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1.
刘丽芳  孟志刚  张常利 《软件》2012,(2):114-116
本文针对遗传算法具有早熟或局部收敛的缺点,根据种群熵S的实际意义,设计了一种可按照当前种群熵S的大小自动切换适应度函数的自适应适应度函数。对基本遗传算法,分别采用指数适应度函数,反比例适应度函数和本文定义的自适应适应度函数,在三种常用检测函数上进行实验,结果表明采用自适应适应度函数的基本遗传算法继承了指数适应度函数和反比例适应度函数的优点,既有强劲的收敛能力,又能保持种群多样性,可以更好更快更精确地收敛到问题的最优解。  相似文献   

2.
基于生态种群竞争模型的协同进化   总被引:33,自引:0,他引:33  
遗传算法基于适应度的进化模式没有考虑进化的外部环境和进化成分之间的关系,这是协同进化研究的内容.借鉴生态学对个体生存环境和种群竞争的认识,构造了一种基于生态种群竞争模型的新的协同进化模式.模拟实验表明,采用该模式的改进遗传算法在改善未成熟收敛和收敛速度两方面具有良好的性能.  相似文献   

3.
传统的优化算法大多数由于计算量大或者容易使性能指标落入局部最优值而严重制约了模型的应用与发展,而用遗传算法等新的智能算法求解则会很简洁和方便。文章针对遗传算法在应用中存在的局限性,采用了小生境技术的遗传算法,结合精英保留策略、种群多样性保持方案、新的适应度值标定方式等改进遗传算法。通过对动态交通分配的特点分析,建立了动态交通分配模型,利用改进的遗传算法对模型进行求解。仿真结果体现了动态交通分配模型的有效性和改进遗传算法的优越性,大大提高了动态交通分配模型的实用价值。  相似文献   

4.
基于遗传算法的图像分割处理技术研究   总被引:1,自引:0,他引:1  
旨在使用遗传算法对带有底部噪声的图像进行处理,并通过对遗传算法的改进实现处理效果的提升。结合图像分割阐述了遗传算法的工作机制,分析了适应度计算、选择、交叉和变异等主要模块的设计方法,阐明了代沟与优秀个体的关系、不同代间的个体替换关系、交叉点的选取方法与变异位置的选择、种群数量的保持等关键性问题,并给出了参数设置的具体值。使用该算法对带底部噪声的图片进行了处理,结果表明传统遗传算法可以将目标图像从存在噪声的背景中分离出来,但处理时间为7.416 s。为提高处理效率,利用进化代数和个体的适应度值自适应地调整种群的交叉概率和变异概率对传统算法进行了改进。使用改进的遗传算法对同一噪声图像进行了分割处理,结果表明改进后的遗传算法图像分割效果更佳,处理时间仅为0.751 s,效率提高了近10倍。  相似文献   

5.
结合免疫机制和数论中的佳点集理论,给出了一种免疫佳点集遗传算法。该算法把数论中佳点集理论运用于遗传算法交叉操作和种群初始化的改进,提出带权欧氏距离计算抗体的相似度、浓度和适应度,引入免疫机制使群体保持多样性和快速导向高适应度模式。实验结果验证了该算法可以有效地避免早熟,改善算法的全局收敛性,提高搜索效率。  相似文献   

6.
云自适应遗传算法   总被引:5,自引:1,他引:5  
传统自适应遗传算法(AGA)虽能有效提高收敛速度,却难以增强算法的鲁棒性.以当代种群平均适应度为期望Ex,根据云模型"3En"规则确定熵En,由X条件云发生器自适应调整交叉变异概率,提出云自适应遗传算法(CAGA).由于云模型云滴具有随机性和稳定倾向性特点,使交叉变异概率值既具有传统AGA的趋势性,满足快速寻优能力;又具有随机性,且当种群适应度最大时并非绝对的零概率值,有利于提高种群多样性,从而大大改善避免陷入局部最优的能力.典型函数优化实验表明,与标准遗传算法(SGA)和AGA相比,CAGA具有更好的收敛速度和鲁棒性.  相似文献   

7.
改进遗传算法搜索性能的研究   总被引:1,自引:0,他引:1  
本文针对改进遗传算法的搜索性能,提出了一种新的变异算子-自适应多位变异算子,它根据每一代的每一个体在当代中的优劣程度,自适应地控制其发生变异的位数。适应度差的个体变异的位数多,一方面使差的模式生存机会少,另一方面增加了种群的多样性,同时也扩大了搜索范围。我们用模式定理证明了该算子的有效性,并且我们作的仿真实验也同样表明该算子能大大改进遗传算法的搜索性能。  相似文献   

