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This paper presents a new method for hierarchical image segmentation. The hierarchical structure is represented by a binary tree with the main image as its root. At the lower levels, each node stands as one image segment, which is described by a weighted graph and may be divided into two new segments at the next level through a specific cut. Graph bi-sectioning is done by the self organizing property of ant systems. Ants are free to wander over one image segment graph to find the best cut on it. When an ant finds a suitable cut, it returns to its colony and leaves a proper value of pheromone over its trail to attract other ants to that cut. By using the Chemical Computing approach in this paper, it is assumed the mobile hormones (pheromone) are secreted which can diffuse around initial positions and attract more ants to the found cut. The advantages of this assumption are reducing the noise effects and improving the convergence speed of ants to find a new selected image segment, which can be seen in the practical results. [ABSTRACT FROM AUTHOR]

Optimal design for urban mass transit network is the precondition and basis to establish an effective public transportation system. Transit network optimization and headway optimization are two of the most important issues to be dealt with. In this paper, a transit network optimization model is firstly proposed to maximize the nonstop passenger flow. Moreover, this paper puts forwards the optimization model of headways for all the transit routes in the optimized network. Since all the two models can be boiled down to the NP-hard problem, two kinds of evolutionary algorithms, i.e., ant colony algorithm and improved genetic algorithm are introduced to solve the problems respectively. Finally, a case study in a typical city is introduced to explain the validity of the proposed methods. [ABSTRACT FROM AUTHOR]

Memetic evolutionary algorithms (MEAs) combine the global search of evolutionary learning methods and the fine-tune ability of local search methods so that they are orders of magnitude more accurate than traditional evolutionary algorithms in many problem domains. However, little work has been done on the mathematical model and convergence analysis of MEAs. In this paper, a theoretical model as well as the convergence analysis of a class of gradient-based MEAs is presented. The results of this paper are extensions of the research work on the abstract model and convergence analysis of general evolutionary algorithms. By modeling the local search of gradient methods as an abstract strong evolution operator, the theoretical framework for abstract memetic evolutionary algorithms is derived. Moreover, the global convergence theorems and the convergence rate estimations of gradient-based MEAs are also established. [ABSTRACT FROM AUTHOR]

A new algorithm of evolutionary computing, which combines clone selective algorithm involved in artificial immunity system theory and mind evolution algorithm (MEA) proposed in reference [4], is presented in this paper. Based on similartaxis which is the one of MEA operators, some operators borne by the new algorithm including such as clone mutation, clone crossover, clone selection are also introduced. Then the clone mind evolution algorithm (CMEA) is developed by using the diversity principle of antigen-antibody. Not only can CMEA converge to globally optimal solution, but also it solve premature convergence problem efficiently. The simulating results of the representative evaluation function show that the problem of degeneration phenomenon existing in GA and MEA can be perfectly solved, and the rapidity of convergence is evidently improved by CMEA studied in the paper. In the example of the solution to the numerical problem, the search range of solution is expanded and the possibility of finding the optimal solution is increased. [ABSTRACT FROM AUTHOR]

According to the principles of non-self detection and negative selection in natural immune system, two generating algorithms of detector are proposed in this paper after reviewing current detector generating algorithms used in artificial immune systems. We call them as Bit Mutation Growth Detector Generating Algorithm (BMGDGA) and Arithmetical-compliment Growth Detector Generating Algorithm (AGDGA) based on their operational features. The principle and work procedure of the two detector generating algorithms are elaborated in details in the paper. For evaluation of the proposed algorithms, they are tested and verified by using different datasets, and compared to Exhaustive Detector Generating Algorithm (EDGA). It turns out that the proposed two algorithms are superior to EDGA in detection performance and computational complexities. [ABSTRACT FROM AUTHOR]

