A self-adaptive linear evolutionary algorithm for solving constrained optimization problems

In many real-world applications of evolutionary algorithms, the fitness of an individual requires a quantitative measure. This paper proposes a self-adaptive linear evolutionary algorithm (ALEA) in which we introduce a novel strategy for evaluating individual’s relative strengths and weaknesses. Based on this strategy, searching space of constrained optimization problems with high dimensions for design variables is compressed into two-dimensional performance space in which it is possible to quickly identify ‘good’ individuals of the performance for a multiobjective optimization application, regardless of original space complexity. This is considered as our main contribution. In addition, the proposed new evolutionary algorithm combines two basic operators with modification in reproduction phase, namely, crossover and mutation. Simulation results over a comprehensive set of benchmark functions show that the proposed strategy is feasible and effective, and provides good performance in terms of uniformity and diversity of solutions.     

Genetic algorithm approach for QoS-based tree topology construction in IEEE 802.16 mesh networks

The topological characteristics of an IEEE 802.16 mesh network including the tree’s depth and degree of its nodes affect the delay and throughput of the network.To reach the desired trade-off between delay and throughput,all potential trees should be explored to obtain a tree with the proper topology.Since the number of extractable tree topologies from a given network graph is enormous,we use a genetic algorithm(GA) to explore the search space and find a good enough trade-off between per-node,as well as network-wide delay and throughput.In the proposed GA approach,we use the Pruefer code tree representation followed by novel genetic operators.First,for each individual tree topology,we obtain expressions analytically for per-node delay and throughput.Based on the required quality of service,the obtained expressions are invoked in the computation of fitness functions for the genetic approach.Using a proper fitness function,the proposed algorithm is able to find the intended trees while different constraints on delay and throughput of each node are imposed.Employing a GA approach leads to the exploration of this extremely wide search space in a reasonably short time,which results in overall scalability and accuracy of the proposed tree exploration algorithm.     

Classification- and Regression-Assisted Differential Evolution for Computationally Expensive Problems

Differential Evolution (DE) has been well accepted as an effective evolutionary optimization technique. However, it usually involves a large number of fitness evaluations to obtain a satisfactory solution. This disadvantage severely restricts its application to computationally expensive problems, for which a single fitness evaluation can be highly timeconsuming. In the past decade, a lot of investigations have been conducted to incorporate a surrogate model into an evolutionary algorithm (EA) to alleviate its computational burden in this scenario. However, only limited work was devoted to DE. More importantly, although various types of surrogate models, such as regression, ranking, and classification models, have been investigated separately, none of them consistently outperforms others. In this paper, we propose to construct a surrogate model by combining both regression and classification techniques. It is shown that due to the specific selection strategy of DE, a synergy can be established between these two types of models, and leads to a surrogate model that is more appropriate for DE. A novel surrogate model-assisted DE, named Classification-and Regression-Assisted DE (CRADE) is proposed on this basis. Experimental studies are carried out on a set of 16 benchmark functions, and CRADE has shown significant superiority over DE-assisted with only regression or classification models. Further comparison to three state-of-the-art DE variants, i.e., DE with global and local neighborhoods (DEGL), JADE, and composite DE (CoDE), also demonstrates the superiority of CRADE.     

Efficient Parallel Algorithms for Some Graph Theory Problems

In this paper,a sequential algorithm computing the aww vertex pair distance matrix D and the path matrix Pis given.On a PRAM EREW model with p,1≤p≤n^2,processors,a parallel version of the sequential algorithm is shown.This method can also be used to get a parallel algorithm to compute transitive closure array A^* of an undirected graph.The time complexity of the parallel algorithm is O(n^3/p).If D,P and A^* are known,it is shown that the problems to find all connected components,to compute the diameter of an undirected graph,to determine the center of a directed graph and to search for a directed cycle with the minimum(maximum)length in a directed graph can all be solved in O(n^2/p logp)time.     

