Modeling manufacturing processes using a genetic programming-based fuzzy regression with detection of outliers

Fuzzy regression (FR) been demonstrated as a promising technique for modeling manufacturing processes where availability of data is limited. FR can only yield linear type FR models which have a higher degree of fuzziness, but FR ignores higher order or interaction terms and the influence of outliers, all of which usually exist in the manufacturing process data. Genetic programming (GP), on the other hand, can be used to generate models with higher order and interaction terms but it cannot address the fuzziness of the manufacturing process data. In this paper, genetic programming-based fuzzy regression (GP-FR), which combines the advantages of the two approaches to overcome the deficiencies of the commonly used existing modeling methods, is proposed in order to model manufacturing processes. GP-FR uses GP to generate model structures based on tree representation which can represent interaction and higher order terms of models, and it uses an FR generator based on fuzzy regression to determine outliers in experimental data sets. It determines the contribution and fuzziness of each term in the model by using experimental data excluding the outliers. To evaluate the effectiveness of GP-FR in modeling manufacturing processes, it was used to model a non-linear system and an epoxy dispensing process. The results were compared with those based on two commonly used FR methods, Tanka’s FR and Peters’ FR. The prediction accuracy of the models developed based on GP-FR was shown to be better than that of models based on the other two FR methods.     

Genetic programming: principles and applications

Genetic algorithms (GA) has given rise to two new fields of research where (global) optimisation is of crucial importance: ‘genetic based machine learning’ (GBML) and ‘genetic programming’ (GP). An introduction by the authors to GA and GBML was given in two previous papers (Eng. Appl. Artif. Intell. 9(6) (1996) 681; Eng. Appl. Artif. Intell. 13(4) (2000) 381). In this paper, the last domain (GP) will be introduced, thereby making up a trilogy which gives a general overview of the whole field. In this third part, an overview will be given of the basic concepts of GP as defined by Koza. A first (educational) example of GP is given by solving a simple symbolic regression of a sinus function. Finally, a more complex application is presented in which GP is used to construct the mathematical equations for an industrial process. To this end, the case study ‘fibre-to-yarn production process’ is introduced. The goal of this study is the automatic development of mathematical equations for the prediction of spinnability and (possible) resulting yarn strength. It is shown that (relatively) simple equations can be obtained which describe accurately 90% of the fibre-to-yarn database.     

Customer relationship management in the hairdressing industry: An application of data mining techniques

With the increase of living standards and the sustainable changing patterns of people’s lives, nowadays, hairdressing services have been widely used by people. This paper adopts data mining techniques by combining self-organizing maps (SOM) and K-means methods to apply in RFM (recency, frequency, and monetary) model for a hair salon in Taiwan to segment customers and develop marketing strategies. The data mining techniques help identify four types of customers in this case, including loyal customers, potential customers, new customers and lost customers and develop unique marketing strategies for the four types of customers.     

遗传程序设计的精确模式理论进展

1.引言从传统意义上来讲,模式定理是用来解释遗传算法是怎样进化的。模式定理可被看成是遗传算法的宏观模型,这就意味着它可以根据当前代测得的宏观量(模式适应度、种群适应度、模式中个体数量等)来确定下一代种群的属性。这些与微观模型形成鲜明对比的宏观量隐含了大量遗传算法自由度信息,由它们可以推导出易于理解和研究的等式。对传统GP模式定理的争论焦点是它们仅提供了在下一代模式H实例数量期望值的下界E[m(H,t 1)],而不是一     

Epigenetic programming: Genetic programming incorporating epigenetic learning through modification of histones