Artificial Immune Systems (AIS), which are inspired from nature immune system, have recently been investigated for many information processing applications, such as feature extraction, pattern recognition, machine learning and data mining. In this paper, we investigate AIS, and in particular the clonal selection algorithm for Associative Classification (AC). To implement associative classification effectively, we need to tackle the problems on the very large search space of candidate rules during the rule mining process. This paper proposes a new approach known as AIS-AC for mining association rules effectively for classification. In AIS-AC, we treat the rule mining process as an optimization problem of finding an optimal set of association rules according to some predefined constraints. The proposed AIS-AC approach is efficient in dealing with the complexity problem on the large search space of rules. It avoids searching greedily for all possible association rules, and is able to find an effective set of associative rules for classification. [ABSTRACT FROM AUTHOR]

Multiple classifier systems have shown a significant potential gain in comparison to the performance of an individual best classifier. In this paper, a weighted combination model of multiple classifier systems was presented, which took sum rule and majority vote as special cases. Particle swarm optimization (PSO), a new population-based evolutionary computation technique, was used to optimize the model. We referred the optimized model as PSO-WCM. An experimental investigation was performed on UCI data sets and encouraging results were obtained. PSO-WCM proposed in this paper is superior to other combination rules given larger data sets. It is also shown that rejection of weak classifier in the ensemble can improve classification performance further. [ABSTRACT FROM AUTHOR]

Topical crawlers are becoming important tools to support applications such as specialized Web portals, online searching, and competitive intelligence. A topic driven crawler chooses the best URLs to pursue during web crawling. It is difficult to evaluate what URLs downloaded are the best. This paper presents some important metrics and an evaluation function for ranking URLs about pages relevance. We also discuss an approach to evaluate the function based on GA. GA evolving process can discover the best combination of the metrics' weights. Avoiding misleading the result by a single topic, this paper presents a method which characterization of the metrics' combination be extracted by mining frequent patterns. Extracting features adopts a novel FP-tree structure and FP-growth mining method based on FP-tree without candidate generation. The experiment shows that the performance is exciting, especially about a popular topic. [ABSTRACT FROM AUTHOR]

Numerous studies on time series prediction have been undertaken by a lot of researchers. Most of them relate to the construction of structure-invariable system whose parameter values do not change all the time. In fact, the parameter values of many realistic systems are always changing with time. In this case, the embedding theorems are invalid, predicting the behavior of parameter-varying systems is more difficult. This paper presents a new prediction technique, which is multiwavelets neural network. This technique absorbs the advantage of high resolution of wavelets and the advantages of learning and feed-forward of neural networks. The procedure of using the multiwavelets neural network for predicting is described in detail in this paper. Principal components analysis (PCA) as a statistical technique has been used to simplify the time series analysis in our experiments. The effectiveness of this network is demonstrated by applying it to predict Ikeda time series. [ABSTRACT FROM AUTHOR]

The business field is one of the important fields where the data mining technology is applied. The study mainly focuses on different attribute object's quantitative prediction and customer structure's qualitative prediction. Aiming at the characteristics of time series in business field, such as near-periodicity, non-stationarity and nonlinearity, the wavelet-neural networks-ARMA method is proposed and its application is examined in this paper. The hidden period and the non-stationarity existed in time series are extracted and separated by wavelet transformation. The characteristic of wavelet decomposition series is applied to BP networks and an autoregressive moving average (ARMA) model. The given example elucidates that the forecasting method mentioned in this paper can be employed to the business field successfully and efficiently. [ABSTRACT FROM AUTHOR]

