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。     

改进的基因表达式程序设计实现复杂函数的自动建模

基因表达式程序设计(简称GEP)是一种新型的遗传算法,它继承了遗传程序设计(简称GP)和遗传算法的优点并且具有更高的效率和更强的搜索能力,但同时也存在缺乏学习机制,搜索过于盲目的缺点,针对其缺点对其进行了如下改进:(1)改变了GEP的基因表达式结构,将原来的“头+尾”结构改成了“头+身+尾”结构,以利于其引进学习机制;(2)同源基因也采用“头+身+尾”结构,以利于增强其搜索能力;用其实现复杂函数的自动建模,实例测试的结果表明用改进的基因表达式程序设计得到的模型比传统方法得到的模型要好,甚至优于用遗传程序设计和基本的基因表达式程序设计得到的模型。     

基于自动定义函数GP的自适应建模研究

遗传程序设计(Genetic Programming，简称GP)在进化过程中由于种群多样性的损失，常导致低收敛性．本文尝试将自动定义函数引入到GP中克服这个问题，并应用于数据的自适应建模，文中将两者的性能进行了比较，实验表明自动定义函数的发现和使用增加了种群的多样性．它不仅降低了整个遗传程序的大小，还增加了GP搜索的计算有效性，提高了收敛性能，取得了满意的结果．     

GEP在数字图像分割中的应用

基因表达式程序设计（简称GEP）是一种新型的遗传算法，它继承了遗传程序设计（简称GP）和遗传算法（简称GA）的优点并且具有更高效和更强的搜索能力，它是借鉴生物选择和进化机制发展起来的一种高度并行、随机、自适应的搜索算法。特别适合于处理传统搜索算法解决不好的复杂的和非线性问题。本文将在系统介绍表达式程序设计的基本理论基础上．介绍其在数字图像分割中的应用。     

线性遗传程序设计比较分析

遗传程序设计是近几年各专家学者研究的热点之一。主要论述了6种线性遗传程序设计的原理,比较分析了各线性遗传程序设计的共同点和差异性,简单介绍了各遗传程序设计的应用领域,总结了针对不同的问题采用相应的遗传程序设计的方法。     

自动程序设计的研究

自动程序设计是计算机科学的中心目标之一,围绕中心目标进行研究是计算机科学工作者的责任。采用一种形式化方法,即演化计算(EvolutionaryComputation)理论,特别是其中的遗传程序设计(GeneticProgramming,简称GP)的理论,深刻研究了自动程序设计的概念和遗传算法。基于该方法的自动程序设计能够模拟生物进化的机制来适应外界环境,并获得最佳解,从而使得自动程序设计获得较好效果。     

基因评估基因表达式程序设计方法

基因表达式程序设计(Gene Expression Programming,简称GEP)与遗传程序设计(Genetic Programming,简称GP)相比,具有更强的搜索能力、更简单的编码表示方法和产生更高复杂性函数的能力.但是它也存在一些缺点,例如缺乏学习机制,搜索过于盲目.针对这一缺点,本文提出了一种新的自动程序设计方法:基因评估基因表达式程序设计(Gene Estimated Gene Expression Programming,简称GEGEP).与GEP相比,GEGEP主要具有如下特点:(1)改变了GEP的基因表达式结构,将原来的“头 尾”结构改成了“头 身 尾”结构,以利于其引进学习机制;(2)同源基因也采用“头 身 尾”结构,以利于增强其搜索能力;(3)引入了分布评估算法(Estimation of Distribution Algorithm,简称EDA)的思想,以利于增加其学习能力并且加快其收敛速度.实验结果表明,与GEP和GP相比,GEGEP具有更高的拟合和预测精度、更快的收敛速度.     

软件可靠性的GP演化建模

为解决软件可靠性模型的不一致性,摆脱传统模型多种主观假设的束缚,提出了采用遗传程序设计(GP)的演化算法建立基于软件失效间隔时间序列的软件可靠性模型。针对装甲兵工程学院的某软件测试用例进行演化建模,并对其性能指标进行了分析评价,结果验证了GP算法的可行性以及拟合、预测的有效性,从而能有效地应用于软件系统测试,保障数据的有效性。     

遗传程序设计算法中选择调整参数的光滑拟合方法

对遗传程序设计(GP)算法中的适应度评价函数光滑拟合问题进行了研究,结合LAM(Linear Association Memory)和HJ(Hook和Jeevs)两种方法,估计GP树数值权值,以减少GP树适应度值评价的计算代价。提出了一种选择调整参数的新方法,同时,给出了一个数学例子,并与广义交叉实验B一样条函数仿真比较验证。     

遗传程序设计模式理论新进展

1.引言在模式识别、细胞自动控制、太空卫星控制、机器人控制、电路设计等领域,遗传程序设计已成功地解决许多难以解决的难题,然而,用于解释它的运行机制的理论却相当少。自John Holland在70年代中期提出了其著名的模式定理以来,模式定理就一直作为解释遗传算法GA(Genetic Algorithm)工作机制的理论基础。GA采用确定长度的染色体编码方案,GP通常是使用规模和形状能够动态变化的不确定分层计算机程序。二者操作的类似性和差异性促使沿着GA理论方法形成GP的理论,以     

Functional search in economics using genetic programming

This paper discusses economic applications of a recently developed artificial intelligence technique-Koza's genetic programming (GP). GP is an evolutionary search method related to genetic algorithms. In GP, populations of potential solutions consist of executable computer algorithms, rather than coded strings. The paper provides an overview of how GP works, and illustrates with two applications: solving for the policy function in a simple optimal growth model, and estimating an unusual regression function. Results suggest that the GP search method can be an interesting and effective tool for economists.     

