Genetic programming based activity recognition on a smartphone sensory data benchmark

Nonlinear model identification of an experimental ball-and-tube system using a genetic programming approach

Extracting discriminative and robust features from video sequences is the first and most critical step in human action recognition. In this paper, instead of using handcrafted features, we automatically learn spatio-temporal motion features for action recognition. This is achieved via an evolutionary method, i.e., genetic programming (GP), which evolves the motion feature descriptor on a population of primitive 3D operators (e.g., 3D-Gabor and wavelet). In this way, the scale and shift invariant features can be effectively extracted from both color and optical flow sequences. We intend to learn data adaptive descriptors for different datasets with multiple layers, which makes fully use of the knowledge to mimic the physical structure of the human visual cortex for action recognition and simultaneously reduce the GP searching space to effectively accelerate the convergence of optimal solutions. In our evolutionary architecture, the average cross-validation classification error, which is calculated by an support-vector-machine classifier on the training set, is adopted as the evaluation criterion for the GP fitness function. After the entire evolution procedure finishes, the best-so-far solution selected by GP is regarded as the (near-)optimal action descriptor obtained. The GP-evolving feature extraction method is evaluated on four popular action datasets, namely KTH, HMDB51, UCF YouTube, and Hollywood2. Experimental results show that our method significantly outperforms other types of features, either hand-designed or machine-learned.

Efficient truck dispatching is crucial for container port operations. Dynamic container port truck dispatching, a complex online optimization problem, poses significant challenges due to its uncertain and non-linear nature. This paper presents a novel neural network assisted genetic programming (NN-GP) approach, which combines the global search of genetic programming (GP) and the local search of recurrent neural network (RNN). In this framework, the RNN further refines GP individuals after genetic operations (crossovers and mutations), enhancing solution adaptability and precision in response to dynamic and uncertain scenarios. The proposed method leverages RNN's understanding of temporal dynamics and GP's robust exploration of the solution space, effectively addressing the dynamic container truck dispatching problem. Experiments using real-world container port data demonstrate that the RNN-GP model outperforms traditional heuristic methods and standalone GP algorithms, reducing dispatching time and increasing port efficiency. This research highlights the potential of hybridizing machine learning techniques with GP in solving complex real-world optimization problems.

This paper presents a novel nonparametric efficiency analysis technique based on the Genetic Programming (GP) in order to measure efficiency of Iran electric power plants. GP model was used to predict the output of power plants with respect to input data. The method, we presented here, is capable of finding a best performance among power plant based on the set of input data, GP predicted results and real outputs. The advantage of using GP over traditional statistical methods is that in prediction with GP, the researcher doesn’t need to assume the data characteristic of the dependent variable or output and the independent variable or input. In this proposed methodology to calculate the efficiency scores, a novel algorithm was introduced which worked on the basis of predicted and real output values. To validate our model, the results of proposed algorithm for calculating efficiency rank of power plants were compared to traditional method. Real data was presented for illustrative our proposed methodology. Results showed that by utilizing the capability of input-output pattern recognition of GP, this method provides more realistic results and outperform in identification of efficient units than the conventional methods.

SMS spam, also known as mobile spam, has become a prevalent and an ever growing issue due to the availability of bulk SMS services at nominal costs. These spam messages may not only be commercial but also pose a great deal of financial threats to the users. To fight against SMS spam, a variety of solutions have been proposed including content-based filtering, semantic indexing, machine learning classifiers, etc. However, in this regard evolutionary algorithms have not been utilized. Since the nature of SMS is contemporary, the representation of text messages keep evolving with the help of slangs, symbols, misspelled words, abbreviations and acronyms. Hence, such a solution is required which can accommodate these changes, also keeping the length of SMS in consideration. The model proposed in this paper generates regular expressions as individuals of population, using Genetic Programming Approach. These regular expressions so generated are used for the classification purpose. The application of Genetic Programming in the domain of SMS spam filtering has not been explored widely. It is able to eliminate False Positive errors, thus saving legitimate messages from being misclassified. The performance tends to improve with higher number of generations. Performance and confusion matrix for different number of generations are tabulated.