8.
为了有效地应用遗传算法解决H2/H∞鲁棒控制系统设计问题,将遗传算法与局部优化方法相结合,提出了基于降维扫描方法的自适应多目标遗传算法(DRSA-MOGA)。通过引入适应度函数标准化方法、基于最优Pareto解集搜索的降维扫描方法和适应度函数自适应调整方法,提高了算法的全局优化性能和局部搜索能力。仿真结果表明,DRSA-MOGA算法在不损失解的均匀度的情况下可以达到很高的逼近度。  相似文献   

9.
一种基于模式分析的防止遗传算法过早收敛的方法   总被引:1,自引:0,他引:1  
张羽飞  冯汝鹏 《信息与控制》2004,33(1):23-26,30
本文提出一种遗传算法中模式的表示方法和个体间最大共有模式的获取方法,并以此为基础提出了基于模式分析的种群插入策略来解决遗传算法过早收敛问题.通过与其他种群插入算法的对比证明该 方法的有效性.给出了采用该种群插入策略的遗传算法的收敛性定理及其证明.  相似文献   

10.
电力系统无功优化问题是一个多变量、多约束的混合非线性规划问题,其操作变量既有连续变量又有离散变量,其优化过程比较复杂。遗传算法是模拟生物在自然环境中的遗传和进化过程而形成的一种自适应的全局优化搜索算法,可用于解决含有离散变量的复杂优化问题。本文选用遗传算法求解电力系统无功优化问题,并对基本遗传算法的编码、初始种群、适应度函数和交叉、变异策略等进行改进,使用本文提出的改进算法对IEEE1 4节点进行无功优化计算,结果证明本文模型和算法的实用性、可靠性和优越性。  相似文献   

11.
A few schema theorems for genetic programming (GP) have been proposed in the literature in the last few years. Since they consider schema survival and disruption only, they can only provide a lower bound for the expected value of the number of instances of a given schema at the next generation rather than an exact value. This paper presents theoretical results for GP with one-point crossover which overcome this problem. First, we give an exact formulation for the expected number of instances of a schema at the next generation in terms of microscopic quantities. Due to this formulation we are then able to provide an improved version of an earlier GP schema theorem in which some (but not all) schema creation events are accounted for. Then, we extend this result to obtain an exact formulation in terms of macroscopic quantities which makes all the mechanisms of schema creation explicit. This theorem allows the exact formulation of the notion of effective fitness in GP and opens the way to future work on GP convergence, population sizing, operator biases, and bloat, to mention only some of the possibilities.  相似文献   

12.
This paper is the second part of a two-part paper which introduces a general schema theory for genetic programming (GP) with subtree-swapping crossover (Part I (Poli and McPhee, 2003)). Like other recent GP schema theory results, the theory gives an exact formulation (rather than a lower bound) for the expected number of instances of a schema at the next generation. The theory is based on a Cartesian node reference system, introduced in Part I, and on the notion of a variable-arity hyperschema, introduced here, which generalises previous definitions of a schema. The theory includes two main theorems describing the propagation of GP schemata: a microscopic and a macroscopic schema theorem. The microscopic version is applicable to crossover operators which replace a subtree in one parent with a subtree from the other parent to produce the offspring. Therefore, this theorem is applicable to Koza's GP crossover with and without uniform selection of the crossover points, as well as one-point crossover, size-fair crossover, strongly-typed GP crossover, context-preserving crossover and many others. The macroscopic version is applicable to crossover operators in which the probability of selecting any two crossover points in the parents depends only on the parents' size and shape. In the paper we provide examples, we show how the theory can be specialised to specific crossover operators and we illustrate how it can be used to derive other general results. These include an exact definition of effective fitness and a size-evolution equation for GP with subtree-swapping crossover.  相似文献   

13.
基于三进制表示的新模式定理   总被引:1,自引:0,他引:1  
一般的模式定理通常使用二进制表示且只讨论了模式存活的概率而没有考虑模式新建的概率,因此它们不能准确地描述模式的变化情况.本文提出了一种新的模式表示法:三进制表示法.利用这种新的表示法,很容易区分模式的存活和新建,并分别估计了在均匀杂交算子作用下模式的存活概率和新建概率.最后对均匀杂交算子给出了在模式的存活和模式的新建共同作用下的模式定理,这一结果深入和推广了目前模式定理的结果.  相似文献   