Personalized course generation and evolution based on genetic algorithms

Online learners are individuals,and their learning abilities,knowledge,and learning performance differ substantially and are ever changing.These individual characteristics pose considerable challenges to online learning courses.In this paper,we propose an online course generation and evolution approach based on genetic algorithms to provide personalized learning.The courses generated consider not only the difficulty level of a concept and the time spent by an individual learner on the concept,but also the changing learning performance of the individual learner during the learning process.We present a layered topological sort algorithm,which converges towards an optimal solution while considering multiple objectives.Our general approach makes use of the stochastic convergence of genetic algorithms.Experimental results show that the proposed algorithm is superior to the free browsing learning mode typically enabled by online learning environments because of the precise selection of learning content relevant to the individual learner,which results in good learning performance.     

A differential evolutionary based algorithm for multiuser OFDMA system adaptive resource allocation

In this paper, the problems of radio resource allocation for Orthogonal Frequency Division Multiple Access （OFDMA） systems are addressed. The main goal of this paper is to present and analyze base station allocation of subcarriers and adaptive modulation. We impose a set of proportional fairness constraints to assure that each user can achieve a required data rate. Since the optimal solution to the fairness problem is extremely computationally complex to obtain, we propose an adaptive radio resource allocation method based on differential evolutionary algorithm for multiuser OFDMA system. The performance of the described schemes is further evaluated in numerical experiments. We improve the convergence of the differential evolutionary algorithm through the method of elitist selection and adding some individuals with ＇good＇ genes to the initial population. Simulation results show that out proposed algorithm better than static subcarrier allocation schemes TDMA in muhiuser OFDMA system.     

An Optimal Model and Algorithm of Distributed Information System

Distributed information system makes itself be placed in changing file storage position according to the users＇ request pattern. In this paper, we rebuild the model for a management system to turn the process of file managing into a 0-1 programming problem, and present a new individual form to improve the operating efficiency. Aiming at the model, we define a neighborhood span to make segmentation for searching space by using the fitness, based on the region contraction algorithm, present a new evolution algorithm which has the capability of self-adaptively generating new individuals, and ultimately solve the management problem of the distributed file system.     

Parameter Control of Genetic Algorithms by Learning and Simulation of Bayesian Networks — A Case Study for the Optimal Ordering of Tables

Parameter setting for evolutionary algorithms is still an important issue in evolutionary computation. There are two main approaches to parameter setting: parameter tuning and parameter control. In this paper, we introduce self-adaptive parameter control of a genetic algorithm based on Bayesian network learning and simulation. The nodes of this Bayesian network are genetic algorithm parameters to be controlled. Its structure captures probabilistic conditional (in)dependence relationships between the parameters. They are learned from the best individuals, i.e., the best configurations of the genetic algorithm. Individuals are evaluated by running the genetic algorithm for the respective parameter configuration. Since all these runs are time-consuming tasks, each genetic algorithm uses a small-sized population and is stopped before convergence. In this way promising individuals should not be lost. Experiments with an optimal search problem for simultaneous row and column orderings yield the same optima as state-of-the-art methods but with a sharp reduction in computational time. Moreover, our approach can cope with as yet unsolved high-dimensional problems.     

Basic principle and rational user in interactive evolutionary computation

As is the basic principle in interactive evolutionary computation （IEC） that the optima evolved by the algorithm should be the individuals that the user is most satisfied with. A rational user will keep consistent dominated relationship between the assigned fitness and his/her preference for the individuals. Even for rational users, it is unavoidable that the fitness he/she assigned contains noise. The result that the fitness noise caused by a rational user will not damage the basic principle in IEC is concluded. In order to prove the conclusion, two theorems separately named as strong condition and weak condition are put forward. The experiments and their results validate the theorems. As the successive issue, the relationship between the two conditions, the relationship between weak condition and fitness scaling, the true fitness in IEC, the definition of noise in IEC and so on are discussed. The results establish necessary foundation for future research.     

