# IEEE Transactions on Evolutionary Computation

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Displaying Results 1 - 22 of 22

Publication Year: 2009, Page(s): C1
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• ### IEEE Transactions on Evolutionary Computation publication information

Publication Year: 2009, Page(s): C2
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Publication Year: 2009, Page(s):945 - 958
Cited by:  Papers (755)
| | PDF (770 KB) | HTML

A new differential evolution (DE) algorithm, JADE, is proposed to improve optimization performance by implementing a new mutation strategy ldquoDE/current-to-p bestrdquo with optional external archive and updating control parameters in an adaptive manner. The DE/current-to-pbest is a generalization of the classic ldquoDE/current-to-best,rdquo while the optional archive operation util... View full abstract»

• ### Agent-Based Approach to Option Pricing Anomalies

Publication Year: 2009, Page(s):959 - 972
Cited by:  Papers (3)
| | PDF (619 KB) | HTML

Psychological studies on decision making under uncertainty, which have been inspired by Kahneman and Tversky's study, have attracted considerable interest in financial research as key factors to solve anomalies that cannot be explained by the traditional models. Recently, we proposed an agent-based prospect theoretical model and demonstrated that the loss-aversion feature of investors is capable o... View full abstract»

• ### Group Search Optimizer: An Optimization Algorithm Inspired by Animal Searching Behavior

Publication Year: 2009, Page(s):973 - 990
Cited by:  Papers (231)
| | PDF (432 KB) | HTML

Nature-inspired optimization algorithms, notably evolutionary algorithms (EAs), have been widely used to solve various scientific and engineering problems because of to their simplicity and flexibility. Here we report a novel optimization algorithm, group search optimizer (GSO), which is inspired by animal behavior, especially animal searching behavior. The framework is mainly based on the produce... View full abstract»

• ### Multiobjective Genetic Algorithm-Based Fuzzy Clustering of Categorical Attributes

Publication Year: 2009, Page(s):991 - 1005
Cited by:  Papers (42)
| | PDF (2126 KB) | HTML

Recently, the problem of clustering categorical data, where no natural ordering among the elements of a categorical attribute domain can be found, has been gaining significant attention from researchers. With the growing demand for categorical data clustering, a few clustering algorithms with focus on categorical data have recently been developed. However, most of these methods attempt to optimize... View full abstract»

• ### Analysis of the $(1+1)$-EA for Finding Approximate Solutions to Vertex Cover Problems

Publication Year: 2009, Page(s):1006 - 1029
Cited by:  Papers (24)
| | PDF (886 KB) | HTML

Vertex cover is one of the best known NP-hard combinatorial optimization problems. Experimental work has claimed that evolutionary algorithms (EAs) perform fairly well for the problem and can compete with problem-specific ones. A theoretical analysis that explains these empirical results is presented concerning the random local search algorithm and the (1+1)-EA. Since it is not expected that an al... View full abstract»

• ### Evolutionary Optimization of Constrained $k$-Means Clustered Assets for Diversification in Small Portfolios

Publication Year: 2009, Page(s):1030 - 1053
Cited by:  Papers (18)
| | PDF (1341 KB) | HTML

The problem of portfolio optimization has been rendered complex for direct solving by traditional and numerical approaches when constraints that model investor preferences and/or market friction are included in the mathematical model, and for such cases, heuristic approaches have been sought for their solution. In this paper, we discuss the solution of a subclass of portfolio optimization problems... View full abstract»

• ### Reliability-Based Optimization Using Evolutionary Algorithms

Publication Year: 2009, Page(s):1054 - 1074
Cited by:  Papers (65)
| | PDF (1843 KB) | HTML

Uncertainties in design variables and problem parameters are often inevitable and must be considered in an optimization task if reliable optimal solutions are sought. Besides a number of sampling techniques, there exist several mathematical approximations of a solution's reliability. These techniques are coupled in various ways with optimization in the classical reliability-based optimization fiel... View full abstract»

• ### On the Complexity of Computing the Hypervolume Indicator

Publication Year: 2009, Page(s):1075 - 1082
Cited by:  Papers (66)
| | PDF (352 KB) | HTML

The goal of multiobjective optimization is to find a set of best compromise solutions for typically conflicting objectives. Due to the complex nature of most real-life problems, only an approximation to such an optimal set can be obtained within reasonable (computing) time. To compare such approximations, and thereby the performance of multiobjective optimizers providing them, unary quality measur... View full abstract»

• ### Runtime Analysis of an Ant Colony Optimization Algorithm for TSP Instances

Publication Year: 2009, Page(s):1083 - 1092
Cited by:  Papers (33)
| | PDF (247 KB) | HTML

Ant colony optimization (ACO) is a relatively new random heuristic approach for solving optimization problems. The main application of the ACO algorithm lies in the field of combinatorial optimization, and the traveling salesman problem (TSP) is the first benchmark problem to which the ACO algorithm has been applied. However, relatively few results on the runtime analysis of the ACO on the TSP are... View full abstract»

