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。     

遗传规划在测量数据拟合中的应用

针对测量数据问题，提出了一种根据遗传规划来寻求最匹配曲线的新方法。与传统的曲线拟合方法相比，该方法只需给定数据点及允许误差即可得到匹配的曲线方程。对于自由曲线用传统的方法是无法拟合的，而遗传规划却可以轻而易举地做到。     

基于改进遗传规划算法的数据拟合*

针对传统数据拟合方法需预先估计基函数、依赖于应用领域等问题,基于遗传规划的动态可变特性,提出将遗传规划与最小二乘法结合,设计具有一定通用性和自适应能力的数据拟合算法。在分析传统遗传规划算法的基础上,详细介绍了算法改进方法,并针对各种类型的拟合数据进行了对比实验。实验结果表明,该算法不仅可以应用到多种场合,而且可以提高拟合效率与精度。     

基于混合蛙跳和遗传规划的跨单元调度方法

针对运输能力受限条件下的跨单元问题,提出了一种基于混合蛙跳与遗传规划的超启发式算法.将改进的混合蛙跳算法作为超启发式算法的高层框架,为跨单元调度问题搜索启发式规则,同时利用遗传规划产生可以兼顾多因素的优质规则,用于扩充超启发式算法的规则集.实验表明,提出的算法可以有效地搜索出优异的规则组合,并且通过遗传规划产生的规则可以在很大程度上改善候选规则集,提升算法性能.     

精确求解一类动态规划问题的神经网络

根据一类动态规划问题的特点，提出一种能够精确求解此问题的神经网络。ＬＤＰＮＮ具有结构简单、易于硬件实现、求解速度快并且能够求得精确最优解等优点，特点适合于大规模动态规划问题的求解。在复杂系统的实时优化与控制等方面具有广阔的应用前景。     

遗传规划算法在化合物设计、筛选研究中的应用

采用计算机科学中新兴的遗传规划算法思想，结合化学物质的本质特点，运用进化操作来实现化合物的合成设计和筛选。文中针对算法运用讨论了函数集、终止集问题，通过计算元素组成的字符串的化合价的结果来确定适应度函数，既符合化学学科的本质规律，又满足了算法的要求。通过复制、交换和突变操作，经过多代次的进化终止，取得了满意的结果。从实验可以看到，经过遗传规划算法的操作，得到了很多合理的化合物设计和筛选结果。所以说，该方法的应用有效地促进了化合物实验合成和筛选。文章还针对其实用性，从化学本质出发，提出了建议和研究方向。可以说本文是遗传规划在化学化合物合成筛选中运用的成功探索，同时也为进一步研究打下了坚实的基础。     

基于遗传规划技术的碳势测定新方法

本文阐述了遗传规划的原理和技术．提出一种基于遗传规划技术的碳势测定新方法。该方法不依赖于问题域，无需对输入的数据进行预处理，可作为解决相关问题的通用方法。该方法精度高，成本低，利于在线检测与控制，可以获得显性的预测函数表达式，能够处理复杂的非线性问题。     

基于遗传规划技术的碳势测定新方法

本文阐述了遗传规划的原理和技术,提出一种基于遗传规划技术的碳势测定新方法。该方法不依赖于问题域,无需对输入的数据进行预处理,可作为解决相关问题的通用方法。该方法精度高,成本低,利于在线检测与控制,可以获得显性的预测函数表达式,能够处理复杂的非线性问题。     

基于多目标遗传规划的软测量建模方法

分析了利用遗传规划进行复杂非线性系统建模时容易出现过学习现象问题的原因,提出了一个基于插值函数保护法、一个评价函数光滑度的准则和基于多目标非支配排序的改进的遗传规划方法.利用非支配排序的思想结合传统的遗传规划来实现对于模型的精确度、复杂度和光滑度的平衡从而提高学习结果的泛化能力.将该方法应用于工业阿维菌素发酵过程中的菌丝浓度估计,取得了良好的效果.     

遗传规划中的正交试验研究

提出用正交试验的方法来优化选择遗传规划的参数,对于不同领域的优化问题,用此方法进行一次参数优化组合,就可以用遗传规划对具体问题进行优化。它在一定程度上可以避免遗传规划中参数选取的盲目性。通过对两个符号回归问题的验证,说明此方法是合理可行的。     

基于模拟退火算法的遗传程序设计方法

遗传程序设计（GP）是运用遗传算法的思想，通过生成计算机程序来解决问题的，但用它来解决大型或复杂问题时，就存在一些难以解决的问题，尤其是大量使用计算机内存和CPU时间，大大影响了工作性能。以符号回归问题为例，针对传统的遗传程序设计方法在解决问题时所遇到的困难，提出一个基于模拟退火算法的遗传程序设计方法，进一步提高GP系统求解问题的能力。     

Implementation of functional parallel typified language (FPTL) on multicore computers

A functional programming language supporting implicit parallelization of programs is described. The language is based on four operations of composition, of which three can perform parallel processing. Functional programs are represented schematically to use a dynamic parallelization algorithm. The implemented algorithms make it possible to dynamically distribute the load between processors and control the grain of parallelism. Experimental results for the efficiency of the implemented system obtained on examples of typical problems are presented.     