We present the results of our work in simulating the recently discovered findings in molecular biology regarding the significant role which histones play in regulating the gene expression in eukaryotes. Extending the notion of inheritable genotype in evolutionary computation from the commonly considered model of DNA to chromatin (DNA and histones), we present epigenetic programming as an approach, incorporating an explicitly controlled gene expression through modification of histones in strongly-typed genetic programming (STGP). We propose a double cell representation of the simulated individuals, comprising somatic cell and germ cell, both represented by their respective chromatin structures. Following biologically plausible concepts, we regard the plastic phenotype of the somatic cell, achieved via controlled gene expression owing to modifications to histones (epigenetic learning, EL) as relevant for fitness evaluation, while the genotype of the germ cell corresponds to the phylogenesis of the individuals. The beneficial effect of EL on the performance characteristics of STGP is verified on evolution of social behavior of a team of predator agents in the predator-prey pursuit problem. Empirically obtained performance evaluation results indicate that EL contributes to about 2-fold improvement of computational effort of STGP. We trace the cause for that to the cumulative effect of polyphenism and epigenetic stability, both contributed by EL. The former allows for phenotypic diversity of genotypically similar individuals, while the latter robustly preserves the individuals from the destructive effects of crossover by silencing certain genotypic combinations and explicitly activating them only when they are most likely to be expressed in corresponding beneficial phenotypic traits.     

遗传编程的Matlab语言实现

引入堆栈技术,采用后缀表达式使遗传编程的树型结构易于转换为线性序列,并解决了初始个体生成算法、杂交算子操作、表达式个体求值等问题,使遗传编程不再依赖于专用编程语言和指针操作,能够方便地用Matlab语言实现。     

Genetic programming for computational pharmacokinetics in drug discovery and development

The success of a drug treatment is strongly correlated with the ability of a molecule to reach its target in the patient’s organism without inducing toxic effects. Moreover the reduction of cost and time associated with drug discovery and development is becoming a crucial requirement for pharmaceutical industry. Therefore computational methods allowing reliable predictions of newly synthesized compounds properties are of outmost relevance. In this paper we discuss the role of genetic programming in predictive pharmacokinetics, considering the estimation of adsorption, distribution, metabolism, excretion and toxicity processes (ADMET) that a drug undergoes into the patient’s organism. We compare genetic programming with other well known machine learning techniques according to their ability to predict oral bioavailability (%F), median oral lethal dose (LD50) and plasma-protein binding levels (%PPB). Since these parameters respectively characterize the percentage of initial drug dose that effectively reaches the systemic blood circulation, the harmful effects and the distribution into the organism of a drug, they are essential for the selection of potentially good molecules. Our results suggest that genetic programming is a valuable technique for predicting pharmacokinetics parameters, both from the point of view of the accuracy and of the generalization ability.

An axiomatic comparison of learned term-weighting schemes in information retrieval: clarifications and extensions

Machine learning approaches to information retrieval are becoming increasingly widespread. In this paper, we present term-weighting functions reported in the literature that were developed by four separate approaches using genetic programming. Recently, a number of axioms (constraints), from which all good term-weighting schemes should be deduced, have been developed and shown to be theoretically and empirically sound. We introduce a new axiom and empirically validate it by modifying the standard BM25 scheme. Furthermore, we analyse the BM25 scheme and the four learned schemes presented to determine if the schemes are consistent with the axioms. We find that one learned term-weighting approach is consistent with more axioms than any of the other schemes. An empirical evaluation of the schemes on various test collections and query lengths shows that the scheme that is consistent with more of the axioms outperforms the other schemes.     

Genetic programming based models for prediction of vapor-liquid equilibrium

The design, operation, and control of chemical separation processes heavily rely on the knowledge of the vapor-liquid equilibrium (VLE). Often, conducting experiments to gain an insight into the separation behavior becomes tedious and expensive. Thus, standard thermodynamic models are used in the VLE prediction. Sometimes, exclusively data-driven models are also used in VLE prediction although this method too possesses drawbacks such as a trial and error approach in specifying the data-fitting function. For overcoming these difficulties, this paper employs a machine learning (ML) formalism namely “genetic programming (GP)” possessing certain attractive features for the VLE prediction. Specifically, three case studies have been performed wherein GP-based models have been developed using experimental data, for predicting the vapor phase composition of a ternary, and a group of non–ideal binary systems. The inputs to models consists of three pure component attributes (acentric factor, critical temperature, and critical pressure), and as many intensive thermodynamic parameters (liquid phase composition, pressure, and temperature). A comparison of the VLE prediction and generalization performance of the GP-based models with the corresponding standard thermodynamic models reveals that the former class of models possess either superior or closely comparable performance vis-a-vis thermodynamic models. Noteworthy features of this study are: (i) a single GP-based model can predict VLE of a group of binary systems, and (ii) applicability of a GP-based model trained on an alcohol-acetate series data for its higher homolog. The VLE modeling approach exemplified here can be gainfully extended to other ternary and non-ideal binary systems, and for designing corresponding experiments in different pressure and temperature ranges.     

