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Aiming at the problem of the recovery sequencing and scheduling of the carrier aircraft fleet landing, the carrier deck environment and the return recovery approach mode of the carrier aircraft are analyzed firstly, and the evaluation index model of the recovery sequencing and scheduling based on the weighted waiting time is established. According to the various constraints in the recovery sequencing and scheduling problem of the carrier aircraft, the model of the recovery sequencing and scheduling problem of the carrier aircraft considering the air refueling condition is established. Then, according to the established recycling sequencing and scheduling model and the idea of huper-heuristic algorithm, a genetic programming with mandatory landing rules (MGP) algorithm is designed to solve the ship necovery scheduling problem. Further, the effectiveness of the proposed recovery sequencing and scheduling model and MGP algorithm is verified by simulation examples, and the advantages of MGP algorithm are verified by comparison with genetic algorithm, pure heuristic algorithm and common genetic programming algorithm. Finally, based on the simulation results of an example, the influence of escape and wave off on the landing fleet recovery scheme is analyzed. [ABSTRACT FROM AUTHOR]

Different methods have been created to make optimization processes more efficient and effective, which is a big step forward in the area of evolutionary optimization. This abstract talks about four well-known methods: Neuroevolution of Augmenting Topologies (NEAT), Genetic Algorithms (GAs), Genetic Programming (GP), and Advanced Neuroevolutionary Genetic Algorithm (ANGA). It focuses on important performance indicators like Fitness Metrics, Generalization, Efficiency and Speed, and Overall Performance. With scores of 90% in Fitness Metrics and 88% in Generalization, NEAT, a neuroevolutionary program, shows strong success in competitive tasks. With an 80% score, it does poorly in Efficiency and Speed, though. GAs are known for using a population-based method. They do very well in Efficiency and Speed (90%), but they do a little worse in Fitness Metrics and Generalization (89% and 85%, respectively). With a focus on updated computer programs, GP gets marks that are equal in Fitness Metrics, Generalization, and Efficiency and Speed (88%, 85%, and 85%, respectively). The new ANGA algorithm stands out as a top worker, doing exceptionally well in all tests. ANGA gets great marks of 93% in Fitness Metrics, 94% in Generalization, and 93% in Efficiency and Speed. This shows how well it can optimize everything. Overall Performance score of 97.78% shows how well it works as a whole, making ANGA a potential method for genetic optimization. [ABSTRACT FROM AUTHOR]

The analytical evaluation of production system performance measures is a difficult task. Over the years, various methods have been developed to solve specific cases of very short production lines. However, formulae for estimating the mean production rate (throughput) are lacking. Recent developments in artificial intelligence simplify their use in the solution of symbolic regression problems. In this work, we use genetic programming (GP) to obtain approximate formulae for calculating the throughput of short reliable approximately balanced production lines, for which the processing times are exponentially distributed. A hybrid GP&GA scheme reduces the search space, in which GP uses genetic algorithms (GA) as a search engine. The scheme produces polynomial formulae for throughput estimation for the first time. To train the GP algorithm we use MARKOV, an accurate algorithm for calculating numerically the exact throughput of short exponential production lines. A few formulae, not previously reported in the literature, are presented. These formulae give close results to the exact results from the MARKOV algorithm, for short (up to five stations) reliable approximately balanced production lines without intermediate buffers. Also, the robustness of these formulae is satisfactory. In addition, the proposed hybrid GP&GA scheme is useful for design/production engineers to adjust the formulae to other ranges of the mean processing rates; the algorithms are quickly retrained to generate a new approximate formula. [ABSTRACT FROM AUTHOR]