Recent researches have been focusing on fusion of audio and visual features for reliable speech recognition in noisy environments. In this paper, we propose a neural network based model of robust speech recognition by integrating audio, visual, and contextual information. Bimodal Neural Network (BMNN) is a multi-layer perceptron of 4 layers, which combines audio and visual features of speech to compensate loss of audio information caused by noise. In order to improve the accuracy of speech recognition in noisy environments, we also propose a post-processing based on contextual information which are sequential patterns of words spoken by a user. Our experimental results show that our model outperforms any single mode models. Particularly, when we use the contextual information, we can obtain over 90% recognition accuracy even in noisy environments, which is a significant improvement compared with the state of art in speech recognition. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach for automatic detection of white blood cells in bone marrow microscopic images. Far more different from traditional color imaging analysis methods, a multispectral imaging techniques for image analysis is introduced. Multispectral image can not only show the spatial features of a cell, but also reveal the unique spectral information of each pixel. The supported vector machine (SVM) classifier is employed to train the spectrum vector of a pixel, and the output of the classifier can indicate the class type of the pixel: nucleus, erythrocytes, cytoplasm and background. Experimental results show that, compared with any other method previously reported, our method is more robust, precise and insensitive to smear staining and illumination condition. [ABSTRACT FROM AUTHOR]

Many algorithms that solve optimization problems are being developed and used. However, large and complex optimization problems still exist, and it is often difficult to obtain the desired results with one of these algorithms alone. This paper applies tabu search and ant colony optimization method to the container load sequencing problem. We also propose a hybrid algorithm, which can combine the merits of these two algorithms by running them alternately. Experiments have shown that the proposed hybrid algorithm is superior to both tabu search and ant colony optimization individually. [ABSTRACT FROM AUTHOR]

Ant Colony optimization takes inspiration from the behavior of real ant colony to solve optimization problems. This paper presents a parallel model for ant colony to solve the optimal packing problem. The problem is represented by a directed graph so that the objective of the original problem becomes to find the shortest closed circuit on the graph under the problem-specific constraints. A number of artificial ants are distributed on the graph and communicate with one another through the pheromone trails which are a form of the long-term memory guiding the future exploration of the graph. The algorithm supports the parallel computation and facilitates quick convergence to the optimal solution. The performance of the proposed method as compared to those of the genetic-based approaches is very promising. [ABSTRACT FROM AUTHOR]

A simulation program is a useful tool to predict the hybridization error propagated through a microfluidic system. This paper shows the hybridization event between solution strands and capture probes. The program provides a Graphical User Interface that allows the user to insert parameters as well as to observe results showing critical values and additional visualization of the simulation process. The program and its interface have been developed in Java, making it platform independent and web-based. [ABSTRACT FROM AUTHOR]

In this paper we propose a procedure for the storage and retrieval of digital signals utilizing DNA. Digital signals are encoded in DNA sequences that satisfy among other constraints the Noise Tolerance Constraint (NTC) that we have previously introduced. NTC takes into account the presence of noise in digital signals by exploiting the annealing between non-perfect complementary sequences. We discuss various issues arising from the development of DNA-based database solutions (i) in vitro (in test tubes, or other materials) for short-term storage and (ii) in vivo (inside organisms) for long-term storage. We discuss the benefits and drawbacks of each scheme and its effects on the codeword design problem and performance. We also propose a new way of constructing the database elements such that a short-term database can be converted into a long term one and vice versa without the need for a re-synthesis. The latter improves efficiency and reduces the cost of a long-term database. [ABSTRACT FROM AUTHOR]

Interest in DNA computing has increased overwhelmly since Adleman successfully demonstrated its capability to solve Hamiltonian Path Problem (HPP). Many research results of similar combinatorial problems which are mainly in the realm of computer science and mathematics have been presented. In this paper, implementation ideas and methods to solve an engineering related combinatorial problem using this DNA computing approach is presented. The objective is to find an optimal path for a complex elevator scheduling problem of an 8-storey building with 3 elevators. Each of the elevator traveled path is represented by DNA sequence of specific length that represent elevator's traveling time in a proportional way based on certain initial conditions such as present and destination floors, and hall calls for an elevator from a floor. The proposed ideas and methods show promising results that DNA computing approach can be well-suited for solving such real-world application in the near future. [ABSTRACT FROM AUTHOR]

