Abstract: The differential evolution algorithm is a floating-point encoded evolutionary algorithm for global optimization over continuous spaces. The algorithm has so far used empirically chosen values for its search parameters that are kept fixed through an optimization process. The objective of this paper is to introduce a new version of the Differential Evolution algorithm with adaptive control parameters – the fuzzy adaptive differential evolution algorithm, which uses fuzzy logic controllers to adapt the search parameters for the mutation operation and crossover operation. The control inputs incorporate the relative objective function values and individuals of the successive generations. The emphasis of this paper is analysis of the dynamics and behavior of the algorithm. Experimental results, provided by the proposed algorithm for a set of standard test functions, outperformed those of the standard differential evolution algorithm for optimization problems with higher dimensionality.

Abstract: Evolutionary algorithms have been widely used for stationary optimization problems. However, the environments of real world problems are often dynamic. This seriously challenges traditional evolutionary algorithms. In this paper, the application of population-based incremental learning (PBIL) algorithms, a class of evolutionary algorithms, for dynamic problems is investigated. Inspired by the complementarity mechanism in nature a Dual PBIL is proposed, which operates on two probability vectors that are dual to each other with respect to the central point in the genotype space. A diversity maintaining technique of combining the central probability vector into PBIL is also proposed to improve PBIL’s adaptability in dynamic environments. In this paper, a new dynamic problem generator that can create required dynamics from any binary-encoded stationary problem is also formalized. Using this generator, a series of dynamic problems were systematically constructed from several benchmark stationary problems and an experimental study was carried out to compare the performance of several PBIL algorithms and two variants of standard genetic algorithm. Based on the experimental results, we carried out algorithm performance analysis regarding the weakness and strength of studied PBIL algorithms and identified several potential improvements to PBIL for dynamic optimization problems.

Abstract: Evolutionary algorithms usually require a large number of objective function evaluations before converging to a good solution. However, many real-world applications allow for only very few objective function evaluations. To solve this predicament, one promising possibility seems to not evaluate every individual, but to just estimate the quality of some of the individuals. In this paper, we estimate an individual’s fitness on the basis of previously observed objective function values of neighboring individuals. Two estimation methods, interpolation and regression, are tested and compared. The experiments show that by using fitness estimation, it is possible to either reach a better fitness level in the given time, or to reach a desired fitness level much faster (roughly half the number of evaluations) than if all individuals are evaluated.

Abstract: A novel hybrid method based on evolutionary computation techniques is presented in this paper for training Fuzzy Cognitive Maps. Fuzzy Cognitive Maps is a soft computing technique for modeling complex systems, which combines the synergistic theories of neural networks and fuzzy logic. The methodology of developing Fuzzy Cognitive Maps relies on human expert experience and knowledge, but still exhibits weaknesses in utilization of learning methods and algorithmic background. For this purpose, we investigate a coupling of differential evolution algorithm and unsupervised Hebbian learning algorithm, using both the global search capabilities of Evolutionary strategies and the effectiveness of the nonlinear Hebbian learning rule. The use of differential evolution algorithm is related to the concept of evolution of a number of individuals from generation to generation and that of nonlinear Hebbian rule to the concept of adaptation to the environment by learning. The hybrid algorithm is introduced, presented and applied successfully in real-world problems, from chemical industry and medicine. Experimental results suggest that the hybrid strategy is capable to train FCM effectively leading the system to desired states and determining an appropriate weight matrix for each specific problem.

Abstract: We study the use of neural networks as approximate models for the fitness evaluation in evolutionary design optimization. To improve the quality of the neural network models, structure optimization of these networks is performed with respect to two different criteria: One is the commonly used approximation error with respect to all available data, and the other is the ability of the networks to learn different problems of a common class of problems fast and with high accuracy. Simulation results from turbine blade optimizations using the structurally optimized neural network models are presented to show that the performance of the models can be improved significantly through structure optimization.

Abstract: The optimisation of dynamic optima can be a difficult problem for evolutionary algorithms due to diversity loss. However, another population based search technique, particle swarm optimisation, is well suited to this problem. If some or all of the particles are ‘charged’, an extended swarm can be maintained, and dynamic optimisation is possible with a simple algorithm. Charged particle swarms are based on an electrostatic analogy—inter-particle repulsions enable charged particles to swarm around a nucleus of neutral particles. This paper proposes a diversity measure and examines its time development for charged and neutral swarms. These results facilitate predictions for optima tracking given knowledge of the amount of dynamism. A number of experiments test these predictions and demonstrate the efficacy of charged particle swarms in a simple dynamic environment.

