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Recently, learning classifier systems (LCS) have been used for a variety of Internet of Things (IoT) devices and multi-cloud services, including cloud-based centralized control of physical robots and actuators in continuous-time environments. Performance analysis of sensors, navigation of humanoid robots and intelligent control of rescue systems. In these systems, we can run evolutionary or intuitive algorithms on cloud servers to search the space of rules and simultaneously other learning processes to assign how to interact with the environment to the rules in the classification. Also, the problem of continuous congestion, traffic reduction, network routing and predicting traffic conditions in wireless networks is the main challenge facing these systems in real environments. Usually such systems are non-Markovian. Therefore, they need memory to save system states. This paper presents a framework for XCS-based memory-based LCS. In addition to identifying optimal rules in overlapping modes, the XCS architecture is equipped with a memory. Memory stores the most efficient classifier rules. These rules reduce the steps to reach the goal. In the first proposed method, only those rules that affect the moving motion are kept in memory. As the number of rules increases, some of them are deleted according to memory space. In the second proposed method, some features are added to the rules of this memory and the performance of the memory is optimized using evolutionary algorithms, which are used to remove the less used rules. The relative success of the proposed LCS architecture in solving well-known maze problems compared to the conventional XCS architecture confirms its efficiency in increasing the number of successes and reducing the steps to reach the goals. The results of this research are suggested as a suitable solution for reducing routing time, reducing network load in the problem of congestion and traffic in solving problems related to wireless networks. [ABSTRACT FROM AUTHOR]

Abstract Benchmarking plays an important role in the development of novel search algorithms as well as for the assessment and comparison of contemporary algorithmic ideas. This paper presents common principles that need to be taken into account when considering benchmarking problems for constrained optimization. Current benchmark environments for testing Evolutionary Algorithms are reviewed in the light of these principles. Along with this line, the reader is provided with an overview of the available problem domains in the field of constrained benchmarking. Hence, the review supports algorithms developers with information about the merits and demerits of the available frameworks. [ABSTRACT FROM AUTHOR]

Abstract The Multi-Level Selection Genetic Algorithm (MLSGA) is shown to increase the performance of a simple Genetic Algorithm. It is unique among evolutionary algorithms as its sub-populations use separate selection and reproduction mechanisms to generate offspring sub-populations, called collectives in this approach, to increase the selection pressure, and uses a split in the fitness function to maintain the diversity of the search. Currently how these novel mechanisms interact with different reproduction mechanisms, except for the one originally tested at the individual level is not known. This paper therefore creates three different variants of MLSGA and explores their behaviour, to see if the diversity and selection pressure benefits are retained with more complex individual selection mechanisms. These hybrid methods are tested using the CEC′09 competition, as it is the widest current benchmark of bi-objective problems, which is updated to reflect the current state-of-the-art. Guidance is given on the new mechanisms that are required to link MLSGA with the different individual level mechanisms and the hyperparameter tuning which results in optimal performance. The results show that the hybrid approach increases the performance of the proposed algorithms across all the problems except for MOEA/D on unconstrained problems. This shows the generality of the mechanisms across a range of Genetic Algorithms, which leads to a performance increase from the MLSGA collective level mechanism and split in the fitness function. It is shown that the collective level mechanism changes the behaviour from the methods selected at the individual level, promoting diversity first instead of convergence, and focuses the search on different regions, making it a particularly strong choice for problems with discontinuous Pareto fronts. This results in the best general solver for the updated bi-objective CEC′09 problem sets. [ABSTRACT FROM AUTHOR]

Abstract Designing reasonable departure schedule is the key step to realize the urban transit priority. It can not only reduce the operating cost of bus company, but also guarantee convenience for passengers. This paper estimates the travel time between bus stations based on the historical trajectory data of the bus, and then combines the number of passengers get on and off at each station to optimize the departure timetable. In addition, several constraints including actual travel time, limited capacity and arrival time distribution type are considered in the optimization models to effectively and comprehensively estimate the passenger waiting time. Finally, a hybrid algorithm combining Genetic Algorithm (GA) and Simulated Annealing Algorithm (SAA) is proposed to search optimal solution in scheduling model. A case study is applied to testify the effectiveness of proposed models. In the experiments, we compare optimization results of proposed method to traditional genetic algorithms, and the results show the superiority and feasibility of the hybrid optimization approach. Highlights • We apply Poisson distribution to determine the passengers waiting time. • An optimization model considering actual travel time, limited capacity and arrival time is proposed. • We propose a hybrid algorithm to generate departure timetable. • The results prove the superiority and feasibility of the proposed optimization method. • We discuss the impact of vehicle resource constraints on the scheduling scheme. [ABSTRACT FROM AUTHOR]