<p>Symbolic regression (SR) is a function identification process, the task of which is to identify and express the relationship between the input and output variables in mathematical models. SR is named to emphasise its ability to find the structure and coefficients of the model simultaneously. Genetic Programming (GP) is an attractive and powerful technique for SR, since it does not require any predefined model and has a flexible representation. However, GP based SR generally has a poor generalisation ability which degrades its reliability and hampers its applications to science and real-world modeling. Therefore, this thesis aims to develop new GP approaches to SR that evolve/learn models exhibiting good generalisation ability. This thesis develops a novel feature selection method in GP for high-dimensional SR. Feature selection can potentially contribute not only to improving the efficiency of learning algorithms but also to enhancing the generalisation ability. However, feature selection is seldom considered in GP for high-dimensional SR. The proposed new feature selection method utilises GP’s built-in feature selection ability and relies on permutation to detect the truly relevant features and discard irrelevant/noisy features. The results confirm the superiority of the proposed method over the other examined feature selection methods including random forests and decision trees on identifying the truly relevant features. Further analysis indicates that the models evolved by GP with the proposed feature selection method are more likely to contain only the truly relevant features and have better interpretability. To address the overfitting issue of GP when learning from a relatively small number of instances, this thesis proposes a new GP approach by incorporating structural risk minimisation (SRM), which is a framework to estimate the generalisation performance of models, into GP. The effectiveness of SRM highly depends on the accuracy of the Vapnik-Chervonenkis (VC) dimension measuring model complexity. This thesis significantly extends an experimental method (instead of theoretical estimation) to measure the VC-dimension of a mixture of linear and nonlinear regression models in GP for the first time. The experimental method has been conducted using uniform and non-uniform settings and provides reliable VC-dimension values. The results show that our methods have an impressively better generalisation gain and evolve more compact model, which have a much smaller behavioural difference from the target models than standard GP and GP with bootstrap, The proposed method using the optimised non-uniform setting further improves the one using the uniform setting. This thesis employs geometric semantic GP (GSGP) to tackle the unsatisfied generalisation performance of GP for SR when no overfitting occurs. It proposes three new angle-awareness driven geometric semantic operators (GSO) including selection, crossover and mutation to further explore the geometry of the semantic space to gain a greater generalisation improvement in GP for SR. The angle-awareness brings new geometric properties to these geometric operators, which are expected to provide a greater leverage for approximating the target semantics in each operation, and more importantly, to be resistant to overfitting. The results show that compared with two kinds of state-of-the-art GSOs, the proposed new GSOs not only drive the evolutionary process fitting the target semantics more efficiently but also significantly improve the generalisation performance. A further comparison on the evolved models shows that the new method generally produces simpler models with a much smaller size and containing important building blocks of the target models.</p>

This paper presents a new genetic programming (GP) approach to accurately classifying cognitive tasks from non-stationary and noisy fNIRS neural signals. To this end, a new GP that effectively handles multiclass problems is developed. In accordance with multi-tree structure, GP operators are innovated: crossover exchanges every subtree of parents without suffering from any incongruity problem and mutation fine-tunes candidate solutions by a less destructive process. Experimental results verifies the effectiveness of the proposed GP classifier over existing references.

Purpose – For measuring the effect of crop root content on soil water retention curves (SWRC), a simulation approach (multi-gene genetic programming (MGGP)), which develops the model structure and its coefficients automatically can be applied. However, it does not perform well due to two vital issues related to its generalization: inappropriate formulation procedure of the multi-gene model and the difficulty in model selection. The purpose of this paper is to propose a heuristic-based-MGGP (N-MGGP) to formulate the functional relationship between the water content and two input parameters (soil suction and volumetric crop root content). Design/methodology/approach – A new simulation approach (heuristic-based-MGGP (N-MGGP)), was proposed to formulate the functional relationship between the water content and two input parameters (soil suction and volumetric crop root content). The proposed approach makes use of a statistical approach of stepwise regression and classification methods (Bayes naïve and artificial neural network (ANN)) to tackle the two issues. Simulated data obtained from the models was evaluated against the experimental data. Findings – The performance of proposed approach was found to better than that of standardized MGGP. Sensitivity and parametric analysis conducted validates the robustness of model by unveiling dominant input parameters and hidden non-linear relationships. Originality/value – To the best of authors’ knowledge, an empirical model is developed that measures the effect of crop root content on the SWRCs. The authors also proposed a new genetic programming approach in simulating the crop root content dependent SWRCs.

