基于块同步模型的并行遗传程序设计方法

采用并行计算方法可以有效避免遗传程序设计执行周期长的缺点。在分析遗传程序设计原理及块同步并行计算模型的基础上,以Linux多处理机系统为物理平台,实现了基于粗粒度并行模式的遗传程序设计方法,并对人工蚂蚁问题求解时处理机数与进化代数的关系进行统计和分析,实践表明采用并行计算模式可以更快的获得最优解。     

Ada语言的发展

Ada语言诞生于1979年,1980年被指定为美国军用标准,1983年被正式确立为ISO标准并投入使用。Ada所追求的主要目标是:程序的可靠性与可维护性、程序设计作为人的活动(强调程序可读性比可写性更重要)以及效率。Ada凭借其强大的功能、良好的可靠性以及对软件工程思想的优良体现在20世纪最后20年对程序设计语言的发展产生了重要影响。Ada广泛应用于高可靠、长生存期的大型软件研发,在军事、商业、公共交通、金融等领域的核心软件开发中发挥着重要作用。诸多欧美国家的国防与空中管制系统、交通运输系统、银行安全防卫系统等均使用Ada语言研制开发。迄今为止,国际标准组织先后确立过Ada 83,Ada 95,Ada 2005,Ada 2012等4个语言标准,新标准在旧标准的基础上均保持了良好的兼容性。从语言机制、应用、影响力等方面对Ada语言的发展进行全面的介绍和分析。     

Biomimetic Representation with Genetic Programming Enzyme

The standard parse tree representation of genetic programming, while a good choice from a generative viewpoint, does not capture the variational demands of evolution. This paper addresses the issue of whether representations in genetic programming might be improved by mimicry of biological behaviors, particularly those thought to be important in the evolution of metabolic pathways, the ‘computational’ structures of the cell. This issue is broached through a presentation of enzyme genetic programming, a form of genetic programming which uses a biomimetic representation. Evaluation upon problems in combinational logic design does not show any significant performance advantage over other approaches, though does demonstrate a number of interesting behaviors including the preclusion of bloat. An erratum to this article can be found at     

Genetic Programming with simple loops

A kind of loop function LoopN in Genetic Programming(GP)is proposed.Different from other forms of loop function,such as While-Do and Repeat-Until,LoopN takes only one argument as its loop body and makes its loop body simply run N times,so infinite loops will never happen.The problem of how to avoid too many layers of loops in Genetic Programming is also solved.The advantage of LoopN in GP is shown by the computational results in solving the nower problem.     

用于模具编程的病毒进化遗传算法

针对模具编程的任务规划问题,提出一种编程任务规划算法。以工期、质量和编程人员负载均衡等为目标,建立多目标优化模型,结合病毒进化遗传算法的横向搜索能力和非支配排序遗传算法Ⅱ(NSGA-Ⅱ)的精英保留策略,求取多样性解,引入多种群思想解决多目标问题。仿真结果表明,该算法求得的近似解分布均匀且多样性较好,与NSGA-Ⅱ相比,其解的分布离最优解的前端更加接近。     

Exact Schema Theory for Genetic Programming and Variable-Length Genetic Algorithms with One-Point Crossover

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.     

Genetic Programming with a Genetic Algorithm for Feature Construction and Selection

The use of machine learning techniques to automatically analyse data for information is becoming increasingly widespread. In this paper we primarily examine the use of Genetic Programming and a Genetic Algorithm to pre-process data before it is classified using the C4.5 decision tree learning algorithm. Genetic Programming is used to construct new features from those available in the data, a potentially significant process for data mining since it gives consideration to hidden relationships between features. A Genetic Algorithm is used to determine which such features are the most predictive. Using ten well-known datasets we show that our approach, in comparison to C4.5 alone, provides marked improvement in a number of cases. We then examine its use with other well-known machine learning techniques.     

Genetic Programming Experiments with Standard and Homologous Crossover Methods

While successful applications have been reported using standard GP crossover, limitations of this approach have been identified by several investigators. Among the most compelling alternatives to standard GP crossover are those that use some form of homologous crossover, where code segments that are exchanged are structurally or syntactically aligned in order to preserve context and worth. This paper reports the results of an empirical comparison of GP using standard crossover methods with GP using homologous crossover methods. Ten problems are tested, five each of pattern recognition and regression.Results suggest that in terms of generalization accuracy, homologous crossover does generate consistently better performance. In addition, there is a consistently lower fraction of introns that are generated in the solution code.     