• ### Facetwise Analysis of XCS for Problems With Class Imbalances

Publication Year: 2009, Page(s):1093 - 1119
Cited by:  Papers (13)
| | PDF (1792 KB) | HTML

Michigan-style learning classifier systems (LCSs) are online machine learning techniques that incrementally evolve distributed subsolutions which individually solve a portion of the problem space. As in many machine learning systems, extracting accurate models from problems with class imbalances-that is, problems in which one of the classes is poorly represented with respect to the other classes-h... View full abstract»

• ### Frankenstein's PSO: A Composite Particle Swarm Optimization Algorithm

Publication Year: 2009, Page(s):1120 - 1132
Cited by:  Papers (135)
| | PDF (1174 KB) | HTML

During the last decade, many variants of the original particle swarm optimization (PSO) algorithm have been proposed. In many cases, the difference between two variants can be seen as an algorithmic component being present in one variant but not in the other. In the first part of the paper, we present the results and insights obtained from a detailed empirical study of several PSO variants from a ... View full abstract»

• ### Benchmarking a Wide Spectrum of Metaheuristic Techniques for the Radio Network Design Problem

Publication Year: 2009, Page(s):1133 - 1150
Cited by:  Papers (12)
| | PDF (1066 KB) | HTML

The radio network design (RND) is an NP-hard optimization problem which consists of the maximization of the coverage of a given area while minimizing the base station deployment. Solving RND problems efficiently is relevant to many fields of application and has a direct impact in the engineering, telecommunication, scientific, and industrial areas. Numerous works can be found in the literature dea... View full abstract»

• ### Memetic Algorithm With Extended Neighborhood Search for Capacitated Arc Routing Problems

Publication Year: 2009, Page(s):1151 - 1166
Cited by:  Papers (83)
| | PDF (321 KB) | HTML

The capacitated arc routing problem (CARP) has attracted much attention during the last few years due to its wide applications in real life. Since CARP is NP-hard and exact methods are only applicable to small instances, heuristic and metaheuristic methods are widely adopted when solving CARP. In this paper, we propose a memetic algorithm, namely memetic algorithm with extended neighborhood search... View full abstract»

• ### Approximating the Set of Pareto-Optimal Solutions in Both the Decision and Objective Spaces by an Estimation of Distribution Algorithm

Publication Year: 2009, Page(s):1167 - 1189
Cited by:  Papers (38)
| | PDF (13916 KB) | HTML

Most existing multiobjective evolutionary algorithms aim at approximating the Pareto front (PF), which is the distribution of the Pareto-optimal solutions in the objective space. In many real-life applications, however, a good approximation to the Pareto set (PS), which is the distribution of the Pareto-optimal solutions in the decision space, is also required by a decision maker. This paper consi... View full abstract»

• ### Using Differential Evolution for a Subclass of Graph Theory Problems

Publication Year: 2009, Page(s):1190 - 1192
Cited by:  Papers (19)
| | PDF (150 KB) | HTML

Conventional differential evolution algorithms explore continuous spaces. In contrast, NP-complete graph problems are combinatorial and thus have discrete solution spaces, many with solutions encoded as binary strings. This letter describes a simple method showing how a conventional differential evolution algorithm can be used to solve these types of graph theory problems. The method is determinis... View full abstract»

• ### Correction to “A Fast Incremental Hypervolume Algorithm” [Dec 08 714-723]

Publication Year: 2009, Page(s): 1193
| | PDF (38 KB) | HTML

In the above titled paper (ibid., vol. 12, no. 6, pp. 714-723, Dec. 08), there was an error in the pseudo-code for the incremental hypervolume by slicing objectives (IHSO) that might prevent its easy implementation. The corrected pseudo-code is presented here. View full abstract»

• ### Special issue on advances in memetic computation

Publication Year: 2009, Page(s): 1194
| PDF (197 KB)

Publication Year: 2009, Page(s):1195 - 1196
| PDF (1065 KB)
• ### IEEE Computational Intelligence Society Information

Publication Year: 2009, Page(s): C3
| PDF (36 KB)
• ### IEEE Transactions on Evolutionary Computation Information for authors

Publication Year: 2009, Page(s): C4
| PDF (31 KB)

## Aims & Scope

IEEE Transactions on Evolutionary Computation publishes archival quality original papers in evolutionary computation and related areas including nature-inspired algorithms, population-based methods, and optimization where selection and variation are integral, and hybrid systems where these paradigms are combined. Purely theoretical papers are considered as are application papers that provide general insights into these areas of computation.

Full Aims & Scope

## Meet Our Editors

Editor-in-Chief
Professor Kay Chen Tan (IEEE Fellow)
Department of Computer Science
City University of Hong Kong
Kowloon Tong, Kowloon, Hong Kong
Email: kaytan@cityu.edu.hk