In this paper, after a new DNA-based semantic model is theoretically proposed, the preliminary experiment on construction of the small test model is successfully done. This model, referred to as ‘semantic model based on molecular computing' (SMC) has the structure of a graph formed by the set of all (attribute, attribute values) pairs contained in the set of represented objects, plus a tag node for each object. Each path in the network, from an initial object-representing tag node to a terminal node represents the object named on the tag. Input of a set of input strands will result in the formation of object-representing dsDNAs via parallel self-assembly, from encoded ssDNAs representing both attributes and attribute values (nodes), as directed by ssDNA splinting strands representing relations (edges) in the network. The proposed model is rather suitable for knowledge representation in order to store vast amount of information with high density. The proposed model will appears as an interaction between AI and biomolecular computing research fields, and will be further extended for several AI applications. [ABSTRACT FROM AUTHOR]

Based on the concept of coarse-grained description, a new encoding scheme with grouped weight for protein sequence is presented in this paper. By integrating the new scheme with the component-coupled algorithm, the overall prediction accuracy of protein structural class is significantly improved. For the same training dataset consisting of 359 proteins, the overall prediction accuracy achieved by the new method is 7% higher than that based solely on the amino-acid composition for the jackknife test. Especially for α + β the increase of prediction accuracy can achieve 15%. For the jackknife test, the overall prediction accuracy by the proposed scheme can reach 91.09%, which implies that a significant improvement has been achieved by making full use of the information contained in the protein sequence. Furthermore, the experimental analysis shows that the improvement depends on the size of the training dataset and the number of groups. [ABSTRACT FROM AUTHOR]

The essential of Molecular docking problem is to find the optimum conformation of ligand bound with the receptor at its active site. Most cases the optimum conformation has the lowest interaction energy. So the molecular docking problem can be treated as a minimization problem. An entropy-based evolution model for molecular docking is proposed in this paper. The model of molecular docking is based on a multi-population genetic algorithm. Two molecular docking processes are investigated to demonstrate the efficiency of the proposed model. [ABSTRACT FROM AUTHOR]

Small populations are very desirable for reducing the required computational resources in evolutionary optimization of complex real-world problems. Unfortunately, the search performance of small populations often reduces dramatically in a large search space. To addresses this problem, a method to find an optimal search dimension for small populations is suggested in this paper. The basic idea is that the evolutionary algorithm starts with a small search dimension and then the search dimension is increased during the optimization. The search dimension will continue to increase if an increase in the search dimension improves the search performance. Otherwise, the search dimension will be decreased and then kept constant. Through empirical studies on a test problem with an infinite search dimension, we show that the proposed algorithm is able to find the search dimension that is the most efficient for the given population size. [ABSTRACT FROM AUTHOR]

This paper investigates a global search optimisation technique, referred to as the repeated weighted boosting search. The proposed optimisation algorithm is extremely simple and easy to implement. Heuristic explanation is given for the global search capability of this technique. Comparison is made with the two better known and widely used global search techniques, known as the genetic algorithm and adaptive simulated annealing. The effectiveness of the proposed algorithm as a global optimiser is investigated through several examples. [ABSTRACT FROM AUTHOR]

Advances in grid computing have recently sparkled the research and development of Grid problem solving environments for complex design. Parallelism in the form of distributed computing is a growing trend, particularly so in the optimization of high-fidelity computationally expensive design problems in science and engineering. In this paper, we present a powerful and inexpensive grid enabled evolution framework for facilitating parallelism in hierarchical parallel evolutionary algorithms. By exploiting the grid evolution framework and a multi-level parallelization strategy of hierarchical parallel GAs, we present the evolutionary optimization of a realistic 2D aerodynamic airfoil structure. Further, we study the utility of hierarchical parallel GAs on two potential grid enabled evolution frameworks and analysis how it fares on a grid environment with multiple heterogeneous clusters, i.e., clusters with differing specifications and processing nodes. From the results, it is possible to conclude that a grid enabled hierarchical parallel evolutionary algorithm is not mere hype but offers a credible alternative, providing significant speed-up to complex engineering design optimization. [ABSTRACT FROM AUTHOR]