In this study, in order to cope with the uncertain environments and space interference scenarios encountered in the production process for a class of manufacturing projects, we propose a novel manufacturing project scheduling problem with setup times under dynamic and interference environments (MPSPST-DIE), and design a novel multi-surrogate genetic programming hyper-heuristic (HH-MGP) algorithm to address it. Firstly, MPSPST-DIE is required to make decisions on the activity schedule, resource setup and space allocation. Therefore, we modify the traditional resource based policy class that only contains the activity schedule and simulate the entire scheduling process. Secondly, a new hyper-heuristic genetic programming algorithm is designed to automatically evolve activity rules, resource setup rules and space allocation rules simultaneously. Moreover, the multi-surrogate is devised to improve the performance of the basic genetic programming (GP) algorithm. In addition, a new evolutionary learning mechanism is embedded in the multi-surrogate. Different surrogates learn from each other to complement each other's strengths. Finally, numerical instances of the MPSPST-DIE are generated by configuring specific parameters of spatial resources and extensive numerical experiments are performed. At the same time, we implement the Taguchi experiment for the sensitivity analysis of parameters. The comparative analysis between the HH-MGP and traditional rules is performed. Further, the performance comparison between the multi-surrogate and other surrogates is also conducted. The experimental results show that the evolved rule of the HH-MGP performs better than the traditional rules for the MPSPST-DIE. The performance of the multi-surrogate model added to the GP algorithm are generally better than the single-surrogate model and no-surrogate. • A novel MPSP with setup times under dynamic and interference environment is proposed. • Traditional RB policy is modified and the entire scheduling process is simulated. • A new GP is designed to automatically evolve three types of rules simultaneously. • A multi-surrogate model is devised to improve the performance of GP. • A new evolutionary learning is embedded in multi-surrogate models. [ABSTRACT FROM AUTHOR]

In order to solve the problem in the application and optimization method of fractal art pattern generation, on the basis of the integration of the fractal theory and genetic algorithm, a generation method based on genetic algorithm and composition generation was proposed. The method provided a more stable and convenient supporting environment for the innovative design of artistic patterns, which met the needs of different users for the design of artistic patterns. The experimental results showed that the simplified genetic programming algorithm was optimized in both individual coding complexity and algorithm complexity. The search space was 1023-1026 times larger than the traditional algorithm, and the evaluation times were 2-3 times larger than the traditional algorithm. The results showed that the method could solve the problem that fitness function could not express well and ensure the optimal genetic population. [ABSTRACT FROM AUTHOR]

Featured Application: Our proposed application can be used to detect bot accounts on the social platform Twitter. Our models are based on evolutionary computation techniques such as genetic algorithms and genetic programming methods. These strategies enabled us to discover models with high interpretability of predictions and good generalization capabilities on unseen data as well. Bot accounts are automated software programs that act as legitimate human profiles on social networks. Identifying these kinds of accounts is a challenging problem due to the high variety and heterogeneity that bot accounts exhibit. In this work, we use genetic algorithms and genetic programming to discover interpretable classification models for Twitter bot detection with competitive qualitative performance, high scalability, and good generalization capabilities. Specifically, we use a genetic programming method with a set of primitives that involves simple mathematical operators. This enables us to discover a human-readable detection algorithm that exhibits a detection accuracy close to the top state-of-the-art methods on the TwiBot-20 dataset while providing predictions that can be interpreted, and whose uncertainty can be easily measured. To the best of our knowledge, this work is the first attempt at adopting evolutionary computation techniques for detecting bot profiles on social media platforms. [ABSTRACT FROM AUTHOR]

Multi-label classification has been used to solve a wide range of problems where each example in the dataset may be related either to one class (as in traditional classification problems) or to several class labels at the same time. Many ensemble-based approaches have been proposed in the literature, aiming to improve the performance of traditional multi-label classification algorithms. However, most of them do not consider the data characteristics to build the ensemble, and those that consider them need to tune many parameters to maximize their performance. In this paper, we propose an Auto-adaptive algorithm based on Grammar-Guided Genetic Programming to generate Ensembles of Multi-Label Classifiers based on projections of k labels (AG3P-kEMLC). It creates a tree-shaped ensemble, where each leaf is a multi-label classifier focused on a subset of k labels. Unlike other methods in the literature, our proposal can deal with different values of k in the same ensemble, instead of fixing one specific value. It also includes an auto-adaptive process to reduce the number of hyper-parameters to tune, prevent overfitting and reduce the runtime required to execute it. Three versions of the algorithm are proposed. The first, fixed , uses the same value of k for all multi-label classifiers in the ensemble. The remaining two deal with different k values in the ensemble: uniform gives the same probability to choose each available value of k , and gaussian favors the selection of smaller values of k. The experimental study carried out considering twenty reference datasets and five evaluation metrics, compared with eleven ensemble methods demonstrates that our proposal performs significantly better than the state-of-the-art methods. [Display omitted] • Genetic programming method to build tree-shaped ensembles of multi-label classifiers. • Each classifier focused on a subset of the labels of different size. • Auto-adaptability reduces the number of hyper-parameters and adapts to each problem. • The use of a variable size of subsets of labels outperforms the use of fixed size. • Our proposal outperforms state-of-the-art methods using 20 datasets and 5 metrics. [ABSTRACT FROM AUTHOR]