遗传程序设计理论及其应用综述

遗传程序设计是上世纪90年代基于遗传算法思想发展起来的具有概率搜索本质和结构优化等特征的演化算法新分支。该文对其研究现状及进展做了介绍,并简要综述了其理论与技术,同时对近年来的应用做了总结、分类与归纳,最后对其应用前景进行了展望。     

沙尘衰减对微波通信的影响

通过分析沙尘尺寸分布,在计算干沙和纯水的复介电常数的基础上,利用粒子散射理论给出了微波传输中的沙尘衰减的计算模型,该模型分析了爆炸沙尘、车扬沙尘和自然沙尘三种不同的沙尘的衰减。通过数值计算的结果,仿真了不同含水量下沙尘衰减率随微波频率的变化,最后给出了相关结论。     

选择策略对遗传归纳逻辑程序设计收敛性能的影响

采用遗传算法（GA）作为归纳逻辑程序设计（ILP）的搜索策略，可以提高ILP方法的鲁棒性和适应性，文章简要叙述了对作者提出的遗传归纳逻辑程序设计（GILP）算法作的改进，测试了选择策略对GILP算法收敛性能的影响，采用不同的选择策略不会影响算法的最终收敛结果，但会产生不同的选择压力，导致算法具有不同的收敛速率。     

An Evaluation of EvolutionaryGeneralisation in Genetic Programming

Generalisation is one of the most important performance evaluationcriteria for artificial learning systems. An increasing amount ofresearch has recently concentrated on the robustness or generalisationability of the programs evolved using Genetic Programming (GP). Whilesome of these researchers report on the brittleness of the solutionsevolved, some others propose methods of promotingrobustness/generalisation. In this paper, a review of research ongeneralisation in GP and problems with brittleness of solutions producedby GP is presented. Also, a brief overview of several new methodspromoting robustness/generalisation of the solutions produced by GP arepresented.     

Learning with Genetic Algorithms: An Overview

Genetic algorithms represent a class of adaptive search techniques that have been intensively studied in recent years. Much of the interest in genetic algorithms is due to the fact that they provide a set of efficient domain-independent search heuristics which are a significant improvement over traditional weak methods without the need for incorporating highly domain-specific knowledge. There is now considerable evidence that genetic algorithms are useful for global function optimization and NP-hard problems. Recently, there has been a good deal of interest in using genetic algorithms for machine learning problems. This paper provides a brief overview of how one might use genetic algorithms as a key element in learning systems.     

Genetic programming for medical classification: a program simplification approach

This paper describes a genetic programming (GP) approach to medical data classification problems. In this approach, the evolved genetic programs are simplified online during the evolutionary process using algebraic simplification rules, algebraic equivalence and prime techniques. The new simplification GP approach is examined and compared to the standard GP approach on two medical data classification problems. The results suggest that the new simplification GP approach can not only be more efficient with slightly better classification performance than the basic GP system on these problems, but also significantly reduce the sizes of evolved programs. Comparison with other methods including decision trees, naive Bayes, nearest neighbour, nearest centroid, and neural networks suggests that the new GP approach achieved superior results to almost all of these methods on these problems. The evolved genetic programs are also easier to interpret than the “hidden patterns” discovered by the other methods.

GP-COACH: Genetic Programming-based learning of COmpact and ACcurate fuzzy rule-based classification systems for High-dimensional problems

In this paper we propose GP-COACH, a Genetic Programming-based method for the learning of COmpact and ACcurate fuzzy rule-based classification systems for High-dimensional problems. GP-COACH learns disjunctive normal form rules (generated by means of a context-free grammar) coded as one rule per tree. The population constitutes the rule base, so it is a genetic cooperative-competitive learning approach. GP-COACH uses a token competition mechanism to maintain the diversity of the population and this obliges the rules to compete and cooperate among themselves and allows the obtaining of a compact set of fuzzy rules. The results obtained have been validated by the use of non-parametric statistical tests, showing a good performance in terms of accuracy and interpretability.     