In this paper, we propose an evolutionary algorithm based on a single operator called stochastic weighted learning, i.e., each individual will learn from other individuals specified with stochastic weight coefficients in each generation, for constrained optimization. For handling equality and inequality constraints, the proposed algorithm introduces a learning rate adapting technique combined with a fitness comparison schema. Experiment results on a set of benchmark problems show the efficiency of the algorithm. [ABSTRACT FROM AUTHOR]

This paper develops a novel method for solving a type of nonlinear programming model with all fuzzy coefficients (AFCNP). For a decision maker specified credibility level, by presenting the equivalent deterministic forms of fuzzy inequality constraints and fuzzy objective, the fuzzy model is converted into a crisp constrained nonlinear programming model with parameter (CPNP). An improved genetic algorithm is presented to solve the CPNP and obtain the crisp optimal solution of AFCNP for specified credibility level. [ABSTRACT FROM AUTHOR]

The differential evolution (DE) is a stochastic, population-based, and relatively unknown evolutionary algorithm for global optimization that has recently been successfully applied to many optimization problems. This paper presents a new variation on the DE algorithm, called the cooperative co-evolutionary differential evolution (CCDE). CCDE adopts the cooperative co-evolutionary architecture, which was proposed by Potter and had been successfully applied to genetic algorithm, to improve significantly the performance of the DE. Such improvement is achieved by partitioning a high-dimensional search space by splitting the solution vectors of DE into smaller vectors, then using multiple cooperating subpopulations (or smaller vectors) to co-evolve subcomponents of a solution. Applying the new DE algorithm to on 11 benchmark functions, we show that CCDE has a marked improvement in performance over the traditional DE and cooperative co-evolutionary genetic algorithm (CCGA). [ABSTRACT FROM AUTHOR]

The hybrid multimodal optimization algorithm that combines a novel clustering method and fitness sharing method is presented in this paper. The only parameter required by the novel clustering method is the peak number. The clustering criteria include minimizing the square sum of the inner-group distance, maximizing the square sum of the inter-group distance, and the fitness value of the individuals. After each individual has been classified to the certain cluster, fitness sharing genetic algorithm is used to find multiple peaks simultaneously. The empirical study of the benchmark problems shows that the proposed method has satisfactory performance. [ABSTRACT FROM AUTHOR]

Fitness sharing genetic algorithm is one of the most common used methods to deal with multimodal optimization problems. The algorithm requires peaks radii as the predefined parameter. It is very difficult to guess peaks radii for designers and decision makers in the real world applications. A novel self-adaptive annealing peaks radii control method has been suggested in this paper to deal with the problem. Peaks radii are coded into chromosomes and evolved while fitness sharing genetic algorithm optimizes the problem. The empirical results tested on the benchmark problems show that fitness sharing genetic algorithm with self-adaptive annealing peaks radii control method can find and maintain nearly all peaks steadily. This method is especially suitable for the problems whose peaks radii are difficult to estimate beforehand. [ABSTRACT FROM AUTHOR]

A new dynamic evolutionary algorithm based on orthogonal design (denoted by ODEA) is proposed in present paper. Its population does not consist of individuals (solution vectors), but of niches, a properly small hyper-rectangle where orthogonal design method likely work well. Each niche selects the best solution found so far as its representative. And orthogonal design method is employed to find potentially good solution which is probably the representative in the niche. The niche mutation, the only genetic operator in this evolutionary algorithm, is guided by the representative of the niche, therefore, the fitness of the offspring is likely better than that of its father, furthermore, ODEA evolves fast. We employ a complex benchmark (moving peaks functions) testing the new approach and the numerical experiments show that ODEA performs much better than SOS [1]. [ABSTRACT FROM AUTHOR]