The paper discusses implementation of a hybrid algorithm for the optimization of equivalent circuit (EQC) parameters in the processing of electrochemical impedance data. Similar to other works, we use the genetic algorithm (GA) to find approximate values of parameters, which are further refined by a complex nonlinear least squares (CNLS) method, e. g. Levenberg‐Marquardt. This removes the important drawback of CNLS: the need to supply optimized starting values that are close to optimal ones. We propose the sequence of GA operations and values of GA parameters. In comparison to other works, our algorithm is designed to simplify connection of its GA part to existing CNLS procedures in electrochemical software packages. The algorithm was tested with datasets generated for both simple and complex EQCs, with and without added noise. No starting values were needed and the allowed ranges for EQC parameter variations were broader than found in usual experimental practice. For GA population as small as 300 individuals and the limit of only 500 GA cycles, more than 85 % of runs were successful, while in many cases the success rate was 100 %, also for smaller populations. [ABSTRACT FROM AUTHOR]

In today's world, the data generated by many applications are increasing drastically, and finding an optimal subset of features from the data has become a crucial task. The main objective of this review is to analyze and comprehend different stochastic local search algorithms to find an optimal feature subset. Simulated annealing, tabu search, genetic programming, genetic algorithm, particle swarm optimization, artificial bee colony, grey wolf optimization, and bat algorithm, which have been used in feature selection, are discussed. This review also highlights the filter and wrapper approaches for feature selection. Furthermore, this review highlights the main components of stochastic local search algorithms, categorizes these algorithms in accordance with the type, and discusses the promising research directions for such algorithms in future research of feature selection. [ABSTRACT FROM AUTHOR]

The main focus of this paper is on the family of evolutionary algorithms and their real-life applications. We present the following algorithms: genetic algorithms, genetic programming, differential evolution, evolution strategies, and evolutionary programming. Each technique is presented in the pseudo-code form, which can be used for its easy implementation in any programming language. We present the main properties of each algorithm described in this paper. We also show many state-of-the-art practical applications and modifications of the early evolutionary methods. The open research issues are indicated for the family of evolutionary algorithms. [ABSTRACT FROM AUTHOR]

In recent years, the rapid development of fresh food e-commerce in China has brought about more development opportunities for the cold chain logistics industry but has also presented new challenges. With the development of cloud computing and big data technology, it is increasingly important to study the application of big data and cloud computing technology in cold chain logistics. The purpose of this paper is to study the intelligent algorithm of cold chain logistics distribution optimization based on big data cloud computing analysis. Based on the constituent elements of the cold chain distribution problem and using cloud computing technology to obtain real-time traffic information in the transportation system through a unified access interface, this article analyses the distribution time and cost of refrigerated vehicles, thereby establishing a cold chain distribution vehicle path optimization model. By analysing the parallel programming mode of cloud computing, the parallel design and analysis of a coarse-grained genetic algorithm are used to solve the simulation model of the established optimization model. The experimental results show that the method of optimizing cold chain logistics vehicle routing using cloud computing is effective. When comparing 1, 2, 4, and 8 processors, the execution times are 19.89, 14.52, 8.12, and 6.41, respectively. It can be seen that the more processors there are, the shorter the calculation time. [ABSTRACT FROM AUTHOR]

In this paper, we propose an evolutionary learning approach for flexible identification of custom composite models for classification problems. To solve this problem in an efficient way, the problem-specific evolutionary operators are proposed and the effectiveness of different modifications of the common genetic programming algorithm is investigated. Also, several implementations of caching for the fitted models were compared from the performance point of view. To verify the proposed algorithm, both synthetic and real-world classification cases are examined. The implemented solution can identify the structure of the composite models from scratch, as well as be used as a part of automated machine learning solutions. [ABSTRACT FROM AUTHOR]