An analysis of diffuse light attenuation in the northern Gulf of Mexico hypoxic zone using the SeaWiFS satellite data record

The Sea-viewing Wide Field-of-View Sensor (SeaWiFS) derived diffuse light attenuation along the Louisiana continental shelf (LCS) was examined at monthly scales from 1998 to 2007 to characterize temporal and spatial patterns, and responsible physical forcing conditions. The SeaWiFS diffuse light attenuation ranged from 0.10 to 2.64 m^− 1. Stepwise multiple linear regression analysis suggested that spatial and temporal patterns in diffuse light attenuation were influenced by wind speed, nutrient loading, and river discharge from the Mississippi and Atchafalaya River Basin. SeaWiFS daily integrated surface photosynthetically active radiation (PAR, 400-700 nm) and diffuse light attenuation were used to calculate the absolute PAR and percentage of surface PAR that reached the sediment water interface (SWI) on the LCS. Large portions of the LCS were euphotic to the SWI especially during April and May. This finding implied that significant primary production was possible beneath the pycnocline during spring and early summer. In addition, this study was the first to demonstrate that the euphotic depth was correlated to the depth at which the water column turned hypoxic on the LCS. The development of hypoxic waters may be influenced by decreased light availability below the pycnocline in addition to aforementioned physical forcing.     

Co-evolutionary multi-population genetic programming for classification in software defect prediction: An empirical case study

Evolving diverse ensembles using genetic programming has recently been proposed for classification problems with unbalanced data. Population diversity is crucial for evolving effective algorithms. Multilevel selection strategies that involve additional colonization and migration operations have shown better performance in some applications. Therefore, in this paper, we are interested in analysing the performance of evolving diverse ensembles using genetic programming for software defect prediction with unbalanced data by using different selection strategies. We use colonization and migration operators along with three ensemble selection strategies for the multi-objective evolutionary algorithm. We compare the performance of the operators for software defect prediction datasets with varying levels of data imbalance. Moreover, to generalize the results, gain a broader view and understand the underlying effects, we replicated the same experiments on UCI datasets, which are often used in the evolutionary computing community. The use of multilevel selection strategies provides reliable results with relatively fast convergence speeds and outperforms the other evolutionary algorithms that are often used in this research area and investigated in this paper. This paper also presented a promising ensemble strategy based on a simple convex hull approach and at the same time it raised the question whether ensemble strategy based on the whole population should also be investigated.     

Genetic algorithms for feature selection of image analysis-based quality monitoring model: An application to an iron mine

Measuring the quality parameters of materials at mines is difficult and a costly job. In this paper, an image analysis-based method is proposed efficiently and cost effectively that determines the quality parameters of material. The image features are extracted from the samples collected from a mine and modeled using neural networks against the actual grade values of the samples generated by chemical analysis. The dimensions of the image features are reduced by applying the genetic algorithm. The results showed that only 39 features out of 189 features are sufficient to model the quality parameter. The model was tested with the testing data set and the result revealed that the estimated grade values are in good agreement with the real grade values (R²=0.77). The developed method was then applied to a case study mine of iron ore. The case study results show that proposed image-based algorithm can be a good alternative for estimating quality parameters of materials at a mine site. The effectiveness of the proposed method was verified by applying it on a limestone deposit and the results revealed that the method performed equally well for the limestone deposit.     

Genetic programming incorporating biased mutation for evolution and adaptation of Snakebot

In this work we propose an approach for incorporating learning probabilistic context-sensitive grammar (LPCSG) in genetic programming (GP), employed for evolution and adaptation of locomotion gaits of a simulated snake-like robot (Snakebot). Our approach is derived from the original context-free grammar which usually expresses the syntax of genetic programs in canonical GP. Empirically obtained results verify that employing LPCSG contributes to the improvement of computational effort of both (i) the evolution of the fastest possible locomotion gaits for various fitness conditions and (ii) adaptation of these locomotion gaits to challenging environment and degraded mechanical abilities of the Snakebot.