In the case of multiobjective evolutionary algorithm, the outcome is usually an approximation of the true Pareto Optimal set and how to evaluate the quality of the approximation of the Pareto-optimal set is very important. In this paper, improved measures are carried out to the approximation, uniformity and well extended for the approximation of the Pareto optimal set with the advantage of easy to operate. Finally, we apply our measures to the four multiobjective evolutionary algorithms that are representative of the state-of-the-art on the standard functions. Results indicate that the measures are highly competitive and can be conducted to the comparisons of the approximation set. [ABSTRACT FROM AUTHOR]

Evolutionary algorithms are especially suited for multi-objective optimization problems. Many evolutionary algorithms have been successfully applied to various multi-objective optimization problems. However, theoretical studies on multi-objective evolutionary algorithms are relatively scarce. This paper analyzes the convergence properties of a simple pragmatic (μ+1)-MOEA. The convergence of MOEAs is defined and the general convergence conditions are studied. Under these conditions, it is proven that the proposed (μ+1)-MOEA converges almost surely to the Pareto-optimal front. [ABSTRACT FROM AUTHOR]

In this paper, the encoding techniques of Genetic Algorithms are studied and a sufficient convergence condition on genetic encoding is presented. Some new categories of codes are defined, such as Uniform code, Bias code, Tri-sector code and Symmetric codes etc. Meanwhile, some new definitions on genetic encoding as well as some operations are presented, so that a sufficient convergence condition of GAs is inducted. Based on this study, a new genetic strategy, GASC(Genetic Algorithm with Symmetric Codes), is developed and applied in robot dynamic control and path planning. The experimental results show that the special genetic encoding techniques enhance the performance of Genetic Algorithms. The convergence speed of GASC is much faster than that of some traditional genetic algorithms. That is very significant for finding more application of GAs, as, in many cases, Genetic Algorithms' applications are limited by their convergence speed. [ABSTRACT FROM AUTHOR]

The aim of this paper is to study two new forms of genetic operators: duplication and fabrication. Duplication is a reproduce procedure that will reproduce the best fit chromosome from the elite base. The introduction of duplication operator into the modified GA will speed up the convergence rate of the algorithm however the trap into local optimality can be avoided. Fabrication is an artificial procedure used to produce one or several chromosomes by mining gene structures from the elite chromosome base. Statistical inference by job assignment procedure will be applied to produce artificial chromosomes and these artificial chromosomes provides new search directions and new solution spaces for the modified GA to explore. As a result, better solution quality can be achieved when applying this modified GA. Different set of problems will be tested using modified GA by including these two new operators in the procedure. Experimental results show that the new operators are very informative in searching the state space for higher quality of solutions. [ABSTRACT FROM AUTHOR]

In this paper we propose one way of mathematical formulation of structure of molecular activity from operator algebraic view points. [ABSTRACT FROM AUTHOR]

In this paper, Immune Mind Evolutionary Algorithm (IMEA) is introduced to correct the nonlinearity of frequency modulation (FM) of voltage controlled oscillator (VCO) in linear frequency modulation continuous wave (LFMCW) radar level gauge. Firstly, the FM voltage is divided into several subsections, then by using fast Fourier transform (FFT) for the beat frequency signals, the characteristic of the spectrum is distilled, and furthermore an evaluation function is constructed. IMEA is applied to optimize the endpoint coordinates of the subsections to get nonlinear curve of FM voltage so as to compensate for the nonlinearity of VCO. Experiments show that the proposed method has good correction performance with no requirement for additional hardware, and it can complete correction in a relative short time. [ABSTRACT FROM AUTHOR]