In real life, many problems are instances of combinatorial optimization. Cross-functional team selection is one of the typical issues. The decision-maker has to select solutions among ( k h ) solutions in the decision space, where k is the number of all candidates, and h is the number of members in the selected team. This paper is our continuing work since 2018; here, we introduce the completed version of the Min Distance to the Boundary model (MDSB) that allows access to both the "deep" and "wide" aspects of the selected team. The compromise programming approach enables decision-makers to ignore the parameters in the decision-making process. Instead, they point to the one scenario they expect. The aim of model construction focuses on finding the solution that matched the most to the expectation. We develop two algorithms: one is the genetic algorithm and another based on the philosophy of DC programming (DC) and its algorithm (DCA) to find the optimal solution. We also compared the introduced algorithms with the MIQP-CPLEX search algorithm to show their effectiveness. [ABSTRACT FROM AUTHOR]

The lack of adequately predictive atomistic empirical models precludes meaningful simulations for many materials systems. We describe advances in the development of a hybrid, population based optimization strategy intended for the automated development of material specific interatomic potentials. We compare two strategies for parallel genetic programming and show that the Hierarchical Fair Competition algorithm produces better results in terms of transferability, despite a lower training set accuracy. We evaluate the use of hybrid local search and several fitness models using system energies and/or particle forces. We demonstrate a drastic reduction in the computation time with the use of a correlation-based fitness statistic. We show that the problem difficulty increases with the number of atoms present in the systems used for model development and demonstrate that vectorization can help to address this issue. Finally, we show that with the use of this method, we are able to “rediscover” the exact model for simple known two- and three-body interatomic potentials using only the system energies and particle forces from the supplied atomic configurations. [ABSTRACT FROM AUTHOR]

Whereas the single-objective cell-formation problem has been studied extensively during the past decades, research on the multi-objective version of the problem has been relatively limited, despite the fact that it represents a more realistic modelling of the manufacturing environment. This article introduces multi-objective GP-SLCA, an evolutionary computation methodology for the solution of the multi-objective cell-formation problem. GP-SLCA is a hybrid algorithm, comprising of GP-SLCA, a genetic programming algorithm for the solution of single-objective cell-formation problems, and NSGA-II, a standard evolutionary multi-objective optimization technique. The proposed methodology is capable of providing the decision maker with a range of non-dominated solutions instead of a single compromise solution, which is usually produced as an outcome of alternative multi-objective optimization techniques. The application of multi-objective GP-SLCA is illustrated on a large-sized test problem taken from the literature. [ABSTRACT FROM AUTHOR]

This study proposes the inspection strategies of computational metrology for roundness measurement and roundness feasibility analysis with respect to ANSI Y14.5M-1994 standard. The roundness measurements are formulated as constrained non-linear programs, and optimization techniques are applied to locate the optimality. Through reformulating the geometric conditions and adding further constraints to the roundness measurement problems, the analysis of roundness inspection can be made by only investigating the feasibility of the constrained non-linear programs instead of advancing to the optimality. Two approaches of computational metrology based on genetic algorithms (GAs) are proposed to explore the optimality of roundness measurements and the roundness feasibility analysis, respectively. Finally, real data generated from machine visions and simulation data generated from analytical curves are used to test the proposed GA-based roundness assessment approaches. The experimental results indicate that the proposed algorithms are practical for on-line implementation to the roundness measurement and roundness feasibility analysis. Furthermore, the proposed computational strategy for the roundness feasibility analysis performs efficiently if the tolerance size is adequately specified and the process capability is stable. [ABSTRACT FROM AUTHOR]

