基于GP病虫害预测预报中适应性函数的研究

适应性函数是遗传算法和遗传程序设计得以实现的复制及群体整体性的质量.在GP程序过程中在常见适应性函数的基础上加以优化和调整,最后通过实例验证了优化调整后的适应性函数在病虫害预测预报中的准确度有了一定的提高.     

Feature generation using genetic programming with comparative partner selection for diabetes classification

The ultimate aim of this research is to facilitate the diagnosis of diabetes, a rapidly increasing disease in the world. In this research a genetic programming (GP) based method has been used for diabetes classification. GP has been used to generate new features by making combinations of the existing diabetes features, without prior knowledge of the probability distribution. The proposed method has three stages: features selection is performed at the first stage using t-test, Kolmogorov–Smirnov test, Kullback–Leibler divergence test, F-score selection, and GP. The results of feature selection methods are used to prepare an ordered list of original features where features are arranged in decreasing order of importance. Different subsets of original features are prepared by adding features one by one in each subset using sequential forward selection method according to the ordered list. At the second stage, GP is used to generate new features from each subset of original diabetes features, by making non-linear combinations of the original features. A variation of GP called GP with comparative partner selection (GP-CPS), utilising the strengths and the weaknesses of GP generated features, has been used at the second stage. The performance of GP generated features for classification is tested using the k-nearest neighbor and support vector machine classifiers at the last stage. The results and their comparisons with other methods demonstrate that the proposed method exhibits superior performance over other recent methods.     

遗传程序设计分析股价移动平均及中长期走势*

将遗传程序设计应用到股票价格分析,在股票市场各种因素相互作用与影响很难厘清的情况下,只从个别因素（价格）入手,测试对单一因素预测所能达到的效果;提出了两种预测方法：对不同尺度的股票移动平均线进行预测和对股票价格数据进行平滑预处理之后所进行的中长期预测。通过遗传程序设计算法,寻找前几个时间单位的股票价格对本期股票价格影响的经验公式,以期反映价格变动的规律。计算机实验模拟表明,该方法对于平均线的预测和中长期预测有较好的效果。     

遗传规划中的基因内区研究

利用试验证实遗传规划的个体中存在基因内区、它们是冗余的表达式，附加在算法树上，使个体变得臃肿，但对实际输出结果没影响，它对进化的收敛既有积极作用，也有消极作用，通过对遗传操作方法和参数的研究，揭示也基因内区发生的规律，并提出扬长避短的途径。     

解非线性约束规划问题的新型多目标遗传算法

给出非线性约束规划问题的一种新解法。把带约束的非线性规划问题转化成为两个目标的多目标优化问题,并为转化后的多目标优化模型设计了一种新型多目标遗传算法,数据实验表明该算法对带约束的非线性规划问题求解是非常有效的。     

基因表达式编程的一种新遗传算子

遗传算子在演化算法中发挥重要的作用。设计了基因表达式编程中的一种新遗传算子（TIS）,它在基因尾部随机选择一个小片段,然后复制该片段（或该片段的倒置）并插入到基因尾部的任意位置,插入点后的符号依次向后挪动,超过尾部长度的编码被丢弃。实验表明,该算子在挖掘多元函数时能提高算法的成功率和收敛速度。     

遗传规划中遗传算子对种群多样性的影响

为了能有效地避免过早收敛并跳出局部最优,提出了一种改进的遗传规划算法来研究遗传算子（选择、交叉和变异）对种群多样性（主要是基因型和表现型）的影响。首先在基准问题（奇偶校验和符号回归中的四次多项式函数）中比较不同的遗传算子在离散和连续的适应度空间中的搜索寻优,然后使用斯皮尔曼相关系数来度量种群多样性与适应度的相关性。结果表明选择和交叉算子极大地减少了种群多样性,变异算子则能维持甚至提高种群多样性,这说明通过控制遗传算子来改变种群多样性从而找到最优个体是可行的。     

运用遗传规划解决异或问题的新方法*

介绍了一种用遗传规划这种新的搜索优化技术解决经典异或问题的新途径.遗传规划实质是使用广义的计算机程序来描述问题,并且可以根据环境状况动态改变计算机程序的结构.根据遗传规划特征,引入两种思路、三种方法对异或问题进行求解,取得了很好的效果.与神经网络相比,遗传规划可以动态进化学习并取得显式的数学表达式.     

Thalassaemia classification by neural networks and genetic programming

This paper presents the use of a neural network and a decision tree, which is evolved by genetic programming (GP), in thalassaemia classification. The aim is to differentiate between thalassaemic patients, persons with thalassaemia trait and normal subjects by inspecting characteristics of red blood cells, reticulocytes and platelets. A structured representation on genetic algorithms for non-linear function fitting or STROGANOFF is the chosen architecture for genetic programming implementation. For comparison, multilayer perceptrons are explored in classification via a neural network. The classification results indicate that the performance of the GP-based decision tree is approximately equal to that of the multilayer perceptron with one hidden layer. But the multilayer perceptron with two hidden layers, which is proven to have the most suitable architecture among networks with different number of hidden layers, outperforms the GP-based decision tree. Nonetheless, the structure of the decision tree reveals that some input features have no effects on the classification performance. The results confirm that the classification accuracy of the multilayer perceptron with two hidden layers can still be maintained after the removal of the redundant input features. Detailed analysis of the classification errors of the multilayer perceptron with two hidden layers, in which a reduced feature set is used as the network input, is also included. The analysis reveals that the classification ambiguity and misclassification among persons with minor thalassaemia trait and normal subjects is the main cause of classification errors. These results suggest that a combination of a multilayer perceptron with a blood cell analysis may give rise to a guideline/hint for further investigation of thalassaemia classification.     