Solving fuzzy equations using evolutionary algorithms and neural nets

 In this paper we use evolutionary algorithms and neural nets to solve fuzzy equations. In Part I we: (1) first introduce our three solution methods for solving the fuzzy linear equation AˉXˉ + Bˉ= Cˉ; for Xˉ and (2) then survey the results for the fuzzy quadratic equations, fuzzy differential equations, fuzzy difference equations, fuzzy partial differential equations, systems of fuzzy linear equations, and fuzzy integral equations; and (3) apply an evolutionary algorithm to construct one of the solution types for the fuzzy eigenvalue problem. In Part II we: (1) first discuss how to design and train a neural net to solve AˉXˉ + Bˉ= Cˉ for Xˉ and (2) then survey the results for systems of fuzzy linear equations and the fuzzy quadratic.     

Research into Application of Directed-Graph Storage Structure in Workflow Based on Object Petri Model-Defining

Storage structure and algorithm of graph play an important role in Object Petri＇s simulation and workflow analysis. Object Petri is a type of directed graph. Through studying the problem of graph storage, a directed graph data structure （carried out by trifurcate chain-table） is put forward, and its construction algorithm is given. It can enhance the speed and decrease the complexity of the algorithm. The trifurcate chain-table structure of Object Pctri is emphasized in this paper and it is of benefit to expanding various analysis algorithms of Object Petri. So the model-defining of workflow is easily carried out.     

Fast global kernel fuzzy c-means clustering algorithm for consonant/vowel segmentation of speech signal

We propose a novel clustering algorithm using fast global kernel fuzzy c-means-F（FGKFCM-F）, where F refers to kernelized feature space. This algorithm proceeds in an incremental way to derive the near-optimal solution by solving all intermediate problems using kernel-based fuzzy c-means-F（KFCM-F） as a local search procedure. Due to the incremental nature and the nonlinear properties inherited from KFCM-F, this algorithm overcomes the two shortcomings of fuzzy c-means（FCM）： sen- sitivity to initialization and inability to use nonlinear separable data. An accelerating scheme is developed to reduce the compu-tational complexity without significantly affecting the solution quality. Experiments are carried out to test the proposed algorithm on a nonlinear artificial dataset and a real-world dataset of speech signals for consonant/vowel segmentation. Simulation results demonstrate the effectiveness of the proposed algorithm in improving clustering performance on both types of datasets.     

Interactive image enhancement by fuzzy relaxation

In this paper,an interactive image enhancement(IIE)technique based on fuzzy relaxation is presented,which allows the user to select different intensity levels for enhancement and intermit the enhancement process according to his/her preference in applications.First,based on an analysis of the convergence of a fuzzy relaxation algorithm for image contrast enhancement,an improved version of this algorithm,which is called FuzzIIE Method 1,is suggested by deriving a relationship between the convergence regions and the parameters in the transformations defined in the algorithm.Then a method called FuzzIIE Method 2 is introduced by using a different fuzzy relaxation function,in which there is no need to re-select the parameter values for interactive image enhancement. Experimental results are presented demonstrating the enhancement capabilities of the proposed methods under different conditions.     

Soft computing based procurement planning of time-variable demand in manufacturing systems

Procurement planning with discrete time varying demand is an important problem in Enterprise Resource Planning (ERP).It can be described using the non-analytic mathematical programming model proposed in this paper.To solve the model we propose to use a fuzzy decision embedded genetic algorithm.The algorithm adopts an order strategy selection to simplify the original real optimization problem into binary ones.Then,a fuzzy decision quantification method is used to quantify experience from planning experts.Thus,decision rules can easily be embedded in the computation of genetic operations.This approach is applied to purchase planning problem in a practical machine tool works,where satisfactory results have been achieved.     

