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实数编码的演化算法求解TSP问题 总被引:1,自引:0,他引:1
对新近提出的求解TSP问题的实数编码的染色体表示方式进行了研究,为了去除存在于这种染色体表示方式中的冗余,对其进行了改动,然后设计了相应的多父体杂交算子和变异算子,完成了一个实数编码的求解TSP问题的演化算法。实验结果表明,这个算法是可行的,能够使解收敛到一定的程度,但还需要提高其收敛的能力。所以下一步的工作重点在于根据这种染色体表示方式的特点,进一步研究更合适的算子,从而得到更好的解。 相似文献
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基因表达式程序设计(GEP)的染色体由具有特殊限制的头、尾组成,并要求尾部符号严格取自基本的终端集。这一做法作用明了、易于表述,基本为现有GEP所采纳,但不利于语义计算的重用。谋求突破尾部限制条件,探究一种开放尾部的新型GEP算法。该算法将运行过程产生的优良个体动态地引入种群个体的基因,从而实现运算精度的提升。符号回归实验表明,开放尾部的GEP算法在平均精度性能上要优于主流GEP方法。 相似文献
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采用实数编码的染色体表示方式,先后自行设计实现了两种演化算法求解TSP问题.其中第二种算法中使用了自适应演化算子,能有效消除路径上的交叉,并能在一定程度上进行合理的段位移,更加符合该染色体表示方式的特点.实验结果表明,用实数编码的染色体表示方式求解TSP是可行的,而且使用自适应演化算法求解可以取得比较好的结果. 相似文献
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多表达式编程是一种基因可复用的线性遗传程序设计方法,目前已应用于许多数据挖掘问题,但在分类问题中的研究还比较少.针对多表达式编程的编码特点并结合现有分类方法,提出一种新的分美算法.该算法将分类规则蕴含于多表达式编程的染色体内,并按照适者生存的原则对分类规则进行演化挖掘.实验表明该算法具有可行性,能够达到较高分类精度. 相似文献
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基因表达式程序设计(GEP)是应用十分广泛的自动程序设计方法.就解码方法而言,它主要依据广度优先原则来实施从个体表示到表达式的转换.这代表基因片段的含义会因环境的变化而变化.为此,现有GEP对个体的评估缺乏并发支持能力.本文从理论与实验两个方面证实:深度优先原则及个体多解技术,即让单个染色体编码多个解的技术,既可解决以上GEP困境也可显著改善其性能. 相似文献
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通过分析连续型属性数据的特点和已有的关联规则挖掘算法,在定量描述的准确性和算法的高效性方面作了进一步研究,针对已有的通过结合最大一项集和隶属函数值去计算染色体的适应值的模糊遗传挖掘算法速度慢的问题,提出一种基于聚类的模糊遗传关联规则挖掘算法。该算法采用模糊遗传原理在交易数据中同时提取关联规则和隶属函数。同时,采用k-means聚类算法对种群中的染色体进行分类并且依据分类得到的信息和自身的信息评估每个染色体的适应性,从而降低了扫描数据库的次数,测试结果表明该算法速度快,准确度高。 相似文献
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论述了逻辑程序设计中剪枝算子的作用及传统剪枝算子的过程性语义和说明性语义不一致问题;介绍了新型逻辑程序语言〔淑划中的COTRTT11t剪枝算子;通过引入一组定义描述其过程语义,并进一步阐述了剪枝算子和延迟计算规则之间的关系,讨论了Godel语言的剪枝策略及控制机制,从而为逻辑程序语言的实现提供了依据。 相似文献
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Mihai Oltean 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2007,11(6):503-518
A new model for evolving evolutionary algorithms (EAs) is proposed in this paper. The model is based on the multi expression
programming (MEP) technique. Each MEP chromosome encodes an evolutionary pattern which is repeatedly used for generating the
individuals of a new generation. The evolved pattern is embedded into a standard evolutionary scheme which is used for solving
a particular problem. Several evolutionary algorithms for function optimization are evolved by using the considered model.