Two cylinders arranged symmetrically on a frame have become a major form of steering mechanism for articulated off-road vehicles (AORVs). However, the differences of stroke and arm lead to pressure fluctuation, vibration noise, and a waste of torque. In this paper, the differences of stroke and arm are reduced based on a genetic algorithm (GA). First, the mathematical model of the steering mechanism is put forward. Then, the difference of stroke and arm are optimized using a GA. Finally, a FW50GLwheel loader is used as an example to demonstrate the proposed GA-based optimization method, and its effectiveness is verified by means of automatic dynamic analysis of mechanical systems (ADAMS). The stroke difference of the steering hydraulic cylinders was reduced by 92% and the arm difference reached a decrease of 78% through GA optimization, in comparison with unoptimized structures. The simulation result shows that the steering mechanism optimized by GA behaved better than by previous methods. [ABSTRACT FROM AUTHOR]

Robots have recently gained a great attention due to their potential to work in dynamic and complex environments with obstacles, which make searching for an optimum path on-the-fly an open challenge. To address this problem, this paper proposes a Genetic Algorithm (GA) based path planning method to work in a dynamic environment called GADPP. The proposed method uses Bezier Curve to refine the final path according to the control points identified by our GADPP. To update the path during its movement, the robot receives a signal from a Base Station (BS) based on the alerts that are periodically triggered by sensors. Compared to the state-of-the-art methods, GADPP improves the performance of robot based applications in terms of the path length, the smoothness of the path, and the required time to get the optimum path. The improvement ratio regarding the path length is between 6% and 48%. While the path smoothness is improved in the range of 8% and 52%. In addition, GADPP reduces the required time to get the optimum path by 6% up to 47%. [ABSTRACT FROM AUTHOR]

This paper proposes a new multi-population based global optimization algorithm called Parallel Three Parent Genetic Algorithm (P3PGA). The proposed approach is an extension of 3PGA approach. Performance of the proposed algorithm was evaluated on the eleven CEC-2014 benchmark functions. We compared the performance of proposed P3PGA algorithm with 16 other algorithms. It was observed that out of the 11 benchmarks functions P3PGA outperformed all other 16 approaches on 5 benchmark functions. Out of 5, for 2 functions the best performance of P3PGA was also equaled by few other approaches. For the other 3 functions the performance of P3PGA was unmatched by any of the other 16 algorithms. Further, this paper proposes a new P3PGA based optimal route evaluation approach for routing in Wireless Mesh Networks. The proposed approach was implemented in MATLAB and simulated for various WMN sizes and scenarios. We compared its performance with 8 other approaches namely, Ad-hoc On Demand Distance Vector (AODV) approach, Dynamic Source Routing (DSR), Genetic Algorithm (GA), Biogeography Based Optimization (BBO), Fire fly Algorithm (FA), Ant Colony Optimization (ACO), BAT and Big Bang-Big Crunch (BB-BC) based optimal cost route evaluation approaches. The P3PGA based approach outperformed all other 8 approaches for the WMNs sized 1000 nodes and above. [ABSTRACT FROM AUTHOR]

Pattern classification is a central problem in machine learning, with a wide array of applications, and rule-based classifiers are one of the most prominent approaches. Among these classifiers, Incremental Rule Learning algorithms combine the advantages of classic Pittsburg and Michigan approaches, while, on the other hand, classifiers using fuzzy membership functions often result in systems with fewer rules and better generalization ability. To discover an optimal set of rules, learning classifier systems have always relied on bio-inspired models, mainly genetic algorithms. In this paper we propose a classification algorithm based on an efficient bio-inspired approach, Artificial Immune Networks. The proposed algorithm encodes the patterns as antigens, and evolves a set of antibodies, representing fuzzy classification rules of ellipsoidal surface, to cover the problem space. The innate immune mechanisms of affinity maturation and diversity preservation are modified and adapted to the classification context, resulting in a classifier that combines the advantages of both incremental rule learning and fuzzy classifier systems. The algorithm is compared to a number of state-of-the-art rule-based classifiers, as well as Support Vector Machines (SVM), producing very satisfying results, particularly in problems with large number of attributes and classes. [ABSTRACT FROM AUTHOR]