In this paper, Immune Clonal Selection theory and Lamarckism are integrated to form a new algorithm, Lamarckian Polyclonal Programming Algorithm (LPPA), for solving the global numerical optimization problem. The idea that Lamarckian evolution described how organism can evolve through learning, namely the point of "Gain and Convey" is applied, then this kind of learning mechanism is introduced into Adaptive Polyclonal Programming Algorithm (APPA). In the experiments, ten benchmark functions are used to test the performance of LPPA, and the scalability of LPPA along the problem dimension is studied with great care. The results show that LPPA achieves a good performance when the dimensions are increased from 20-10,000. Moreover, even when the dimensions are increased to as high as 10,000, LPPA still can find high quality solutions at a low computation cost. Therefore, LPPA has good scalability and is a competent algorithm for solving high dimensional optimization problems. [ABSTRACT FROM AUTHOR]

The accurate control of the work pieces temperature is a nonlinear, large time-delay, and cross-coupling complicated control problem in vacuum annealing furnace. In order to control the temperature of work pieces accurately. The optimization model for accurate work pieces temperature control has been proposed by the data gathered from the scene. The model was set up with Wavelet Neural Networks (WNN). Adaptive Immune Genetic Algorithm (AIGA) optimized the WNN structure and parameters (weights, dilation and translation). Simulation and experiment results show that the model in this paper is better than the model established with NN and optimizing the weights of NN by GA. And, it improves the training rate of Networks and obtains a system with good steady state precision, real timeliness and robustness. [ABSTRACT FROM AUTHOR]

Designing optimal measurement infrastructure is a key step for network management. In this work the goal of the optimization is to identify a minimum aggregating nodes set subject to bandwidth and delay constraints on the aggregating procedure. The problem is NP-hard. In this paper, we describe the way of using Genetic Algorithm for finding aggregating nodes set. The simulation indicates that Genetic Algorithm can produce much better result than the current method of randomly picking aggregating nodes. [ABSTRACT FROM AUTHOR]

Based on clonal selection principle, an improved immune algorithm (IIA) is proposed in this paper. This algorithm generates the next population under the guidance of the previous superior antibodies (Ab's) in a small and a large neighborhood respectively, in order to realize the parallel global and local search capabilities. The computational results show that higher quality solutions are obtained in a shorter time, and the degree of diversity in population are maintained by the proposed method. Meanwhile, "Average truncated generations" and "Distribution entropy of truncated generations" are used to evaluate the optimization efficiency of IIA. The comparison with clonal selection algorithm (CSA) demonstrates the superiority of the proposed algorithm IIA. [ABSTRACT FROM AUTHOR]

Production scheduling is an important part in the factories, and there are various uncertainties in the production scheduling of industrial processes. A scheduling mathematical model for flow shop problems with uncertain processing time has been established based on fuzzy programming theory. And in this paper, the fuzzy model can be translated into two mathematical models about the characteristic of scheduling problems. Furthermore, a fuzzy immune scheduling algorithm combined with the feature of the Immune Algorithm is proposed, which prevents the possibility of stagnation in the iteration process and achieves fast convergence for global optimization. The effectiveness and efficiency of the fuzzy scheduling model and the proposed algorithm are demonstrated by simulation results. [ABSTRACT FROM AUTHOR]

A new algorithm based on antibody immunodominance (AIDA) for TSP is explored. The main content of this paper is to explore how to produce the set of immunodominance and the superior antibodies. The experience proves that the algorithm has higher convergence speed and better solution compared with the corresponding genetic algorithm and is fit to solving complex problems. [ABSTRACT FROM AUTHOR]

The goal of maximum cut problem is to partition the vertex set of an undirected graph into two parts in order to maximize the cardinality of the set of edges cut by the partition. This paper proposes an Adaptive Hybrid Immune Genetic Algorithm, which includes key techniques such as vaccine abstraction, vaccination and affinity-based selection. A large number of instances have been simulated, and the results show that proposed algorithm is superior to existing algorithms. [ABSTRACT FROM AUTHOR]