The evolved evolutionary algorithms are compared with a human-designed genetic algorithm. Numerical experiments show that
the evolved evolutionary algorithms can compete with standard approaches for several well-known benchmarking problems. 相似文献
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Evolutionary computation is a class of global search techniques based on the learning process of a population of potential solutions to a given problem, that has been successfully applied to a variety of problems. In this paper a new approach to the construction of neural networks based on evolutionary computation is presented. A linear chromosome combined to a graph representation of the network are used by genetic operators, which allow the evolution of the architecture and the weights simultaneously without the need of local weight optimization. This paper describes the approach, the operators and reports results of the application of this technique to several binary classification problems. 相似文献
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基于函数级FPGA原型的硬件内部进化 总被引:24,自引:0,他引:24
电路进化设计是现阶段可进化硬件(EHW)研究的重点内容,针对制约进化设计能力的主要“瓶颈”,该文提出并讨论了一种简洁高效的内部进化方法,包括基于函数变换的染色体高效编码方案,与之配套的函数级FPGA原型和进化实验平台以及在线评估与遗传数自适应方法等,交通灯控制器,4位可级联比较器等相对复杂且具应用价值的电路的成功进化,证明该方法适用于组合,时序电路的进化设计,并可显著地减少运算量,提高进化设计的速度和规模。 相似文献
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Pei-Chann Chang Meng-Hui Chen 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2014,18(6):1177-1188
Recently, estimation of distribution algorithms (EDAs) have gradually attracted a lot of attention and have emerged as a prominent alternative to traditional evolutionary algorithms. In this paper, a block-based EDA using bivariate model is developed to solve combinatorial problems. Instead of generating a set of chromosomes, our approach generates a set of promising blocks using bivariate model and these blocks are reserved in an archive for future use. These blocks will be updated every other k generation. Then, two rules, i.e., AC1 and AC2, are developed to generate a new chromosome by combining the set of selected blocks and rest of genes. This block based approach is very efficient and effective when compared with the traditional EDAs. According to the experimental results, the block based EDA outperforms EDA, GA, ACO and other evolutionary approaches in solving benchmark permutation problems. The block based approach is a new concept and has a very promising result for other applications. 相似文献
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Mahdi Saadatmand-Tarzjan Hamid Abrishami Moghaddam 《IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics》2007,37(1):139-153
Optimization of content-based image indexing and retrieval (CBIR) algorithms is a complicated and time-consuming task since each time a parameter of the indexing algorithm is changed, all images in the database should be indexed again. In this paper, a novel evolutionary method called evolutionary group algorithm (EGA) is proposed for complicated time-consuming optimization problems such as finding optimal parameters of content-based image indexing algorithms. In the new evolutionary algorithm, the image database is partitioned into several smaller subsets, and each subset is used by an updating process as training patterns for each chromosome during evolution. This is in contrast to genetic algorithms that use the whole database as training patterns for evolution. Additionally, for each chromosome, a parameter called age is defined that implies the progress of the updating process. Similarly, the genes of the proposed chromosomes are divided into two categories: evolutionary genes that participate to evolution and history genes that save previous states of the updating process. Furthermore, a new fitness function is defined which evaluates the fitness of the chromosomes of the current population with different ages in each generation. We used EGA to optimize the quantization thresholds of the wavelet-correlogram algorithm for CBIR. The optimal quantization thresholds computed by EGA improved significantly all the evaluation measures including average precision, average weighted precision, average recall, and average rank for the wavelet-correlogram method. 相似文献
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This paper presents a new computational finance approach, combining a Symbolic Aggregate approXimation (SAX) technique together with an optimization kernel based on genetic algorithms (GA). The SAX representation is used to describe the financial time series, so that, relevant patterns can be efficiently identified. The evolutionary optimization kernel is here used to identify the most relevant patterns and generate investment rules. The proposed approach was tested using real data from S&P500. The achieved results show that the proposed approach outperforms both B&H and other state-of-the-art solutions. 相似文献
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Pei-Chann Chang Meng-Hui Chen Manoj K. Tiwari Asif Sikandar Iquebal 《Applied Soft Computing》2013,13(12):4536-4547
Combinatorial problems like flow shop scheduling, travel salesman problem etc. get complicated and are difficult to solve when the problem size increases. To overcome this problem, we present a block-based evolutionary algorithm (BBEA) which will conduct evolutionary operations on a set of blocks instead of genes. BBEA includes the block mining and block recombination approaches. A block mining algorithm is developed to decompose a chromosome into a set of blocks and rest of genes. The block is with a fixed length and can be treated as a building block in forming a new chromosome later on. To guide the block mining process, a gene linkage probability matrix is defined that shows the linkage strength among genes. Therefore the blocks can be further evolved during the evolutionary processes using this matrix. In the block recombination approach, the blocks along with the rest of genes are recombined to form a new chromosome. This new evolutionary approach of BBEA is tested on a set of discrete problems. Experimental results show that BBEA is very competitive when compared with traditional GA, EA or ACGA and HGIA approaches and it can largely improve the performance of evolutionary algorithm and save a fair amount of computational times simultaneously. 相似文献
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基因表达式编程初始种群的多样化策略 总被引:27,自引:0,他引:27
基因表达式编程(Gene Expression Programming,GEP)算法是遗传家族的新成员,被广泛用于知识发现,其初始种群的质量对进化效率和进化结果至关重要.为了产生优势初始种群,提出了基因空间均匀分布策略(Gene Space Balance Strategy,GSBS),证明了描述编码空间量化性质的GEP编码空间定理.实验表明,GSBS提高进化效率超过20%.GSBS算法的思想还可以应用于其它进化计算中. 相似文献