We present an alternative approach for training feed-forward neural networks (abbrev. NN) by means of a genetic algorithm (abbrev. GA) that alters the network's hidden weights and biases. We, by no means,out-rule the back-propagation training algorithm, but instead use it to train the evolved NNs for a much smaller number of generations and focus more on the mutation and crossover operators and how they can be applied. The basic principle involved is that each and every NN can be treated as a chromosome for a GA and, as a consequence, is subject to the mutation and crossover operators. A notable advantage of our approach is that we not only avoid over-fitting the NN, but are also able to alter the number of hidden neurons that make up the hidden layer of the NN, effectively removing the need for the user to specify them explicitly. We manage to outperform plain-vanilla NNs by a factor of 1 to 10 percent on well known data sets. At first this may not seem significant, but it becomes crucial when dealing with applications where accuracy is critical and training time is not an issue (such as disease diagnosis). [ABSTRACT FROM AUTHOR]

In current decades, various Evolutionary Algorithms(EAs) raise as well as many kinds of benchmarks are popular in evaluations of EAs׳ performances. Since there exists randomness in EAs׳ performances, the evaluations are made by a large number of runs in simulations or experiments in order to present a relatively fair comparison. However, there still exit several problems that have not been well explained. Does it make sense to deem two algorithms have equal ability if they have same final results? Is it convinced to decide winners or losers in comparisons just by tiny difference in performances? Besides the final results, how to compare algorithms׳ performances during the optimization iterations? In this paper, a neural network classifier based on extreme learning machine (ELM) is proposed to solve these problems. A novel role of classifier is first proposed to convince the differences between algorithms. If the classifier succeeds to classify algorithms based on their performances recorded in all generations, we deem the two algorithms have so convinced difference that comparisons of two algorithms can reflect algorithms׳ disparity. Therefore, the conclusions to judge the two algorithms are feasible and acceptable. Otherwise, if classifiers cannot distinguish two algorithms, we deem the two have similar performances so that it is meaningless to differ two algorithms just by tiny differences. By employing a set of classical benchmarks and six EAs, the simulations and computations are conducted. According to the analysis results, the proposed classifier can provide more information to reflect true abilities of algorithms, which is a novel view to compare EAs. [ABSTRACT FROM AUTHOR]

Application of multiobjective optimization in sewerage rehabilitation management is not widespread due to the limitation of data collection and complex optimization process. Thus, a few researches in literature focused on sewerage rehabilitation optimization, and only considered two-objective optimization usually between the service life and the direct cost instead of a social cost. A sewerage rehabilitation multiobjective optimization decision support system (SRMOS) was developed for sewerage rehabilitation management in this study. The nondominated sorting genetic algorithm-II was used to design a set of Pareto surfaces with desirable rehabilitation effectiveness at the lowest cost by providing optimal plans comprising a construction method and substitute material. The SRMOS was applied to a real sewerage system to provide tradeoff solutions for three conflicting objectives, which are minimizing rehabilitation cost, maximizing pipe service, and minimizing traffic disruption. Compared with the experts' manual estimation, the plan derived from the SRMOS enables saving nearly 20 % of the rehabilitation cost. The contours of the rehabilitation cost show the equivalent relation between the traffic disruption and service life of pipes. The results indicate that increasing the number of objectives can make up the drawback of cost hard to be quantified and can also facilitate deriving practical plans for reference in decision-making. [ABSTRACT FROM AUTHOR]

Container handling problems at container terminals are considered as NP-hard combinatorial optimization problems. In this paper, we propose an optimization methodology for solving container handling problems using genetic algorithm. The proposed methodology is applied on a real case study data of container terminal at Port-said Port in Egypt. The computational results show the effectiveness of the proposed methodology for container terminal where 56% reduction in ship service time (loading/unloading) in port is achieved. [ABSTRACT FROM AUTHOR]