Evolutionary Pseudo-Relaxation Learning Algorithm for Bidirectional Associative Memory

This paper analyzes the sensitivity to noise in BAM (Bidirectional Associative Memory), and then proves the noise immunity of BAM relates not only to the minimum absolute value of net inputs (MAV) but also to the variance of weights associated with synapse connections. In fact, it is a positive monotonically increasing function of the quotient of MAV divided by the variance of weights. Besides, the performance of pseudo-relaxation method depends on learning parameters(λ and ξ), but the relation of them is not linear. So it is hard to find a best combination of λ and ξ which leads to the best BAM performance. And it is obvious that pseudo-relaxation is a kind of local optimization method, so it cannot guarantee to get the global optimal solution. In this paper, a novel learning algorithm EPRBAM (evolutionary psendo-relaxation learning algorithm for bidirectional association memory) employing genetic algorithm and pseudo-relaxation method is proposed to get feasible solution of BAM weight matrix. This algorithm uses the quotient as the fitness of each individual and employs pseudo-relaxation method to adjust individual solution when it does not satisfy constraining condition any more after genetic operation. Experimental results show this algorithm improves noise immunity of BAM greatly. At the same time, EPRBAM does not depend on learning parameters and can get global optimal solution.     

Building Fuzzy Thematic Clusters and Mapping Them to Higher Ranks in a Taxonomy

We present a novel methodology for the analysis of activities engaged in an organization such as the research conducted in a University department by mapping them to a related hierarchical taxonomy such as Classification of Computer Subjects by ACM (ACM-CCS). We start by collecting data of activities of the individual components of the organization and present them as the components fuzzy membership profiles over the subjects of the taxonomy. Our method generalizes the profiles in two steps. First step finds fuzzy clusters of taxonomy subjects according to the working of the organization. Second, each cluster is mapped to higher ranks of the taxonomy in a parsimonious way. Each of the steps is formalized and solved in a novel way. We build fuzzy clusters of the taxonomy leaves according to the similarity between individual profiles by using a novel, additive spectral, fuzzy clustering method that involves a number of model-based stopping conditions, in contrast to other methods. As the found clusters are not necessarily consistent with the taxonomy, each is considered as a query set. To lift a query set to higher ranks of the taxonomy, we develop an original recursive algorithm for minimizing a penalty function that involves ''head subjects'' on the higher ranks of the taxonomy together with their ''gaps'' and ''offshoots''. The method is illustrated by applying it to real-world data.     

Robust graph coloring based on the matrix semi-tensor product with application to examination timetabling

This paper investigates the robust graph coloring problem with application to a kind of examination timetabling by using the matrix semi-tensor product, and presents a number of new results and algorithms. First, using the matrix semi-tensor product, the robust graph coloring is expressed into a kind of optimization problem taking in an algebraic form of matrices, based on which an algorithm is designed to find all the most robust coloring schemes for any simple graph. Second, an equivalent problem of robust graph coloring is studied, and a necessary and sufficient condition is proposed, from which a new algorithm to find all the most robust coloring schemes is established. Third, a kind of examination timetabling is discussed by using the obtained results, and a method to design a practicable timetabling scheme is presented. Finally, the effectiveness of the results/algorithms presented in this paper is shown by two illustrative examples.     

Classification evolution algorithm based on cloud model

As an important branch of Knowledge Discovery, the task of Data Classification is to determine the objects that belong to which pre-defined goals. As evolutionary computation does not require priori assumptions, it shows great vitality in dealing with imprecise, incomplete and uncertain information, which the traditional methods of statistical classifications are helpless in the classification issues. This paper presents a classification algorithm based on cloud model and genetic algorithm. Experiments show that the algorithm is efficient to continuous attribute data sets for the classification.     

Partner selection model and soft computing approach for dynamic alliance of enterprises

Partner selection is an active research topic in agile manufacturing and supply chain management. In this paper, the problem is described by a 0-1 integer programming with non-analytical objective function. Then, the solution space is reduced by defining the inefficient candidate. By using the fuzzy rule quantification method, a fuzzy logic based decision making approach for the project scheduling is proposed. We then develop a fuzzy decision embedded genetic algorithm. We compare the algorithm with tranditional methods. The results show that the suggested approach can quickly achieve optimal solution for large size problems with high probability. The approach was applied to the partner selection problem of a coal fire power station construction project. The satisfactory results have been achieved.