Application of Multiobjective Genetic Programming to the Design of Robot Failure Recognition Systems

We present an evolutionary approach using multiobjective genetic programming (MOGP) to derive optimal feature extraction preprocessing stages for robot failure detection. This data-driven machine learning method is compared both with conventional (nonevolutionary) classifiers and a set of domain-dependent feature extraction methods. We conclude MOGP is an effective and practical design method for failure recognition systems with enhanced recognition accuracy over conventional classifiers, independent of domain knowledge.     

'Identifying the structure of nonlinear dynamic systems using multiobjective genetic programming

A method for identifying the structure of nonlinear polynomial dynamic models is presented. This approach uses an evolutionary algorithm, genetic programming, in a multiobjective fashion to generate global models which describe the dynamic behavior of the nonlinear system under investigation. The validation stage of system identification is simultaneously evaluated using the multiobjective tool, in order to direct the identification process to a set of global models of the system.     

Inference of differential equation models by genetic programming

This paper describes an evolutionary method for identifying a causal model from the observed time-series data. We use a system of ordinary differential equations (ODEs) as the causal model. This approach is known to be useful for practical applications, e.g., bioinformatics, chemical reaction models, control theory, etc. To explore the search space more effectively in the course of evolution, the right-hand sides of ODEs are inferred by genetic programming (GP) and the least mean square (LMS) method is used along with the ordinary GP. We apply our method to several target tasks and empirically show how successfully GP infers the systems of ODEs. We also describe an extension of the approach to the inference of differential equation systems with transcendental functions.     

Gaussian process approach for modelling of nonlinear systems

Parametric modelling principals such as neural networks, fuzzy models and multiple model techniques have been proposed for modelling of nonlinear systems. Research effort has focused on issues such as the selection of the structure, constructive learning techniques, computational issues, the curse of dimensionality, off-equilibrium behaviour, etc. To reduce these problems, the use of non-parametrical modelling approaches have been proposed. This paper introduces the Gaussian process (GP) prior approach for the modelling of nonlinear dynamic systems. The relationship between the GP model and the radial basis function neural network is explained. Issues such as selection of the dimension of the input space and the computation load are also discussed. The GP modelling technique is demonstrated on an example of the nonlinear hydraulic positioning system.     

二次演化建模在实时仿真中的应用

遗传程序设计在复杂系统的建模中表现出智能性，自适应性等特点，它可以找出能描述系统的静态和动态过程一系列相关联的函数或微分方程，为描述复杂系统提供了一种有效的手段，针对传统的遗传程序设计方法的搜索效率低，所建模型的精度不高等缺点，提出了一种新的演化建模算法：二次演化建模，该算法引入人工智能中的系统公告板来公布最好的树及其子树，从而加速了优化过程，使之达到实时仿真的目的，并将其应用于描述一个实时仿真系统－－轮机仿真系统，并结合仿真系统的特点来指导遗传算法，减少其搜索的盲目性，实验的结果表明无论在算法的求解速度还是模型的精度上二次演化建模算法均优于传统的遗传程序设计方法。     

A two-level hybrid evolutionary algorithm for modelingone-dimensional dynamic systems by higher-order ODE models

This paper presents a new algorithm for modeling one-dimensional (1-D) dynamic systems by higher-order ordinary differential equation (HODE) models instead of the ARMA models as used in traditional time series analysis. A two-level hybrid evolutionary modeling algorithm (THEMA) is used to approach the modeling problem of HODE's for dynamic systems. The main idea of this modeling algorithm is to embed a genetic algorithm (GA) into genetic programming (GP), where GP is employed to optimize the structure of a model (the upper level), while a GA is employed to optimize the parameters of the model (the lower level). In the GA, we use a novel crossover operator based on a nonconvex linear combination of multiple parents which works efficiently and quickly in parameter optimization tasks. Two practical examples of time series are used to demonstrate the THEMA's effectiveness and advantages.     

Detecting nonlinear interrelation patterns among process variables using genetic programming

Detecting non-linear interaction patterns among process variables is an important task for fault detection and propagation analysis. There are many statistical and evolutionary techniques being developed in the literature for prediction of interaction strengths but their accuracy is generally unsatisfactory. This study demonstrates an evolutionary programming approach to uncover non-linear relations among process variables. In this study, we make an attempt to use genetic programming (GP) based approach for this purpose. GP overcomes many shortcomings faced by other statistical or evolutionary techniques in this context. The effectiveness, feasibility, and robustness of the proposed method are demonstrated on simulated data emanating from a well-known Tennessee Eastman process. The proposed method has successfully achieved reasonable detection and prediction of non-linear interaction patterns among process variables.     