In this manuscript, we proposed an intelligent hybrid pattern recognition system to classify eight hand motions. Two evolutionary algorithms: genetic algorithm (GA) and particle swarm optimization (PSO) were combined with support vector machine (SVM) to construct a novel model (GAPSO–SVM). The proposed hybrid system able to obtain a high performance in handling learning task with SVM by selecting the optimal parameters of radial basis function (RBF–SVM) and choosing the optimal decomposition level of the mother wavelet function. In this study, energy of wavelet coefficients (EWC) were extracted from surface electromyographic (sEMG) signals, which collected from forearm's muscles. Moreover, due to the reason that the effectiveness of myoelectric pattern recognition system also depends on the degree of dimensionality reduction of the selected feature vector (FV), we investigated the performance of different feature projection algorithms: neighborhood preserving embedding (NPE), principle component analysis (PCA), independent components analysis (ICA) and orthogonal linear discriminant analysis (OLDA). The result shows that the proposed hybrid model predicts with greater accuracy and reliability in comparison to other three classifiers: support vector machines (SVM), fuzzy least squares support vector machines (LS–SVM) and K-nearest neighbor (K-NN). The highest achieved average accuracy rate was 98.7% by employing SVM-GAPSO classifier based on PCA. [ABSTRACT FROM AUTHOR]

This paper presents a study of aircraft featuring truss-braced wing configurations that have been optimized for minimum fuel consumption using multidisciplinary design optimization. The investigation proceeds following an earlier Boeing Subsonic Ultra Green Aircraft Research N + 3 study, which selected the truss-braced wing concept as the most promising of several N + 3 concept vehicles. This comes from the fact that a significantly higher-aspect-ratio wing could achieve substantial reductions in induced drag but requires major structural changes to support such a large span. This problem was explored through the use of a multidisciplinary design and analysis environment implemented in ModelCenter®. Optimization was performed using ModelCenter's Design Explorer and Darwin genetic algorithm optimizers. Using the multidisciplinary environment, a large multidimensional design space, featuring design variables spread across the major aircraft design disciplines, was explored. Configurations featuring a strut-braced wing and truss-braced wings with one and two juries were evaluated for different span limits. Subsequently, different laminar wing design options were investigated in conjunction with lift augmentation system options. The multidisciplinary optimization used for this investigation was able to produce candidate designs with the desired attributes and performance improvements while satisfying all relevant geometric and performance constraints. [ABSTRACT FROM AUTHOR]

The application of a repetitive control mechanism for use in a mechanical control system has been a topic of investigation. The fundamental purpose of repetitive control is to eliminate disturbances in a mechanical control system. This paper presents two different repetitive control laws using individual types of basis function feedback and their combinations. These laws adjust the command given to a feedback control system to eliminate tracking errors, generally resulting from periodic disturbance. Periodic errors can be reduced through linear basis functions using regression and a genetic algorithm. The results illustrate that repetitive control is most effective method for eliminating disturbances. When the data are stabilized, the tracking error of the obtained convergence value, 10 −14 , is the optimal solution, verifying that the proposed regression and genetic algorithm can satisfactorily reduce periodic errors. [ABSTRACT FROM AUTHOR]

Thermoelectric modules (TEM) are solid state components which are able to convert thermal to electric energy without moving parts and therefore are generally recognized as durable and reliable. However, the performance of TEM is strongly depending on the temperature difference driving the heat flux and causing thermo-mechanical stress within the module. This study introduces a multi-objective optimization procedure based on a genetic algorithm with which this conflict in objectives can be solved under realistic boundary conditions. Therefore a finite element model is presented and an optimization model is derived. The results of the optimization show that a significant improvement in electric power output and mechanical stability can be achieved by regarding all relevant design parameters in the module. [ABSTRACT FROM AUTHOR]

This paper presents a computational method for eliminating severe-stress concentration at the unsupported railhead ends in rail joints through innovative shape optimization of the contact zone, which is complex due to near field nonlinear contact. With a view to minimizing the computational efforts, a hybrid genetic algorithm (GA) method coupled with parametric finite element has been developed and compared with the traditional GA. The shape of railhead top surface where the wheel contacts nonlinearly was optimized using the hybridized GA method. Comparative study of the optimal result and the search efficiency between the traditional and hybrid GA methods has shown that the hybridized GA provides the optimal shape in fewer computational cycles without losing accuracy. The method will be beneficial to solving complex engineering problems involving contact nonlinearity. [ABSTRACT FROM AUTHOR]