A novel immunity-based genetic algorithm is proposed to resolve Qos multicast routing effectively and efficiently in this paper. The idea of immunity is mainly realized through two steps based on reasonably selecting vaccines, i.e., a vaccination and an immune selection. Experimental results show that this algorithm can find optimal solution quickly and has a good scalability. [ABSTRACT FROM AUTHOR]

This paper proposes a novel dynamic clustering algorithm called DCBAIN, which based on the artificial immune network and immune optimization algorithm. The algorithm includes two phases, it begins by running artificial immune network to find a clustering feasible solution (CFS), then it employs antibody clone algorithm (ACA) to get the optimal cluster number and cluster centers on the CFS. Some experimental results show that new algorithm has satisfied convergent probability and convergent speed. [ABSTRACT FROM AUTHOR]

Inspired by the learning mechanism of the biological immune system, the paper presents a method for weather forecast. Expressions of antigen and B-cell are defined. An immune-based supervised learning algorithm is described in detail. A weather forecast system based on immune theory has thus been presented. The experimental results show that the proposed method has higher forecast accuracy rate than neural network based weather forecast technique. [ABSTRACT FROM AUTHOR]

Intrusion Detection based on Adaptive Resonance Theory and Artificial Immune Network Clustering (ID-ARTAINC) is proposed in this paper. First the mass data for intrusion detection are pretreated by Adaptive Resonance Theory (ART) network to form glancing description of the data and to get vaccine. The outputs of ART network are considered as initial antibodies to train an Immune Network, Last Minimal Spanning Tree is employed to perform clustering analysis and obtain characterization of normal data and abnormal data. ID-ARTAINC can deal with mass unlabeled data to distinguish between normal and anomaly and to detect unknown attacks. The computer simulations on the KDD CUP99 dataset show that ID-ARTAINC achieves higher detection rate and lower false positive rate. [ABSTRACT FROM AUTHOR]

In order to improve the generating e.ciency of the detector set, a new detection rule called edit distance rule is presented in this paper, based on the negative selection model of Arti.cial Immune System (AIS). Under this rule, edit distance is adopted to measure the similarity between self strings and randomly generated strings. Then a new detector generating algorithm used the new rule is discussed. It is necessary to use the Trie data structure to store the strings in the self set in this new algorithm. Finally, the advantages of the algorithm are given through the theoretical analysis. [ABSTRACT FROM AUTHOR]

Observing purely reactive situations in modern computer games, one can see that in many cases few, simple rules are sufficient to perform well in the game. In spite of this, the programming of an artificial opponent is still a hard and time consuming task in the way it is done for the most games today. In this paper we propose a system in which no direct programming of the behaviour of the opponents is necessary. Instead, rules are gained by observing human players and then evaluated and optimised by an evolutionary algorithm to optimise the behaviour. We will show that only little learning effort is required to be competitive in reactive situations. In the course of our experiments our system proved to generate better artificial players than the original ones supplied with the game. [ABSTRACT FROM AUTHOR]

This paper proposes a dynamic proportion portfolio insurance (DPPI) strategy based on the popular constant proportion portfolio insurance (CPPI) strategy. The constant multiplier in CPPI is generally regarded as the risk multiplier. Since the market changes constantly, we think that the risk multiplier should change accordingly. This research identifies factors relating to market volatility. These factors are built into equation trees by genetic programming. Experimental results show that our DPPI strategy is more profitable than traditional CPPI strategy. [ABSTRACT FROM AUTHOR]

Immune algorithm is a global optimal algorithms based on the biological immune theory. In this paper, a novel immune algorithm is proposed for classification rule discovery. The idea of immunity is mainly realized through two steps based on reasonably selecting vaccines, i.e., a vaccination and an immune selection. Experimental results show that immune algorithm performs better than RISE with respect to predictive accuracy and rule list mined simplicity. [ABSTRACT FROM AUTHOR]