Solar energy is a major type of renewable energy, and its estimation is important for decision-makers. This study introduces a new prediction model for solar radiation based on support vector regression (SVR) and the improved particle swarm optimization (IPSO) algorithm. The new version of algorithm attempts to enhance the global search ability for the PSO. In practice, the SVR method has a few parameters that should be determined through a trial-and-error procedure while developing the prediction model. This procedure usually leads to non-optimal choices for these parameters and, hence, poor prediction accuracy. Therefore, there is a need to integrate the SVR model with an optimization algorithm to achieve optimal choices for these parameters. Thus, the IPSO algorithm, as an optimizer is integrated with SVR to obtain optimal values for the SVR parameters. To examine the proposed model, two solar radiation stations, Adana, Antakya and Konya, in Turkey, are considered for this study. In addition, different models have been tested for this prediction, namely, the M5 tree model (M5T), genetic programming (GP), SVR integrated with four different optimization algorithms SVR-PSO, SVR-IPSO, Genetic Algorithm (SVR-GA), FireFly Algorithm (SVR-FFA) and the multivariate adaptive regression (MARS) model. The sensitivity analysis is performed to achieve the highest accuracy level of the prediction by choosing different input parameters. Several performance measuring indices have been considered to examine the efficiency of all the prediction methods. The results show that SVR-IPSO outperformed M5T and MARS. [ABSTRACT FROM AUTHOR]

In this manuscript, we identify and evaluate some of the most used optimization models for rule extraction using genetic programming-based algorithms. Six different models, which combine the most common fitness functions, were tested. These functions employ well-known metrics such as support, confidence, sensitivity, specificity, and accuracy. The models were then applied in the assessment of the performance of a single algorithm in several real classification problems. Results were compared using two different criteria: accuracy and sensitivity/specificity. This comparison, which was supported by statistical analysis, pointed out that the use of the product of sensitivity and specificity provides a more realistic estimation of classifier performance. It was also shown that the accuracy metric can make the classifier biased, especially in unbalanced databases. [ABSTRACT FROM AUTHOR]

Abstract A large number of optimization algorithms have been proposed. However, the no free lunch (NFL) theorems inform us that no algorithm can solve all types of optimization problems. An approach, which can suggest the most suitable algorithm for different types of problems, is valuable. In this paper, we propose an approach called sequential algorithm portfolio (SAP) which belongs to the inter-disciplinary fields of algorithm portfolio and algorithm selection. It uses a pre-trained predictor to predict the most suitable algorithm and a termination mechanism to automatically stop the optimization algorithms. The SAP is easy to implement and can incorporate any optimization algorithm. We experimentally compare SAP with two state-of-the-art algorithm portfolio approaches and single optimization algorithms. The result shows that SAP is a well-performing algorithm portfolio approach. [ABSTRACT FROM AUTHOR]

Abstract There are many kinds of VRP problems and there are many ways to solve them. In this paper, we use ant colony algorithm, genetic algorithm, and annealing algorithm to optimize the selection of the logistics path of H-group located at He Nan Province. It establishes an algorithm evaluation system, and analyzes the performance of the three algorithms in six dimensions from qualitative and quantitative perspectives. Through MATLAB data calculation, we found that the ant colony algorithm performs equally well on the solution quality, solution efficiency, code storage, robustness, adaptability, and optimality. [ABSTRACT FROM AUTHOR]

A hybrid algorithm that combines genetic programming (GP) and genetic algorithms (GAs) that deduce a closed-form correlation of building energy use is presented. Throughout the evolution, the terms, functions, and form of the correlation are evolved via the genetic program. Whenever the fitness of the best correlation stagnates for a specific number of GP generations, the GA optimizes the real-valued coefficients of each correlation in the population. When the GA, in turn, stagnates, correlations with optimized coefficients and powers are passed back to the GP for further search. The hybrid algorithm is applied to the problem of predicting energy use of a U-shape building. More than 800 buildings with various foot-print areas, relative compactness (RC), window-to-wall ratio (WWR), and projection factor (PF) values were simulated using the VisualDOE™ energy simulation engine. The algorithm tries to minimize the difference between simulated and predicted values by maximizing the R2 value. The algorithm was able to arrive at a closed-form correlation that combines the four building parameters, accurate to within 4%. The methodology can be easily used to model any type of data behavior in any engineering or nonengineering application. [ABSTRACT FROM AUTHOR]

In this paper, we compared the efficacy of observation based modeling approach using a genetic algorithm with the regular statistical analysis as an alternative methodology in plant research. Preliminary experimental data on in vitro rooting was taken for this study with an aim to understand the effect of charcoal and naphthalene acetic acid (NAA) on successful rooting and also to optimize the two variables for maximum result. Observation-based modelling, as well as traditional approach, could identify NAA as a critical factor in rooting of the plantlets under the experimental conditions employed. Symbolic regression analysis using the software deployed here optimised the treatments studied and was successful in identifying the complex non-linear interaction among the variables, with minimalistic preliminary data. The presence of charcoal in the culture medium has a significant impact on root generation by reducing basal callus mass formation. Such an approach is advantageous for establishing in vitro culture protocols as these models will have significant potential for saving time and expenditure in plant tissue culture laboratories, and it further reduces the need for specialised background. [ABSTRACT FROM AUTHOR]