DELPHI完美实现动态界面

因工作需要编写了一套机房管理软件,其中涉及到。了“动态界面”这个技术点,在DELPHI中利用对INI文件读取、查询使用,利用代码在程序中动态创建控件技术以及利用汉字首字母拼音进行查询等技术,在DELPHI中完美实现了动态界面技术,大大提高了程序的可维护性与扩展性,并成功简化了程序界面的开发工作。     

Empowering teachers to create educational software: A constructivist approach utilizing Etoys,pair programming and cognitive apprenticeship

This study investigates whether a visual programming environment called Etoys could enable teachers to create software applications meeting their own instructional needs. Twenty-four teachers who participated in the study successfully developed their own educational computer programs in the educational technology course employing cognitive apprenticeship and pair programming approaches as the primary instructional strategies. Two educational software programs created by the participating teachers were described in order to explain what they were trying to do using Etoys and how they accomplished their goals. The results of an anonymous survey evaluating the difficulty of and the attitude toward learning Etoys indicate that teachers enjoyed learning Etoys and would like to continue to use it in the future although they found it was slightly more difficult, compared to their self-evaluated computer skill. The strengths and weaknesses of Etoys, the difficult computer programming concepts, and the educational implications of Etoys programming were also discussed.     

遗传程序设计方法综述

近年来,遗传程序设计（genetic programming,GP)的研究引起了人们很大的关注,它运用遗传算法（genetic algorithm,GA)的思想,通过生成计算机程序来解决问题,介绍了遗传程序设计的研究状况以及目前的研究进展,概述了它的基本算法、主要特点、理论与技术,同时介绍了一些GP实现系统以及主要的应用领域,最后探讨了遗传程序设计的研究方向。     

Dynamic programming on a functional memory computer

In a previous paper [1], we described the solution of dynamic programming problems on a new class of parallel processing systems, the Hawaii Parallel Computer (HPC). The HPC has a novel architecture distinguished by its incorporation of field programmable gate arrays to evaluate expressions and by its use of a decision-table data structure to represent computer programs. As specific examples, we showed how the HPC can be used to implement dynamic programming solutions of shortest-path and traveling-salesman problems. In that earlier implementation, we simply adapted algorithms intended for execution on conventional deterministic von Neumann computers. More recently, we designed a successor to the HPC, a “functional memory” computer, which includes constructs for nondeterministic computation. In this paper, we discuss how dynamic programming algorithms can be adapted to take advantage of this nondeterminism.     

Scaling of program functionality

The distribution of fitness values (landscapes) of programs tends to a limit as the programs get bigger. We use Markov chain convergence theorems to give general upper bounds on the length of programs needed for convergence. How big programs need to be to approach the limit depends on the type of the computer they run on. We give bounds (exponential in N, N log N and smaller) for five computer models: any, average or amorphous or random, cyclic, bit flip and four functions (AND, NAND, OR and NOR). Programs can be treated as lookup tables which map between their inputs and their outputs. Using this we prove similar convergence results for the distribution of functions implemented by linear computer programs. We show most functions are constants and the remainder are mostly parsimonious. The effect of ad-hoc rules on genetic programming (GP) are described and new heuristics are proposed. We give bounds on how long programs need to be before the distribution of their functionality is close to its limiting distribution, both in general and for average computers. The computational importance of destroying information is discussed with respect to reversible and quantum computers. Mutation randomizes a genetic algorithm population in generations. Results for average computers and a model like genetic programming are confirmed experimentally.     

一种线性表示的遗传程序设计方法研究

遗传程序设计领域中的一个重要研究内容是如何有效地表示进化的个体（计算机程序），对采用树的线性后缀形式的个体进行位置信息编码以实现多种形式的遗传操作，并给出形式化定义，设计并实现了一个基于栈的遗传程序设计算法，通过模拟实验比较了各操作的性能，这种编码方式可以扩展到程序的线性结构中，以实现特定的遗传操作，显示出线性表示具有适于解决不同问题的可行性和灵活性，还给出了基于串的一点交叉的线性遗传程序设计的模式理论，它可以把标准遗传算法的模式生成机制统一到该理论框架中。     

Automatic mineral identification using genetic programming

Automatic mineral identification using evolutionary computation technology is discussed. Thin sections of mineral samples are photographed digitally using a computer-controlled rotating polarizer stage on a petrographic microscope. A suite of image processing functions is applied to the images. Filtered image data for identified mineral grains is then selected for use as training data for a genetic programming system, which automatically synthesizes computer programs that identify these grains. The evolved programs use a decision-tree structure that compares the mineral image values with one other, resulting in a thresholding analysis of the multi-dimensional colour and textural space of the mineral images. Received: 18 October 1999 / Accepted: 20 January 2001     

DOM/XML-based portable genetic representation of the morphology,behavior and communication abilities of evolvable agents

This article presents the results of our work on the role of genetic representation in facilitating the quick design of efficiently running offline learning via genetic programming (GP). An approach using the widely adopted document object model/extensible mark-up language (DOM/XML) standard for the representation of genetic programs, and off-the-shelf DOM-parsers with built-in application programming interface (API) for manipulating them is proposed. This approach means a significant reduction in time in the usually slow software engineering of GP, and offers a generic way to facilitate the reduction of computational effort by limiting the search space of genetic programming by handling only semantically correct genetic programs. The concept is accomplished through strongly typed genetic programming (STGP), in which the use of W3C-recommended standard XML schema is proposed as a generic way to represent and impose the grammar rules in STGP. The ideas laid in the foundation of the proposed approach are verified by the implementation of GP in the evolving social behavior of agents in predator–prey pursuit problems.This work was presented in part at the 8th International Symposium on Artificial Life and Robotics, Oita, Japan, January 24–26, 2003