Low-frequency oscillation damping in the electric network through the optimal design of UPFC coordinated PSS employing MGGP

The aim of this study was to develop three roundabout entry capacity models (RECMs) by employing evolutionary-based regression techniques such as genetic programming (GP), age-layered population structure genetic programming (ALPSGP) and grammatical evolution genetic programming (GEGP) in mixed traffic conditions. Necessary data were collected from 27 roundabouts located in eight states in India. The influence area for gap acceptance method was used to determine the critical gap. To assess the significance of the models and select the best, the modified rank index was applied. The results showed that the GEGP model performed better than the GP and ALPSGP models. The GEGP model is also applicable in practice because of its simplicity. Sensitivity analysis revealed that the critical gap is the prime variable in the development of RECMs. The findings of this study should be useful for traffic planners and designers in the capacity estimation of roundabouts in mixed traffic conditions in developing countries with traffic characteristics similar to those in India.

In this article, an evolutionary algorithm known as Genetic Programming (GP) was used to design a parametric speech quality estimation model. Nowadays, GP is one of the machine learning techniques employed in a quality estimation process. In principle, the set of quality-affecting parameters was used as an input to the designed estimation model based on GP approach in order to estimate a quality of synthesized speech transmitted over IP channel (VoIP environment). The performance results obtained by the designed estimation model have confirmed the good properties of genetic programming, namely good accuracy and generalization ability; this makes it to be perspective approach to a quality estimation of this type of speech in the corresponding environment. The developed model can be helpful for network operators and service providers implementing it in planning phase or early-development stage of telecommunication services based on synthesized speech.

On the use of the genetic programming for balanced load distribution in software-defined networks

Accurate predictions of sea-level are important for geodetic applications, navigation, coastal, industrial and tourist activities. In the current work, the Genetic Programming (GP) and artificial neural networks (ANNs) were applied to forecast half-daily and daily sea-level variations from 12 hours to 5 days ahead. The measurements at the Cocos (Keeling) Islands in the Indian Ocean were used for training and testing of the employed artificial intelligence techniques. A comparison was performed of the predictions from the GP model and the ANN simulations. Based on the comparison outcomes, it was found that the Genetic Programming approach can be successfully employed in forecasting of sea level variations.

Computer science will be radically transformed if ongoing efforts to build large-scale quantum computers eventually succeed and if the properties of these computers meet optimistic expectations. Nevertheless, computer scientists still lack a thorough understanding of the power of quantum computing, and it is not always clear how best to utilize the power that is understood. This dilemma exists because quantum algorithms are difficult to grasp and even more difficult to write. Despite large-scale international efforts, only a few important quantum algorithms are documented, leaving many essential questions about the potential of quantum algorithms unanswered. These unsolved problems are ideal challenges for the application of automatic programming technologies. Genetic programming techniques, in particular, have already produced several new quantum algorithms and it is reasonable to expect further discoveries in the future. These methods will help researchers to discover how additional practical problems can be solved using quantum computers, and they will also help to guide theoretical work on both the power and limits of quantum computing. This tutorial will provide an introduction to quantum computing and an introduction to the use of evolutionary computation for automatic quantum computer programming. No background in physics or in evolutionary computation will be assumed. While the primary focus of the tutorial will be on general concepts, specific results will also be presented, including human-competitive results produced by genetic programming. Follow-up material is available from the presenter's book, Automatic Quantum Computer Programming: A Genetic Programming Approach, published by Springer and Kluwer Academic Publishers.

Cloud Computing is the delivery of computing as a service, which is specifically involved with Storage of data, enabling ubiquitous, convenient access to shared resources that are provided to computers and other devices as a utility over a network. Storage, which is considered to be the key attribute, is hindered by the presence of redundant copies of data. Data Deduplication is a specialized technique for data compression and duplicate detection for eliminating duplicate copies of data to make storage utilization efficient. Cloud Service Providers currently employ Hashing technique so as to avoid the presence of redundant copies. Apparently, there are a few major pitfalls which can be vanquished through the employment of a Nature - Inspired, Genetic Programming Approach, for deduplication. Genetic Programming is a systematic, domain - independent programming model making use of the ideologies of biological evolution so as to handle a complicated problem. A Sequence Matching Algorithm and Levenshtein's Algorithm are used for Text Comparison and then Genetic Programming concepts are utilized to detect the closest match. The performance of these three algorithms and hashing technique are compared. Since bio-inspired concepts, systems and algorithms are found to be more efficient, a Nature-Inspired Approach for data deduplication in cloud storage is implemented.