As there are various risks of failure in its execution, a composite web service (CWS) requires a transactional mechanism to guarantee its reliable execution. Though the existing service selection methods have considered that its transactional properties may affect its quality of service (QoS) such as its execution time, some of these methods can just give the locally optimal transactional CWS while others can give globally optimal CWS only under a given fixed transactional workflow. This paper addresses the issue of selecting and composing web services via a genetic algorithm (GA) and gives a transaction and QoS-aware selection approach. First, it introduces transactional properties of a single web service and CWS and the transactional rules used to compose them. Next, it conducts the performance analysis of basic workflow patterns such as sequential, parallel, selectable, and loop patterns and develops an algorithm to compute the execution time of a complex CWS. Then, it presents a GA-based approach, which takes into account the execution time, price, transactional property, stability, and penalty-factor, to achieve globally optimal service selection. Finally, this paper reports experimental results that compare the proposed approach with the exhaustive search algorithm, transactional-QoS-driven selection algorithm, and transactional service selection algorithm. The experimental results show that the proposed algorithm is efficient and effective and can give a globally optimal transactional CWS. [ABSTRACT FROM PUBLISHER]

The combination of classifiers is an active research area of the machine learning and pattern recognition communities. Many theoretical and empirical studies have been published demonstrating the advantages of the paradigm of combination of classifiers over the individual classifiers. When combining classifiers it is important to guarantee the diversity among them [16]. Some statistical measures can be used to estimate how diverse the ensembles of classifiers are, they are called diversity measures. On the other hand the number of individual classifiers are very big and just a litter group of classifiers can be generated a large number of combinations, so emerge the idea to use one of the meta-heuristics: the genetic algorithms (gas). Genetic algorithms play a significant role as search technique for handling complex spaces in many fields. They are based on the underlying genetic process in biological organisms and on the natural evolution principles of populations. These algorithms process a population of chromosomes, which represent search space solutions, with three operations: selection, crossover and mutation. In this paper some diversity measures are presented and one modulation of genetic algorithm using diversity measures is enunciated and implemented in order to obtain, from all possible combinations of a large number of base classifiers, a combination that ensures greater diversity among the chosen classifiers and the best accuracy in multi classifier system. We also present and discuss the results of applying the implemented system in two fields of application. Finally, the general conclusions are exposed. [ABSTRACT FROM AUTHOR]

Rainfall as a semi-random hydrological event is difficult to forecast due to some very complicated and unforeseen physical factors and their chaotic behavior. Artificial neural networks (ANN), which perform a nonlinear mapping between inputs and outputs, have played a crucial role in rainfall forecasting. In this paper, some feature selection approaches have been implemented to simulate the regional scale rainfall field in order to address a few deficiencies of ANN, such as selection of informative features of input data encountered in hydrological processes. The main simulator is a multi-layer perceptron neural network optimized by simple genetic algorithm (GA) to determine optimal input vectors in order to compare with other statistical approaches. Current rainfall from a limited number of neighboring stations is shown to be valuable to forecast current rainfall of certain target stations in the province of Fars in Iran for 30 min leading time. Among the studied features selection approaches such as chi-squared, linear correlation coefficient and mutual information (Ml), the results by Ml have considerable competency with regard to computational efficiency using the optimized scenario by GA. [ABSTRACT FROM AUTHOR]

Background: The development of next-generation sequencing technologies (NGS) has made the use of wholegenome sequence data for routine genetic evaluations possible, which has triggered a considerable interest in animal and plant breeding fields. Here, we investigated whether complete or partial sequence data can improve upon existing SNP (single nucleotide polymorphism) array-based selection strategies by simulation using a mixed coalescence - gene-dropping approach. Results: We simulated 20 or 100 causal mutations (quantitative trait nucleotides, QTN) within 65 predefined 'gene' regions, each 10 kb long, within a genome composed of ten 3-Mb chromosomes. We compared prediction accuracy by cross-validation using a medium-density chip (7.5 k SNPs), a high-density (HD, 17 k) and sequence data (335 k). Genetic evaluation was based on a GBLUP method. The simulations showed: (1) a law of diminishing returns with increasing number of SNPs; (2) a modest effect of SNP ascertainment bias in arrays; (3) a small advantage of using whole-genome sequence data vs. HD arrays i.e. ∼4%; (4) a minor effect of NGS errors except when imputation error rates are high (≥20%); and (5) if QTN were known, prediction accuracy approached 1. Since this is obviously unrealistic, we explored milder assumptions. We showed that, if all SNPs within causal genes were included in the prediction model, accuracy could also dramatically increase by ∼40%. However, this criterion was highly sensitive to either misspecification (including wrong genes) or to the use of an incomplete gene list; in these cases, accuracy fell rapidly towards that reached when all SNPs from sequence data were blindly included in the model. Conclusions: Our study shows that, unless an accurate prior estimate on the functionality of SNPs can be included in the predictor, there is a law of diminishing returns with increasing SNP density. As a result, use of whole-genome sequence data may not result in a highly increased selection response over high-density genotyping. [ABSTRACT FROM AUTHOR]