14.
In the study of data exchange one usually assumes an open-world semantics, making it possible to extend instances of target schemas. An alternative closed-world semantics only moves ‘as much data as needed’ from the source to the target to satisfy constraints of a schema mapping. It avoids some of the problems exhibited by the open-world semantics, but limits the expressivity of schema mappings. Here we propose a mixed approach: one can designate different attributes of target schemas as open or closed, to combine the additional expressivity of the open-world semantics with the better behavior of query answering in closed worlds. We define such schema mappings, and show that they cover a large space of data exchange solutions with two extremes being the known open and closed-world semantics. We investigate the problems of query answering and schema mapping composition, and prove two trichotomy theorems, classifying their complexity based on the number of open attributes. We find conditions under which schema mappings compose, extending known results to a wide range of closed-world mappings. We also provide results for restricted classes of queries and mappings guaranteeing lower complexity.  相似文献   

15.
Previous proofs of data flow determinacy proceeded indirectly and relied on results from abstract semantics. In this paper, a direct proof of the determinacy of data flow schema is given. This proof does not use high powered metamathematical theorems and consequently is accessible to all researchers in data flow, not just those intimately familiar with deep semantic results. Our proof proceeds in three stages. First, the data flow schema which contains instances of Link, Sink and Operator actors is proved to be determinate by reduction to the results of Karp and Miller (1966). Second, data flow schema containingSwitch actors, in addition to the above actors, are proved to be determinate. Finally, data flow schema which additionally contain Apply actors are proven to be determinate.  相似文献   

16.
RETE网络中的优化编译模式及其PVS形式验证   总被引:1,自引:0,他引:1  
刘晓建  陈平 《计算机科学》2003,30(6):168-171
In the compilation of rule program to the intermediate code-RETE network,optimizing compilation is an important compiler schema,and is a necessary step in the compiler verification.In this paper,we discuss optimization schemas in rule program compilation,and prove the semantic equivalence theorems of these schemas.Firstly,the structure of RETE network and its PVS specification are represented.Secondly,three kinds of optimization schemas are listed.Then algorithms evaluating semantics of target RETE network are given.Finally,we prove the semantic equivalence theorems with theorem prover PVS (Prototype Verification System).  相似文献   

17.
Schema theory is the most well-known model of evolutionary algorithms. Imitating from genetic algorithms (GA), nearly all schemata defined for genetic programming (GP) refer to a set of points in the search space that share some syntactic characteristics. In GP, syntactically similar individuals do not necessarily have similar semantics. The instances of a syntactic schema do not behave similarly, hence the corresponding schema theory becomes unreliable. Therefore, these theories have been rarely used to improve the performance of GP. The main objective of this study is to propose a schema theory which could be a more realistic model for GP and could be potentially employed for improving GP in practice. To achieve this aim, the concept of semantic schema is introduced. This schema partitions the search space according to semantics of trees, regardless of their syntactic variety. We interpret the semantics of a tree in terms of the mutual information between its output and the target. The semantic schema is characterized by a set of semantic building blocks and their joint probability distribution. After introducing the semantic building blocks, an algorithm for finding them in a given population is presented. An extraction method that looks for the most significant schema of the population is provided. Moreover, an exact microscopic schema theorem is suggested that predicts the expected number of schema samples in the next generation. Experimental results demonstrate the capability of the proposed schema definition in representing the semantics of the schema instances. It is also revealed that the semantic schema theorem estimation is more realistic than previously defined schemata.  相似文献   

18.
半结构化数据的模式抽取对于半结构化数据查询、优化及异构数据的集成具有重要的意义.结合标签路径及标签路径的目标集概念,提出了基于OEM(Object Exchange Model)模型的半结构化数据最小化模式抽取新方法,并给出了与标签路径目标集、支持度计算相关的两个定理.算法的基本思路:依据文中的两个定理,采用宽度优先自顶向下的遍历策略依次求出各标签路径的最后一个标签的目标集及支持度,标签支持度大的目标集优先映射为对应的模式节点.对同一半结构数据实例,算法抽取的模式与其他算法得到的模式相比规模小、算法执行时间短.算法适用于层次型及包含环路的OEM半结构化数据模式抽取.  相似文献   

19.
20.
陈亚端  廖士中 《计算机科学》2010,37(10):207-210,245
Ising图模型概率推理的主要工作是通过变量求和来计算配分函数和边缘概率分布。传统计算复杂性理论证明Ising图模型精确概率推理是NP难的,并且Ising图模型近似概率推理是NP难的。研究了Ising图模型精确概率推理和Ising均值场近似概率推理的参数化复杂性。首先证明了不同参数的Ising图模型概率推理的参数化复杂性定理,指出基于变量个数或图模型树宽的参数化概率推理问题是固定参数可处理的。然后证明了Ising均值场的参数化复杂性定理,指出基于自由分布树宽、迭代次数和变量个数的参数化Icing均值场是固定参数可处理的;进一步,当Ising图模型参数满足Ising均值场迭代式压缩条件时,基于自由分布树宽和迭代次数的参数化Ising均值场是固定参数可处理的。  相似文献   

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