遗传规划在符号回归中的应用

遗传规划(GP)是一种基于达尔文进化理论的数学规划方法。讨论了GP在符号回归中的应用。与传统的数据拟合方法相比,GP不必给出拟合函数的形式,同时,在初始群体足够大而且交叉和变异概率设置合理的情况下,不会陷入局部优化,具有更广泛的适用性。对于不给定函数形式的曲线拟合,GP可以自动得到曲线的函数形式及其参数大小,避免了传统方法的缺陷。通过具体的应用实例,说明了GP在测量数据处理中的应用。     

Feature extraction and dimensionality reduction by genetic programming based on the Fisher criterion

Abstract: Feature extraction helps to maximize the useful information within a feature vector, by reducing the dimensionality and making the classification effective and simple. In this paper, a novel feature extraction method is proposed: genetic programming (GP) is used to discover features, while the Fisher criterion is employed to assign fitness values. This produces non‐linear features for both two‐class and multiclass recognition, reflecting the discriminating information between classes. Compared with other GP‐based methods which need to generate c discriminant functions for solving c‐class (c>2) pattern recognition problems, only one single feature, obtained by a single GP run, appears to be highly satisfactory in this approach. The proposed method is experimentally compared with some non‐linear feature extraction methods, such as kernel generalized discriminant analysis and kernel principal component analysis. Results demonstrate the capability of the proposed approach to transform information from the high‐dimensional feature space into a single‐dimensional space by automatically discovering the relationships between data, producing improved performance.     

Parallel genetic programming: Synchronous and asynchronous migration

Genetic programming (GP) has been used successfully as a technique for constructing robot control programs. Depending on the number of evaluations and the cost of each evaluation however, GP may require a substantial amount of processing time to find a feasible solution. The advent of parallel GP has brought the execution time of GP to a more acceptable level. This paper investigates parallel GP with a mobile robot navigation problem. The parallel implementations are based on a coarse-grained model. A technique for distributing the task of serial GP is proposed. In particular, this technique shows that the total amount of work can be reduced while maintaining the quality of the solutions. Asynchronous and synchronous implementations are examined. We compare the performance in terms of both the solution quality and the execution time. The timing analysis is investigated to give an insight into the behavior of parallel implementations. The results show that the parallel algorithm with asynchronous migration using 10 processors is 33 times faster than the serial algorithm. This work was presented in part at the 5th International Symposium on Artificial Life and Robotics, Oita, Japan, January 26–28, 2000.     

遗传规划在精馏系统综合中的应用

提出采用遗传规划求解精馏系统综合问题,制定了一套独特的编码方案和求解策略,可以利用树状结构代码直观地表达各类精馏流程,而且算法能够灵活表达任意可行的塔间热集成结构。同时,结合分离工程领域知识指导代码生成和进化操作,以保证代码的有效性,从而明显提高了算法的进化速度。在不必给定精馏系统超结构的前提下,算法成功实现了可行域的自动搜索,从中快速求解出最优精馏结构。计算表明,该算法能够快速准确地求解大规模精馏系统最优综合问题,为工业生产中精馏流程的最佳设计和优化操作提供准确依据。     

基于遗传规划的服务匹配算法研究

将服务组合的问题视为一个Agent动作规划的问题，给出了一个基于动作规划的服务组合模型。进而结合遗传规划的可变分层结构，提出了一个适应于上述模型的服务匹配算法。仿真实验表明，在具有关联特征的服务选择空间中，该算法比其他算法具有更好的寻优能力和更快的速度。     

Hoare logic-based genetic programming

Almost all existing genetic programming systems deal with fitness evaluation solely by testing. In this paper, by contrast, we present an original approach that combines genetic programming with Hoare logic with the aid of model checking and finite state automata, henceby proposing a brand new verification-focused formal genetic programming system that makes it possible to evolve reliable programs with mathematically-verified properties.     

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.     

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。     

求解供应链伙伴选择的泛遗传差异演化算法

伙伴选择是供应链组建过程中的关键问题,也是一个难题,针对该问题,构造了融入特殊自然演化规则的泛遗传算法,并且与差异演化算法结合,得到了泛遗传差异演化算法,克服了差异演化算法局部收敛的缺陷,提高了其全局收敛的能力。实验表明,针对供应链伙伴选择问题,泛遗传差异演化算法优于传统的遗传算法和差异演化算法。     

非均匀演化的动态遗传算法

针对传统遗传算法的缺陷,提出一种基于基因位置分布差异而进行演化的改进算法.该算法利用2进制编码位置的差异性,在评价机制的基础上对种群进行动态的划分,并针对各个不同子种群的特点,使用动态的演化参数进行独立的演化操作,使得算法种群的构成类型能够保持多样性的发展,有效地抑制了“早熟”现象的发生.通过后续多个测试函数的对比实验结果表明,该算法在收敛速度、精度及稳定性上有所提高.该算法简单、易于实现、具有较强的通用性,是一种有效解决优化问题的方法.     

A note on the variance of rank-based selection strategies for genetic algorithms and genetic programming

This paper evaluates different forms of rank-based selection that are used with genetic algorithms and genetic programming. Many types of rank based selection have exactly the same expected value in terms of the sampling rate allocated to each member of the population. However, the variance associated with that sampling rate can vary depending on how selection is implemented. We examine two forms of tournament selection and compare these to linear rank-based selection using an explicit formula. Because selective pressure has a direct impact on population diversity, we also examine the interaction between selective pressure and different mutation strategies.