is complete(1)Selection Select two parent chromosomes from a population according to their fitness (the better fitness, the bigger chance to be selected)2) Crossover With a crossover probability cross over the parents to form a new offspring (children)