Using weighted genetic programming to program squat wall strengths and tune associated formulas

This study developed a weighted genetic programming (WGP) approach to study the squat wall strength. The proposed WGP evolves on genetic programming (GP), an evolutionary algorithm-based methodology that employs a binary tree topology and optimized functional operators. Weight coefficients were introduced to each GP linkage in the tree in order to create a new weighted genetic programming (WGP) approach. The proposed WGP offers two distinct advantages, including: (1) a balance of influences is struck between the two front input branches and (2) weights are incorporated throughout generated formulas. Resulting formulas contain a certain quantity of optimized functions and weights. Genetic algorithms are employed to accomplish WGP optimization of function selection and proper weighting tasks. Case studies herein focused on a reference study of squat wall strength. Results demonstrated that the proposed WGP provides accurate results and formula outputs. This paper further utilized WGP to tune referenced formulas, which yielded a final formula that combined the positive attributes of both WGP and analytical models.     

Domain-independent feature extraction for multi-classification using multi-objective genetic programming

We propose three model-free feature extraction approaches for solving the multiple class classification problem; we use multi-objective genetic programming (MOGP) to derive (near-)optimal feature extraction stages as a precursor to classification with a simple and fast-to-train classifier. Statistically-founded comparisons are made between our three proposed approaches and seven conventional classifiers over seven datasets from the UCI Machine Learning database. We also make comparisons with other reported evolutionary computation techniques. On almost all the benchmark datasets, the MOGP approaches give better or identical performance to the best of the conventional methods. Of our proposed MOGP-based algorithms, we conclude that hierarchical feature extraction performs best on multi-classification problems.     

Multicriteria distribution network planning using simulated annealing

The classical long-range distribution network planning problem involves deciding network investments to meet future demand at a minimum cost while meeting technical restrictions (thermal limits and maximum voltage drop). The decision whether to construct facilities and branches leads to a mixed integer programming problem with a large number of decision variables. The great deal of uncertainty associated with data that cannot be modeled using probabilistic methods leads to the use of fuzzy models to capture the uncertainty. In addition, several criteria must be taken into account that is resulting in the problem being fuzzy multiobjective. The combinatorial nature of the problem limits the use of traditional mathematical tools to limited size problems. This contribution presents a methodology that generates a sample of efficient solutions for the fuzzy multiobjective problem, based on a meta-heuristic, simulated annealing (SA). The results obtained with this approach are shown to be satisfactory compared to other methods under similar conditions.     

高阶常微分方程的演化建模用于时间序列的分析

本文提出采用高阶常微分方程模型代替传统的时序分析中所用的ＡＲＭＡ模型来实现一维动态系统的建模,并针对传统方法建模过程中所遇到的困难,设计了将遗传程序设计与遗传算法个嵌套的混合演化建模算法,以遗传程序设计优化模型结构,以遗传算法优化模型参数,首次成功地实现了动态系统的高阶微分方程建模过程自动化,对三个典型时间序列实例的实验结果表明：采用此算法可由计算机自动发现适合描述该动态系统的高阶常微分方程模型,     

Evolutionary constructive induction

Feature construction in classification is a preprocessing step in which one or more new attributes are constructed from the original attribute set, the object being to construct features that are more predictive than the original feature set. Genetic programming allows the construction of nonlinear combinations of the original features. We present a comprehensive analysis of genetic programming (GP) used for feature construction, in which four different fitness functions are used by the GP and four different classification techniques are subsequently used to build the classifier. Comparisons are made of the error rates and the size and complexity of the resulting trees. We also compare the overall performance of GP in feature construction with that of GP used directly to evolve a decision tree classifier, with the former proving to be a more effective use of the evolutionary paradigm.     

Solution of matrix Riccati differential equation for nonlinear singular system using genetic programming

In this paper, we propose a novel approach to find the solution of the matrix Riccati differential equation (MRDE) for nonlinear singular systems using genetic programming (GP). The goal is to provide optimal control with reduced calculation effort by comparing the solutions of the MRDE obtained from the well known traditional Runge Kutta (RK) method to those obtained from the GP method. We show that the GP approach to the problem is qualitatively better in terms of accuracy. Numerical examples are provided to illustrate the proposed method.      

Explanatory Analysis of the Metabolome Using Genetic Programming of Simple, Interpretable Rules

Genetic programming, in conjunction with advanced analytical instruments, is a novel tool for the investigation of complex biological systems at the whole-tissue level. In this study, samples from tomato fruit grown hydroponically under both high- and low-salt conditions were analysed using Fourier-transform infrared spectroscopy (FTIR), with the aim of identifying spectral and biochemical features linked to salinity in the growth environment. FTIR spectra of whole tissue extracts are not amenable to direct visual analysis, so numerical modelling methods were used to generate models capable of classifying the samples based on their spectral characteristics. Genetic programming (GP) provided models with a better prediction accuracy to the conventional data modelling methods used, whilst being much easier to interpret in terms of the variables used. Examination of the GP-derived models showed that there were a small number of spectral regions that were consistently being used. In particular, the spectral region containing absorbances potentially due to a cyanide/nitrile functional group was identified as discriminatory. The explanatory power of the GP models enabled a chemical interpretation of the biochemical differences to be proposed. The combination of FTIR and GP is therefore a powerful and novel analytical tool that, in this study, improves our understanding of the biochemistry of salt tolerance in tomato plants.     