动态系统的常微分方程组建模—基于不同搜索技术的实验研究

以人口模型和化学反应模型为例,通过大量实验研究比较了分别采用基于两种传统的搜索方法即局部搜索算法和模拟退火算法、遗传算法（简称GA）四者相结合的14种不同算法建立动态系统的常微分方程组模型的实验结果,得到了有关各算法性能比较的一些新的结论。两个实例的实验结果表明：在14种算法中,GP＋GA＋LS－MU算法（即在采用GP的模型结构的优化过程中嵌入采用GA的模型参数的优化过程,并且在每一演化代对种群中的部分个体进行基于GP的标准变异算子产生邻域解的局域搜索过程）是目前解决常微分方程组建模问题的最好算法。     

Developing Mobile Robot Wall-Following Algorithms Using Genetic Programming

This paper demonstrates the use of genetic programming (GP) for the development of mobile robot wall-following behaviors. Algorithms are developed for a simulated mobile robot that uses an array of range finders for navigation. Navigation algorithms are tested in a variety of differently shaped environments to encourage the development of robust solutions, and reduce the possibility of solutions based on memorization of a fixed set of movements. A brief introduction to GP is presented. A typical wall-following robot evolutionary cycle is analyzed, and results are presented. GP is shown to be capable of producing robust wall-following navigation algorithms that perform well in each of the test environments used.     

Probabilistic model building in genetic programming: a critical review

Probabilistic model-building algorithms (PMBA), a subset of evolutionary algorithms, have been successful in solving complex problems, in addition providing analytical information about the distribution of fit individuals. Most PMBA work has concentrated on the string representation used in typical genetic algorithms. A smaller body of work has aimed to apply the useful concepts of PMBA to genetic programming (GP), mostly concentrating on tree representation. Unfortunately, the latter research has been sporadically carried out, and reported in several different research streams, limiting substantial communication and discussion. In this paper, we aim to provide a critical review of previous applications of PMBA and related methods in GP research, to facilitate more vital communication. We illustrate the current state of research in applying PMBA to GP, noting important perspectives. We use these to categorise practical PMBA models for GP, and describe the main varieties on this basis.     

基于遗传编程的非线性系统辨识

为了实现对非线性系统的辨识,能够对目标系统的结构和参数进行同步辨识,将遗传编程(Genetic Programming,GP)作为辨识工具.使用基本遗传编程算法对非线性静态系统进行辨识-对电厂钢球磨煤机存煤量与产粉量之间的特性关系曲线进行辨识;使用一种改进的遗传编程算法对非线性动态系统进行辨识-对一个二阶离散非线性差分方程进行辨识.所有辨识都取得了满意的结果.遗传编程进化过程中,目标系统的结构与参数同时准确辨识,证明遗传编程非常适合于解决非线性系统辨识问题,并在算法上实现了结构辨识和参数辨识的统一.     

SGA、GP、GEP的研究概述

结合三种典型的遗传算法:SGA、GP、GEP。介绍了遗传算法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、同时介绍了三种算法的主要应用领域。     

Evolving Recursive Programs by Using Adaptive Grammar Based Genetic Programming

Genetic programming (GP) extends traditional genetic algorithms to automatically induce computer programs. GP has been applied in a wide range of applications such as software re-engineering, electrical circuits synthesis, knowledge engineering, and data mining. One of the most important and challenging research areas in GP is the investigation of ways to successfully evolve recursive programs. A recursive program is one that calls itself either directly or indirectly through other programs. Because recursions lead to compact and general programs and provide a mechanism for reusing program code, they facilitate GP to solve larger and more complicated problems. Nevertheless, it is commonly agreed that the recursive program learning problem is very difficult for GP. In this paper, we propose techniques to tackle the difficulties in learning recursive programs. The techniques are incorporated into an adaptive Grammar Based Genetic Programming system (adaptive GBGP). A number of experiments have been performed to demonstrate that the system improves the effectiveness and efficiency in evolving recursive programs. Communicated by: William B. Langdon An erratum to this article is available at .     

常微分方程组的演化建模

利用演化算法的自适应,自组织,自学习的特性,设计了遗传程序设计与遗传算法和相嵌套的常微分方程组混合演化建模算法,以遗传程序设计优化模型结构,以遗传算法优化模型参数,首次实现了常微分方程组建模过程自动化并可进行有效的预测。     

Bayesian Methods for Efficient Genetic Programming

A Bayesian framework for genetic programming (GP) is presented. This is motivated by the observation that genetic programming iteratively searches populations of fitter programs and thus the information gained in the previous generation can be used in the next generation. The Bayesian GP makes use of Bayes theorem to estimate the posterior distribution of programs from their prior distribution and likelihood for the fitness data observed. Offspring programs are then generated by sampling from the posterior distribution by genetic variation operators. We present two GP algorithms derived from the Bayesian GP framework. One is the genetic programming with the adaptive Occam's razor (AOR) designed to evolve parsimonious programs. The other is the genetic programming with incremental data inheritance (IDI) designed to accelerate evolution by active selection of fitness cases. A multiagent learning task is used to demonstrate the effectiveness of the presented methods. In a series of experiments, AOR reduced solution complexity by 20% and IDI doubled evolution speed, both without loss of solution accuracy.