In this article, we imagine a breeding scenario with a population of individuals that have been genotyped but not phenotyped. We derived a computationally efficient statistic that uses this genetic information to measure the reliability of genomic estimated breeding values (GEBV) for a given set of individuals (test set) based on a training set of individuals. We used this reliability measure with a genetic algorithm scheme to find an optimized training set from a larger set of candidate individuals. This subset was phenotyped to create the training set that was used in a genomic selection model to estimate GEBV in the test set. Our results show that, compared to a random sample of the same size, the use of a set of individuals selected by our method improved accuracies. We implemented the proposed training selection methodology on four sets of data on Arabidopsis, wheat, rice and maize. This dynamic model building process that takes genotypes of the individuals in the test sample into account while selecting the training individuals improves the performance of genomic selection models. [ABSTRACT FROM AUTHOR]

To maintain geological consistency, it is necessary to carry out the history matching process integrated to the geostatistical modeling. However, this integration leads to a complex optimization problem because the relationship between the input and output variables can be highly nonlinear. The purpose of this paper is to present a framework to integrate the history matching of production and seismic-derived dynamic data through a genetic algorithm with adaptive bounds. A new procedure is proposed to reduce the range of the parameters during the optimization process. The methodology was applied to a synthetic reservoir model with structural and petrophysical properties similar to a real reservoir and the results showed that it is possible to apply genetic algorithm in the integration of history matching and geostatistical modeling with feasible computational effort in terms of number of flow simulations. [ABSTRACT FROM AUTHOR]

High reliability and low power consumption are among the major requirements in design of wireless sensor networks (WSNs). In this paper, a multi-objective problem is formulated as a Joint Power consumption and data Reliability (JPR) optimization problem. For this purpose, a connected dominating set (CDS)-based topology control approach is proposed. Our objective is to self-organize the network with minimum interference and power consumption. We consider the power changes into a topology with minimum CDS infrastructure subject to connectivity constraints. Since this problem is NP-hard, it cannot be dealt with using polynomial-time exact algorithms. Therefore, we first present a genetic algorithm taking into consideration problem-specific goals and constraints in an approximated manner called JPR Genetic Algorithm ( Jpr- ga). Secondly, a Hierarchical Sub-Chromosome Genetic Algorithm ( Hsc- ga) is proposed to obtain more accurate and faster solutions in the large and dense networks. We evaluate these algorithm over different networks topologies to analyse their efficiency. Comparing Jpr- ga and Hsc- ga with two different scenarios reveal that the proposed algorithms can efficiently balance power consumption and data communication reliability of sensor nodes and also prolong the network lifetime in WSNs. [ABSTRACT FROM AUTHOR]

In this paper, we address an inspection policy problem for a one-shot system with two types of units, namely, Type 1 units that fail at random times and Type 2 units that degrade with time. Interval availability and life cycle cost are used as optimization criteria and estimated by simulation. We determine inspection intervals, preventive replacement ages of Type 1 units, and preventive maintenance thresholds of Type 2 units that have minimal life cycle cost and satisfy the target interval availability during inspection periods. A simulation-based optimization procedure using a hybrid genetic algorithm is proposed to find near-optimal solutions. Numerical examples are studied to investigate the effects of model parameters on optimal solutions and compare the hybrid genetic algorithm with the general genetic algorithm. [ABSTRACT FROM AUTHOR]