Coriolis flowmeters are well established for the mass flow measurement of single-phase flow with high accuracy. In recent years, attempts have been made to apply Coriolis flowmeters to measure two-phase flow. This paper presents data driven models that are incorporated into Coriolis flowmeters to measure both the liquid mass flowrate and the gas volume fraction of a two-phase flow mixture. Experimental work was conducted on a purpose-built two-phase flow test rig on both horizontal and vertical pipelines for a liquid mass flowrate ranging from 700 to 14500 kg/h and a gas volume fraction between 0% and 30%. Artificial neural network (ANN), support vector machine (SVM), and genetic programming (GP) models are established through training with the experimental data. The performance of backpropagation-ANN (BP-ANN), radial basis function-ANN (RBF-ANN), SVM, and GP models is assessed and compared. Experimental results suggest that the SVM models are superior to the BP-ANN, RBF-ANN, and GP models for two-phase flow measurement in terms of robustness and accuracy. For liquid mass flowrate measurement with the SVM models, 93.49% of the experimental data yield a relative error less than ±1% on the horizontal pipeline, while 96.17% of the results are within ±1% on the vertical installation. The SVM models predict the gas volume fraction with a relative error less than ±10% for 93.10% and 94.25% of the test conditions on the horizontal and vertical installations, respectively. [ABSTRACT FROM PUBLISHER]

We describe and analyze algorithms for shape-constrained symbolic regression, which allow the inclusion of prior knowledge about the shape of the regression function. This is relevant in many areas of engineering — in particular, when data-driven models, which are based on data of measurements must exhibit certain properties (e.g. positivity, monotonicity, or convexity/concavity). To satisfy these properties, we have extended multi-objective algorithms with shape constraints. A soft-penalty approach is used to minimize both the constraint violations and the prediction error. We use the non-dominated sorting genetic algorithm (NSGA-II) as well as the multi-objective evolutionary algorithm based on decomposition (MOEA/D). The algorithms are tested on a set of models from physics textbooks and compared against previous results achieved with single objective algorithms. Further, we generated out-of-domain samples to test the extrapolation behavior using shape constraints and added a different level of noise on the training data to verify if shape constraints can still help maintain the prediction errors to a minimum and generate valid models. The results showed that the multi-objective algorithms were capable of finding mostly valid models, also when using a soft-penalty approach. Further, we investigated that NSGA-II achieved the best overall ranks on high noise instances. • Use shape constraints to enforce desired properties based on prior knowledge. • Minimizing errors and constraint violations with a multi-objective approach. • Extend shape-constrained symbolic regression with a soft-constraint approach. • Testing on high noise level and extrapolation instances. [ABSTRACT FROM AUTHOR]

Genetic-based composition algorithms are able to explore an immense space of possibilities, but the main difficulty has always been the implementation of the selection process. In this work, sets of melodies are utilized for training a machine learning approach to compute fitness, based on different metrics. The fitness of a candidate is provided by combining the metrics, but their values can range through different orders of magnitude and evolve in different ways, which makes it hard to combine these criteria. In order to solve this problem, amulti-objective fitness approachis proposed, in which the best individuals are those in the Pareto front of the multi-dimensional fitness space.Melodic treesare also proposed as a data structure for chromosomic representation of melodies and genetic operators are adapted to them. Some experiments have been carried out using a graphical interface prototype that allows one to explore the creative capabilities of the proposed system. An Online Supplement is provided and can be accessed athttp://dx.doi.org/10.1080/17459737.2016.1188171, where the reader can find some technical details, information about the data used, generated melodies, and additional information about the developed prototype and its performance. [ABSTRACT FROM PUBLISHER]