Abstract: This paper examines the use of fuzzy cognitive maps (FCMs) as a technique for modeling political and strategic issues situations and supporting the decision-making process in view of an imminent crisis. Its object domain is soft computing using as its basic elements different methods from the areas of fuzzy logic, cognitive maps, neural networks and genetic algorithms. FCMs, more specifically, use notions borrowed from artificial intelligence and combine characteristics of both fuzzy logic and neural networks, in the form of dynamic models that describe a given political setting. The present work proposes the use of the genetically evolved certainty neuron fuzzy cognitive map (GECNFCM) as an extension of certainty neuron fuzzy cognitive maps (CNFCMs) aiming at overcoming the main weaknesses of the latter, namely the recalculation of the weights corresponding to each concept every time a new strategy is adopted. This novel technique combines CNFCMs with genetic algorithms (GAs), the advantage of which lies with their ability to offer the optimal solution without a problem-solving strategy, once the requirements are defined. Using a multiple scenario analysis we demonstrate the value of such a hybrid technique in the context of a model that reflects the political and strategic complexity of the Cyprus issue, as well as the uncertainties involved in it. The issue has been treated on a purely technical level, with distances carefully kept concerning all sides involved in it.

Abstract: This paper introduces new hybrid cross-over methods and new hybrid selection methods for real coded genetic algorithm (RCGA), to solve the optimal control problem of a class of hybrid system, which is motivated by the structure of manufacturing environments that integrate process and optimal control. In this framework, the discrete entities have a state characterized by a temporal component whose evolution is described by event-driven dynamics and a physical component whose evolution is described by continuous time-driven systems. The proposed RCGA with hybrid genetic operators can outperform the conventional RCGA and the existing Forward Algorithms for this class of systems. The hybrid genetic operators improve both the quality of the solution and the actual optimum value of the objective function. A typical numerical example of the optimal control problem with the number of jobs varying from 5 to 25 is included to illustrate the efficacy of the proposed algorithm. Several statistical analyses are done to compare the betterment of the proposed algorithm over the conventional RCGA and Forward Algorithm. Hypothesis t-test and Analysis of Variance (ANOVA) test are also carried out to validate the effectiveness of the proposed algorithm.

Abstract: Fuzzy cognitive maps (FCMs) constitute an attractive modeling approach that encompasses advantageous features. The most pronounces are the flexibility in system design, model and control, the comprehensive operation and the abstractive representation of complex systems. The main deficiencies of FCMs are the critical dependence on the initial expert’s beliefs, the recalculation of the weights corresponding to each concept every time a new strategy is adopted and the potential convergence to undesired equilibrium states. In order to update the initial knowledge of human experts and to combine the human experts’ structural knowledge with the training from data, a learning methodology for FCMs is proposed. This learning method, based on nonlinear Hebbian-type learning algorithm, is used to adapt the cause–effect relationships of the FCM model improving the efficiency and robustness of FCMs. A process control problem is presented and its process is investigated using the proposed weight adaptation technique.

Abstract: Numerical problems are not very frequently addressed in the P systems literature. In this paper we present an effective solution to the 2-Partition problem via a family of deterministic P systems with active membranes using 2-division. The design of this solution is a sequel of several previous works on other problems, mainly on the Subset-Sum and the Knapsack problems. Several improvements are introduced and explained.

Abstract: Dynamic cognitive networks (DCNs) define a novel approach to functionalize cognitive mapping and complex systems analysis, which were recently supported by fuzzy cognitive maps (FCMs). The modeling and inference limitations met in FCMs, especially in situations with strong nonlinearity and temporal phenomena, pushed towards DCNs; their theoretical framework is scheduled to confront the preceding weaknesses and offer wider possibilities in causal structures management. Trying to contribute to the enhancement of DCNs, at first, systemic and environmental metaphors are introduced with practical mathematical formalisms and generalized nomenclature. Nonlinear and asymmetric cause-effect relationships, decaying mechanisms, inertial forces, diminishing effects and biases formulate a powerful set of adaptive characteristics that strengthen the operational behavior of DCNs. Second, the strategic reorientation of DCNs is attempted as generalized approximation tools. This new strategic option is verified not only in classical function approximation tests, but also in the challenging area of securities markets. The platform of evaluation of DCNs involves comparisons with a linear multiple regression model, a feed-forward neural network trained with both back-propagation and evolution strategies, a radial basis function network, and an adaptive network-based fuzzy inference system (ANFIS). Through the experiments for short-term stock price predictions, multiple issues are analyzed not only about the role of diverse DCN parameters, but also about the given problem of financial markets modeling and forecasting. Simulations distinguish DCNs as a strong methodology with noticeable adaptability in complicated patterns and broad generalization capabilities while, at the same time, the all-embracing outcomes support previous findings of partially random walk phenomena in short-term stock market forecasting attempts.