SUMMARY Building an appropriate mathematical model that describes the system behaviour with a certain degree of satisfaction is quite challenging owing to the uncertain and volatile nature of thermodynamic constants and geometric parameters. In this paper, we present a technique to approximate and validate the dynamic behaviour of the Aström-Bell boiler-turbine power plant based on an RBFNN over a large operating range. The proposed RBFNN is applied to solve the parametric identification problem for nonlinear and complex systems using an optimiser based on a hybrid genetic algorithm. This optimiser is composed of the gradient descent optimiser and a genetic algorithm for fast convergence. Two simulations were performed to show the effectiveness of the proposed technique under different situations with several boiler-turbine input variables. The optimal structure and parameters of the obtained RBFNN-based model emulates well the dynamic behaviour of the Aström-Bell boiler-turbine system. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Mathematical modeling has become an integral part of synthesizing gene regulatory networks. One of the common problems is the determination of parameters, which are a part of the model description. In the present work, we propose a customized genetic algorithm as a method to determine the parameters such that the underlying oscillatory system exhibits the target behavior. We propose a problem specific, adaptive fitness function evaluation and a method to quantify the effect of a single parameter on the system response. The properties of the algorithm are highlighted and confirmed on two test cases of synthetic biological oscillators. [ABSTRACT FROM AUTHOR]

The problem of improving the voltage profile and reducing power loss in electrical networks must be solved in an optimal manner. This paper deals with comparative study of Genetic Algorithm (GA) and Differential Evolution (DE) based algorithm for the optimal allocation of multiple FACTS (Flexible AC Transmission System) devices in an interconnected power system for the economic operation as well as to enhance loadability of lines. Proper placement of FACTS devices like Static VAr Compensator (SVC), Thyristor Controlled Switched Capacitor (TCSC) and controlling reactive generations of the generators and transformer tap settings simultaneously improves the system performance greatly using the proposed approach. These GA & DE based methods are applied on standard IEEE 30 bus system. The system is reactively loaded starting from base to 200% of base load. FACTS devices are installed in the different locations of the power system and system performance is observed with and without FACTS devices. First, the locations, where the FACTS devices to be placed is determined by calculating active and reactive power flows in the lines. GA and DE based algorithm is then applied to find the amount of magnitudes of the FACTS devices. Finally the comparison between these two techniques for the placement of FACTS devices are presented. [ABSTRACT FROM AUTHOR]

Membrane computing is an important and newly-emerging branch of natural computing. As the basic type of membrane computing mod-els, a cell-like P system consists of three basic elements: membrane structure, initial objects and evolution rules. The automatic design of a ccll-like P system in the existing literature considered a simplified case, i.e., the selection of evolution rules from a predefined set of redundant evolution rules on the condition that the membrane structure and initial objects were fixed. How to automatically design a cell-like P system by simultaneously considering its three basic elements is a challenging and ongoing issue. In this paper, wc propose an automatic design approach for a cell-like P system, by tuning its three key elements, membrane structures, initial objects and evolution rules, simultaneously and in an interrelated manner. In this method, a binary encoding technique is used to codify the P system with variable membrane structures, initial objects and evolution rules; an elitist genetic algorithm is applied to evolve a population of P systems toward a successful P system for fulfilling a specific task; an effective fitness function is employed to evaluate each candidate P system by using a P-lingua simulator. The parameter setting is also discussed. Experimental results show that the introduced method can successfully accomplish the automatic design of a cell-like P system for calculating the square of 4. [ABSTRACT FROM AUTHOR]

Abstract: The purpose of this paper is to describe and evaluate a new algorithm for optimization. The new algorithm is named the Genetic Flock Algorithm. This algorithm is a type of hybrid of a Genetic Algorithm and a Particle Swarm Optimization Algorithm. The paper discusses strengths and weaknesses of these two algorithms. It then explains how the Genetic Flock Algorithm combines features of both and gives details of the algorithm. All three algorithms are compared using eight standard optimization problems that are used in the literature. It is shown that the Genetic Flock Algorithm provides superior performance on 75% of the tested cases. In the remaining 25% of the cases it outperforms either the Genetic Algorithm or the Particle Swarm Optimization Algorithm; it is never worse than both. Possible future improvements to the Genetic Flock Algorithm are briefly described. [Copyright &y& Elsevier]