ABSTRACT The presence of sediments in a river is one of the major factors that characterize the river. The presence of sediment in any water resource is detrimental to its design purpose and it scratches any structure such as bridge foundations, conduit pipes, and turbine blades it comes into contact with while in motion and this leads to their eventual failure under load. The correct estimation of sediment yield transported by a river is indispensable in water resources engineering as sediment affects its hydraulic structure. The use of mathematical modeling algorithms such as genetic algorithms (GA) has proved to be very accurate in predicting sediment load in a river. The analogy behind GA is that genes in DNA functions are manipulated in specific ways through specific transcription operations. Therefore, applying the same logical operators to selected parameters relevant to sediment loads in rivers leads to mathematical prediction of the sediment load. This review article discusses the dynamic of sedimentation and analyses the use of GA as a hydrological model for accurately predicting sediment yield in a river, its potentials and shortcomings while recommending its modification. [ABSTRACT FROM AUTHOR]

A stacking sequence optimization method, which is conducted on the basis of a ground laminate and utilizes a two-level approximation as well as a genetic algorithm (GA), was developed before by the authors. Compared with general GAs, this method shows lower computational costs while reaching a high level of practical feasibility. However, the published work did not involve problems constrained with a strength requirement, which is essential for laminate structures subject to multiple loading conditions. Thus, in the present study, this approach is extended to implement strength constraints for laminate stacking sequence optimizations. First, to avoid the selection of some control parameters in the GA as well as to improve its performance, the standard genetic algorithm is modified with adaptive schemes in the fitness function and GA operators. Furthermore, by adopting the first-ply failure criterion and considering the stresses/strains for each layer in the ground laminate, the concept of temporal deletion techniques is proposed to extend this approach for handling optimization problems with strength constraints. Moreover, by combining the optimizer with the general finite element software MSC. Patran/Nastran, an optimization framework is established to conduct the optimization easily. Numerical examples are performed in repeated runs to illustrate the performance of the modified approaches in the GA as well as the feasibility and efficiency of this optimization system. [ABSTRACT FROM AUTHOR]

An approach based on an application of cellular automata (CA) to the problem of two-dimensional (2D) patterns or images reconstruction from ones with only partial information available is presented in the paper. 2D CA are used to process patterns/images, and genetic algorithm (GA) is applied to discover CA rules, which will be able to reconstruct original patterns/images from, e.g. destroyed or modified ones. A number of experiments have been conducted to reconstruct patterns and human face images with use of the proposed approach. Results of experiments show that CA rules discovered by GA in the learning process allow to reconstruct images with large number of damaged pixels. [ABSTRACT FROM AUTHOR]

Cost-efficient project management based on Critical Chain Method (CCPM) is investigated in this paper. This is a variant of the resource-constrained project scheduling problem (RCPSP) when resources are only partially available and a deadline is given, but the cost of the project should be minimized. RCPSP is a well- known NP-hard problem but originally it does not take into con- sideration the initial resource workload. A metaheuristic algorithm driven by a metric of a gain was adapted to solve the problem when applied to CCPM. Refinement methods enhancing the quality of the results are developed. The improvement expands the search space by inserting the task in place of an already allocated task, if a better allocation can be found for it. The increase of computation time is reduced by distributed calculations. The computational experiments showed significant efficiency of the approach, in comparison with the greedy methods and with genetic algorithm, as well as high reduction of time needed to obtain the results. [ABSTRACT FROM AUTHOR]

This study proposes a technique aimed at the automatic search for parameter adaptation strategies in a differential evolution algorithm with genetic programming symbolic regression. Genetic programming is applied to find the symbolic expression for scaling factor control during the optimization process of differential evolution based on the current computational resource, ratio of successful solutions and adapted scaling factor value. The design of the parameter adaptation technique is performed by a computational experiment, which consisted in solving several complex optimization problems. Better symbolic expressions are selected with regards to the Friedman ranking procedure, and the best solutions are additionally evaluated to compare them to the existing parameter adaptation techniques. The experimental results show that the automatically designed parameter adaptation techniques described by symbolic expressions are capable of outperforming existing parameter adaptation methods, while using different information sources. The analysis of automatically generated solutions shows that the proposed technique can be considered an automatic knowledge extraction method. This is due to the results showing that well-performing parameter adaptation can behave differently from state-of-the-art methods, thereby revealing previously unknown algorithm properties. [ABSTRACT FROM AUTHOR]