一种改进的遗传规划算法

遗传规划的实质是用广义的层次化计算机程序描述问题．比较适合于求解一类由于各种不确定因素导致的复杂非线性问题。该文采用了一种改进的遗传规划算法,建立了相应的预测模型,将其与Weka里的GP算法在标准数据集上进行对比测试,结果表明该改进的遗传规划算法是有效的、可行的。     

遗传编程实现的研究

本文指出了遗传编程中的几个关键步骤，针对遗传编程的特点，提出实现的不同方法。并提出了一个通用的模型，以虚拟机来提高遗传编程的效率。     

Reinforced Genetic Programming

This paper introduces the Reinforced Genetic Programming (RGP) system, which enhances standard tree-based genetic programming (GP) with reinforcement learning (RL). RGP adds a new element to the GP function set: monitored action-selection points that provide hooks to a reinforcement-learning system. Using strong typing, RGP can restrict these choice points to leaf nodes, thereby turning GP trees into classify-and-act procedures. Then, environmental reinforcements channeled back through the choice points provide the basis for both lifetime learning and general GP fitness assessment. This paves the way for evolutionary acceleration via both Baldwinian and Lamarckian mechanisms. In addition, the hybrid hints of potential improvements to RL by exploiting evolution to design proper abstraction spaces, via the problem-state classifications of the internal tree nodes. This paper details the basic mechanisms of RGP and demonstrates its application on a series of static and dynamic maze-search problems.     

结合序列线性规划法的混合遗传算法

通过将遗传算法与改进的序列线性规划法相结合，形成混合遗传算法．当迭代点没有发生交叉和变异时，将目标函数和约束条件在迭代点处线性化，为使迭代点邻域仍然满足约束条件，加入软约束项，用线性规划方法进行寻优．该方法具有全局收敛性，不要求迭代点一定为可行点．仿真结果验证了此法的有效性和合理性．     

遗传算法程序的模块化设计

随着优化问题的复杂化和优化对象的多样化，对优化方法的主要实现形式－计算机程序设计提出了更高的要求，这里分析了一种进化算法－遗传算法特点，给出一种通用性与计算效率并重的模块化程序设计方法，在不同类型的优化问题中均能取得性能良好的解，在工程计算领域有很强的应用价值，目前已应用于网络图优化，生产作业排序等方面。     

遗传规划中近似函数的求解问题

文中给出了两个测试例子以及它们的结果,提出了一种在给定区间以及给定运算符集合和终止符集合下求解近似函数的遗传规划方法,它在传统遗传算法的基础上克服了传统遗传算法中染色体必须是0,1二进制位串的缺点。通过这种方法,可以把遗传算法应用于更广泛的领域。     

遗传编程运行期个体多样性分析方法及应用

文中根据遗传算法理论分析了遗传编程中种群多样性对算法收敛特性的影响,提出了一种可行的种群多样性跟踪评测方法,同时提出了优选父代个体的改进方法。以求解旅行商问题为例,通过统计性实验数据验证了改进后的算法较采用同样局部优化的常规遗传算法具有更好的收敛速度和优化解,同时也对改进后算法的相关控制参数选择进行了实验分析,结论为改进算法能获得更好的收敛性能。     

遗传编程在符号回归中的应用

遗传编程是一种新型的搜索优化技术,文章介绍了遗传编程的基本原理,以及遗传编程的算法设计及其实现的几个关键问题,并研究了基于遗传编程方法的符号回归。与传统回归方法相比,该方法得到的拟合函数更精确,具有更广泛的适用性。文中通过对一个函数进行符号回归验证,说明此方法合理可行。     

基于遗传编程的网格资源调度算法

网格资源调度是一个非常重要的研究课题.由于因特网的开放、动态性,传统的资源调度和分配方法已经不再适用网格计算,基于经济模型的资源管理和调度成为研究热点.在计算市场模型中,构造有效的效益函数又是提高算法性能的关键.有关文献中采用的是线性效益函数,虽然降低了复杂度,但不能很好地反映用户的效益.文中提出了基于遗传编程来寻找和构造非线性效益函数的方法,并将其应用到网格调度算法中.实验结果表明该算法可以提高网格中的资源调度性能.     

基于遗传编程的网格资源调度算法

网格资源调度是一个非常重要的研究课题。由于因特网的开放、动态性,传统的资源调度和分配方法已经不再适用网格计算,基于经济模型的资源管理和调度成为研究热点。在计算市场模型中,构造有效的效益函数又是提高算法性能的关键。有关文献中采用的是线性效益函数,虽然降低了复杂度,但不能很好地反映用户的效益。文中提出了基于遗传编程来寻找和构造非线性效益函数的方法,并将其应用到网格调度算法中。实验结果表明该算法可以提高网格中的资源调度性能。     

Evolving Teams of Predictors with Linear Genetic Programming

This paper applies the evolution of GP teams to different classification and regression problems and compares different methods for combining the outputs of the team programs. These include hybrid approaches where (1) a neural network is used to optimize the weights of programs in a team for a common decision and (2) a realnumbered vector (the representation of evolution strategies) of weights is evolved with each term in parallel. The cooperative team approach results in an improved training and generalization performance compared to the standard GP method. The higher computational overhead of team evolution is counteracted by using a fast variant of linear GP.