Practical performance models of algorithms in evolutionary program induction and other domains

Evolutionary computation techniques have seen a considerable popularity as problem solving and optimisation tools in recent years. Theoreticians have developed a variety of both exact and approximate models for evolutionary program induction algorithms. However, these models are often criticised for being only applicable to simplistic problems or algorithms with unrealistic parameters. In this paper, we start rectifying this situation in relation to what matters the most to practitioners and users of program induction systems: performance. That is, we introduce a simple and practical model for the performance of program-induction algorithms. To test our approach, we consider two important classes of problems — symbolic regression and Boolean function induction — and we model different versions of genetic programming, gene expression programming and stochastic iterated hill climbing in program space. We illustrate the generality of our technique by also accurately modelling the performance of a training algorithm for artificial neural networks and two heuristics for the off-line bin packing problem.We show that our models, besides performing accurate predictions, can help in the analysis and comparison of different algorithms and/or algorithms with different parameters setting. We illustrate this via the automatic construction of a taxonomy for the stochastic program-induction algorithms considered in this study. The taxonomy reveals important features of these algorithms from the performance point of view, which are not detected by ordinary experimentation.     

经费分配中基于多目标优化的遗传规划模型

在传统遗传规划中引入多目标优化原理,探索新的经费分配方法和管理模式,建立了一种多目标优化的非线性遗传规划模型,提出了一种先进的基于正交试验的新型混合遗传算法来求解该问题.对求解过程中的选择算子、交叉算子和变异算子等进行正交试验,得到的种群个体明显优于基本遗传算法的个体.这种基于多目标优化的遗传规划模型能产生精度更高的最优解,通过对经费分配问题的实验验证,得到了较好的结果.     

A new multi-gene genetic programming approach to nonlinear system modeling. Part I: materials and structural engineering problems

This paper presents a new approach for behavioral modeling of structural engineering systems using a promising variant of genetic programming (GP), namely multi-gene genetic programming (MGGP). MGGP effectively combines the model structure selection ability of the standard GP with the parameter estimation power of classical regression to capture the nonlinear interactions. The capabilities of MGGP are illustrated by applying it to the formulation of various complex structural engineering problems. The problems analyzed herein include estimation of: (1) compressive strength of high-performance concrete (2) ultimate pure bending of steel circular tubes, (3) surface roughness in end-milling, and (4) failure modes of beams subjected to patch loads. The derived straightforward equations are linear combinations of nonlinear transformations of the predictor variables. The validity of MGGP is confirmed by applying the derived models to the parts of the experimental results that are not included in the analyses. The MGGP-based equations can reliably be employed for pre-design purposes. The results of MSGP are found to be more accurate than those of solutions presented in the literature. MGGP does not require simplifying assumptions in developing the models.     

Modeling and prediction for discharge lifetime of battery systems using hybrid evolutionary algorithms

A hybrid evolutionary modeling algorithm (HEMA) is proposed to build the discharge lifetime models with multiple impact factors for battery systems as well as make predictions. The main idea of the HEMA is to embed a genetic algorithm (GA) into genetic programming (GP), where GP is employed to optimize the structure of a model, while a GA is employed to optimize its parameters. The experimental results on lithium-ion batteries show that the HEMA works effectively, automatically and quickly in modeling the discharge lifetime of battery systems. The algorithm has some advantages compared with most existing modeling methods and can be applied widely to solving the automatic modeling problems in many fields.     

Predicting high-strength concrete parameters using weighted genetic programming

Genetic programming (GP) is an evolutionary algorithm-based methodology that employs a binary tree topology with optimized functional operators. This study introduced weight coefficients to each GP linkage in a tree in order to create a new weighted genetic programming (WGP) approach. Two distinct advantages of the proposed WGP include (1) balancing the influences of the two front input branches and (2) incorporating weights throughout generated formulas. Resulting formulas contain a certain quantity of optimized functions and weights. Genetic algorithms are employed to accomplish WGP optimization of function selection and proper weighting tasks. Case studies presented herein highlight a high-strength concrete reference study. Results showed that the proposed WGP not only improves GP in terms of introduced weight coefficients, but also provides both accurate results and formula outputs.     

Lyapunov-based stability analysis automated by genetic programming

This contribution describes an automatic technique to detect suitable Lyapunov functions for nonlinear systems. The theoretical basis for the work is Lyapunov’s Direct Method, which provides sufficient conditions for stability of equilibrium points. In our proposed approach, genetic programming (GP) is used to search for suitable Lyapunov functions, that is, those that best predict the true domain of attraction. In the work presented here, our GP approach has been extended by defining a target function accounting for the Lyapunov function level sets.