In this work we present an extensive empirical analysis of the BioHEL genetics-based machine learning system using the k-Disjunctive Normal Form (k-DNF) family of boolean functions. These functions present a broad set of possible challenges for most machine learning techniques, such as different degrees of specificity, class imbalance and niche overlap. Moreover, as the ideal solutions are known, it is possible to assess if a learning system is able to find them, and how fast. Specifically, we study two aspects of BioHEL: its sensitivity to the coverage breakpoint parameter (that determines the degree of generality pressure applied by the fitness function) and the impact of the default rule policy. The results show that BioHEL is highly sensitive to the choice of coverage breakpoint and that using a default class suitable for the problem allows the system to learn faster than using other default class policies (e.g. the majority class policy). Moreover, the experiments indicate that BioHEL's scalability depends directly on both k (the specificity of the k-DNF terms) and the number of terms in the problem. In the last part of the paper we discuss alternative policies to adjust the coverage breakpoint parameter. [ABSTRACT FROM AUTHOR]

Evolutionary learning techniques are comparable in accuracy with other learning methods such as Bayesian Learning, SVM, etc. These techniques often produce more interpretable knowledge than, e.g. SVM; however, efficiency is a significant drawback. This paper presents a new representation motivated by our observations that Bioinformatics and Systems Biology often give rise to very large-scale datasets that are noisy, ambiguous and usually described by a large number of attributes. The crucial observation is that, in the most successful rules obtained for such datasets, only a few key attributes (from the large number of available ones) are expressed in a rule, hence automatically discovering these few key attributes and only keeping track of them contributes to a substantial speed up by avoiding useless match operations with irrelevant attributes. Thus, in effective terms this procedure is performing a fine-grained feature selection at a rule-wise level, as the key attributes may be different for each learned rule. The representation we propose has been tested within the BioHEL machine learning system, and the experiments performed show that not only the representation has competent learning performance, but that it also manages to reduce considerably the system run-time. That is, the proposed representation is up to 2–3 times faster than state-of-the-art evolutionary learning representations designed specifically for efficiency purposes. [ABSTRACT FROM AUTHOR]

Abstract: This work developed and demonstrated a machine learning approach for robust ATR. The primary innovation of this work was the development of an automated way of developing inference rules that can draw on multiple models and multiple feature types to make robust ATR decisions. The key realization is that this “meta learning” problem is one of structural learning, and that it can be conducted independently of parameter learning associated with each model and feature based technique. This was accomplished by using a learning classifier system, which is based on genetics-based machine learning, for the ill conditioned combinatorial problem of structural rule learning, while using statistical and mathematical techniques for parameter learning. This system was tested on MSTAR Public Release SAR data using standard and extended operation conditions. These results were also compared against two baseline classifiers, a PCA based distance classifier and a MSE classifier. The classifiers were evaluated for accuracy (via training set classification) and robustness (via testing set classification). In both cases, the LCS based robust ATR system performed well with accuracy over 99% and robustness over 80%. [Copyright &y& Elsevier]

In recent years, the interest in interpretable classification models has grown. One of the proposed ways to improve the interpretability of a classification model based on collections of crisp rules is to use sets (unordered collections) instead of lists (ordered collections). One of the problems associated with sets is that multiple rules may cover a single instance but predict different classes for it, thus requiring a conflict resolution strategy. In this work, we propose two algorithms capable of finding feature-space regions inside which any created rule would be consistent with the already existing rules, preventing inconsistencies from arising. Our algorithms, named CFSGS and CFSBE, do not generate classification rules nor classification models but are instead meant to enhance algorithms that do so, such as Learning Classifier Systems. We analyzed both algorithms from a theoretical perspective and conducted experiments with a proof of concept evolutionary algorithm that employs CFSBE. The experiments suggest that using CFSBE as an embedded tool does incur a computational overhead, but such cost is not prohibitive. • The proposed algorithms enhance the generation of sets of classification rules. • The enhanced process ensures that all the rules are consistent with each other. • Generating conflict-free rules removes the need for conflict resolution strategies. • The algorithms can be used with categorical and continuous data. • The generated rules can be single or multi-label; hierarchical or flat. [ABSTRACT FROM AUTHOR]

The fragment-type appearance of two-dimensional barcodes provides the hint of embedding a fragment-type ultra-high frequency radio frequency identification (UHF RFID) tag into the barcode. In this reported work, genetic algorithm searching is used to select which modules in a quick response (QR) barcode should be metalised and connected to form the RFID tag antenna. The design is demonstrated by embedding a 915 MHz UHF RFID tag in a QR barcode label of dimensions 14.5 × 14.5 mm on a single-sided PCB. [ABSTRACT FROM AUTHOR]