Abstract: Wavelets are used for the processing of signals that are non-stationary and time varying. The electromyogram (EMG) contains transient signals related to muscle activity. Wavelet coefficients are proposed as features for identifying muscle fatigue. By observing the approximation coefficients it is shown that their amplitude follows closely the muscle fatigue development. The proposed method for detecting fatigue is automated by using neural networks. The self-organizing map (SOM) has been used to visualize the variation of the approximation wavelet coefficients and aid the detection of muscle fatigue. The results show that a 2D SOM separates EMG signatures from fresh and fatigued muscles, thus providing a visualization of the onset of fatigue over time. The map is able to detect if muscles have recovered temporarily. The system is adaptable to different subjects and conditions since the techniques used are not subject or workload regime specific.

Abstract: In this paper, we show how one can take advantage of the stability and effectiveness of object data clustering algorithms when the data to be clustered are available in the form of mutual numerical relationships between pairs of objects. More precisely, we propose a new fuzzy relational algorithm, based on the popular fuzzy C-means (FCM) algorithm, which does not require any particular restriction on the relation matrix. We describe the application of the algorithm to four real and four synthetic data sets, and show that our algorithm performs better than well-known fuzzy relational clustering algorithms on all these sets.

Abstract: To improve recognition and generalization capability of back-propagation neural networks (BP-NN), a hidden layer self-organization inspired by immune algorithm called SONIA, is proposed. B cell construction mechanism of immune algorithm inspires a creation of hidden units having local data recognition ability that improves recognition capability. B cell mutation mechanism inspires a creation of hidden units having diverse data representation characteristics that improves generalization capability. Experiments on a sinusoidal benchmark problem show that the approximation error of the proposed network is 1/17 times lower than that of BP-NN. Experiments on real time-temperature-based food quality prediction data shows that the recognition capability is 18% improved comparing to that of BP-NN. The development of the world first time-temperature-based food quality prediction demonstrates the real applicability of the proposed method in the field of food industry.

Abstract: A new algorithm for generating a symmetric min-transitive opening of a similarity relation is proposed. Since min-transitive similarity relations are in one-to-one correspondence to hierarchical partition trees, our algorithm can be compared to certain classical clustering algorithms. The new algorithm is efficient and the generated opening is in practical situations usually closer to the original relation than are the openings generated by other algorithms. Various tests illustrate the obtained results.

Abstract: Route planning is an important problem for a car navigation system. Given a set of origin-destination pair, there could be many possible routes for a driver. Search for shortest route from one point to another on a weighted graph is a well known problem and has several solutions like Dijkstra algorithm, Bellman-Ford algorithm etc. But in case of car navigation systems the shortest path may not be the best one from the point of view of driver's satisfaction. So, for a practical car navigation system in dynamical environment, we need to specify multiple and separate good (near optimal) choices according to multiple criteria which make the search space too large to find out the solution in real time by deterministic algorithms. Genetic algorithms (GA) are now widely used to solve search problems with applications in practical routing and optimization problems. GA includes a variety of quasi optimal solutions, which can be obtained in a given time. In this work we propose a GA based algorithm to find out simultaneously several alternate routes depending on different criterion according to driver's choice such as shortest path by distance, path which contains minimum number of turns, path passing through mountains or by the side of a river etc. The proposed algorithm has been evaluated by simulation experiment using real road map compared to other existing GA based algorithms. It has been found that the proposed algorithm is quite efficient in finding alternate non overlapping routes with different characteristics.

Abstract: The primary features of the object-oriented paradigm lead to develop a complex and compositional testing framework for object-oriented software. Agent-oriented approach has become a trend in software engineering. Agent technologies facilitate the software testing by virtue of their high-level independency with parallel activation and automation. This paper proposed an integrated framework that has been built on two existing testing techniques namely Mutation Testing and Capability Testing. In both the cases, testing is carried out at Autonomous Unit Level (AUL) and Inter-Procedural Level (IPL). Mutation-Based Testing-Agent and Capability Assessment Testing-Agent have been developed for performing AUL testing and Method Interaction Testing-Agent has been developed for performing IPL testing. This agent-based framework is an attempt for developing an automated software testing environment and among the several phases of Software Development Life Cycle (SDLC), this framework is recommended for unit testing in code complete phase and alpha phase. This methodology gives the basic approach to agent-based frameworks for testing and to optimization of agent-based testing schedules, subject to timing constraints. This adds ''interesting new opportunities in the object-oriented software testing phase'' to the existing literature that is concerned with software testing frameworks.

Abstract: T-cell-dependent humoral immune response is one of the more complex immunological events in the biological immune system, involving interaction of B cells with antigen (Ag) and their proliferation, differentiation and subsequent secretion of antibody (Ab). Inspired by these immunological principles, a Multilevel Immune Learning Algorithm (MILA) is proposed for novel pattern recognition. This paper describes the detailed background of MILA, and outlines its main features in different phases: Initialization phase, Recognition phase, Evolutionary phase and Response phase. Different test problems are studied and experimented with MILA for performance evaluation. The results show MILA is flexible and efficient in detecting anomalies and novel patterns.

Abstract: Multiple sequence alignment is an important tool in molecular sequence analysis. This paper presents genetic algorithms to solve multiple sequence alignments. Several data sets are tested and the experimental results are compared with other methods. We find our approach could obtain good performance in the data sets with high similarity and long sequences.The software can be found in http://rsdb.csie.ncu.edu.tw/tools/msa.htm.

Abstract: This paper presents a new tracking method for a mobile robot by combining predictive control and fuzzy logic control. Trajectory tracking of autonomous mobile robots usually has non-linear time-varying characteristics and is often perturbed by additive noise. To overcome the time delay caused by the slow response of the sensor, the algorithm uses predictive control, which predicts the position and orientation of the robot. In addition, fuzzy control is used to deal with the non-linear characteristics of the system. Experimental results demonstrate the feasibility and advantages of this predictive fuzzy control on the trajectory tracking of a mobile robot.

Abstract: A dynamic fuzzy logic-based adaptive algorithm is proposed for reducing the effect of stick-slip friction and for the compensation of backlash. The control scheme proposed is an online identification and indirect adaptive control, in which the control input is adjusted adaptively to compensate the effect of these non-linearities. A tuning algorithm for fuzzy logic parameters is used to ensure stable performance. The efficacy of the proposed algorithm is verified on a one degree of freedom (1-DOF) mechanical mass system with stick-slip friction and on a one-link robot manipulator with backlash.

Abstract: This paper describes an evolutionary way to acquire behaviors of a mobile robot for recognizing environments. We have proposed Action-based Environment Modeling (AEM) approach for a simple mobile robot to recognize environments. In AEM, a behavior-based mobile robot acts in each environment and action sequences are obtained. The action sequences are transformed into vectors characterizing the environments, and the robot identifies the environments with similarity between the vectors. The suitable behaviors like wall-following for AEM have been designed by a human. However the design is very difficult for him/her because the search space is huge and intuitive understanding is hard. Thus we apply evolutionary robotics approach to design of such behaviors using genetic algorithm and make simulations in which a robot recognizes the environments with different structures. As results, we find out suitable behaviors are learned even for environments in which human hardly designs them, and the learned behaviors are more efficient than hand-coded ones.

Abstract: It is proved that four membranes suffice to a variant of P systems with symport/antiport with maximal parallelism to generate all recursively enumerable sets of numbers. P systems with symport/antiport without maximal parallelism are also studied, considering two termination criteria.

Abstract: In order to delineate the state of the art of the main TM applications a two-step strategy has been pursued: first of all, some of the main European and Italian companies offering TM solutions were contacted, in order to collect information on the characteristics of the applications; secondly, a detailed search on the web was made to collect further information about users or developers and applications. On the basis of the material collected, a synthetic grid was built to collocate, from more than 300 cases analysed, the 100 ones that we considered most relevant for the typology of function and sector of activity. The joint analysis of the different case studies has given an adequate picture of TM applications according to the possible types of results that can be obtained, the main specifications of the sectors of applications and the type of functions. Finally it is possible to classify the applications matching the level of customisation (followed in the tools development) and the level of integration (between users and developers). This matching produces four different situations: standardisation, outsourcing, internalisation, synergism.

Abstract: In this paper we overview recent results about the lattice of subvarieties of the variety BL of BL-algebras and the equational definition of some families of them.

Abstract: Our method of estimation of parameters in statistics uses a set of confidence intervals producing a triangular shaped fuzzy number for the estimator. In crisp linear regression ** we use this to obtain fuzzy number estimators for τ and λ. This is then employed in fuzzy prediction and fuzzy hypothesis testing about the values of τ and λ.