# IEEE Transactions on Evolutionary Computation

## Issue 1 • Feb. 2018

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## Filter Results

Displaying Results 1 - 17 of 17

Publication Year: 2018, Page(s): C1
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• ### IEEE Transactions on Evolutionary Computation publication information

Publication Year: 2018, Page(s): C2
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• ### Guest Editorial Evolutionary Many-Objective Optimization

Publication Year: 2018, Page(s):1 - 2
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• ### Localized Weighted Sum Method for Many-Objective Optimization

Publication Year: 2018, Page(s):3 - 18
Cited by:  Papers (2)
| |PDF (1616 KB) |  Media

Decomposition via scalarization is a basic concept for multiobjective optimization. The weighted sum (WS) method, a frequently used scalarizing method in decomposition-based evolutionary multiobjective (EMO) algorithms, has good features such as computationally easy and high search efficiency, compared to other scalarizing methods. However, it is often criticized by the loss of effect on nonconvex... View full abstract»

• ### On the Performance Degradation of Dominance-Based Evolutionary Algorithms in Many-Objective Optimization

Publication Year: 2018, Page(s):19 - 31
| |PDF (826 KB)

In the last decade, it has become apparent that the performance of Pareto-dominance-based evolutionary multiobjective optimization algorithms degrades as the number of objective functions of the problem, given by ${n}$ , grows. This performance degradation has been the subject of several studies in the last years, but the exac... View full abstract»

• ### Particle Swarm Optimization With a Balanceable Fitness Estimation for Many-Objective Optimization Problems

Publication Year: 2018, Page(s):32 - 46
| |PDF (3096 KB) |  Media

Recently, it was found that most multiobjective particle swarm optimizers (MOPSOs) perform poorly when tackling many-objective optimization problems (MaOPs). This is mainly because the loss of selection pressure that occurs when updating the swarm. The number of nondominated individuals is substantially increased and the diversity maintenance mechanisms in MOPSOs always guide the particles to expl... View full abstract»

• ### A Set-Based Genetic Algorithm for Interval Many-Objective Optimization Problems

Publication Year: 2018, Page(s):47 - 60
| |PDF (1073 KB)

Interval many-objective optimization problems (IMaOPs), involving more than three objectives and at least one subjected to interval uncertainty, are ubiquitous in real-world applications. However, there have been very few effective methods for solving these problems. In this paper, we proposed a set-based genetic algorithm to effectively solve them. The original optimization problem was first tran... View full abstract»

• ### Multiline Distance Minimization: A Visualized Many-Objective Test Problem Suite

Publication Year: 2018, Page(s):61 - 78
| |PDF (2381 KB) |  Media

Studying the search behavior of evolutionary many-objective optimization is an important, but challenging issue. Existing studies rely mainly on the use of performance indicators which, however, not only encounter increasing difficulties with the number of objectives, but also fail to provide the visual information of the evolutionary search. In this paper, we propose a class of scalable test prob... View full abstract»

• ### A Scalability Study of Many-Objective Optimization Algorithms

Publication Year: 2018, Page(s):79 - 96
Cited by:  Papers (1)
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Over the past few decades, a plethora of computational intelligence algorithms designed to solve multiobjective problems have been proposed in the literature. Unfortunately, it has been shown that a large majority of these optimizers experience performance degradation when tasked with solving problems possessing more than three objectives, referred to as many-objective problems (MaOPs). The downfa... View full abstract»

• ### A Decision Variable Clustering-Based Evolutionary Algorithm for Large-Scale Many-Objective Optimization

Publication Year: 2018, Page(s):97 - 112
Cited by:  Papers (4)
| |PDF (1927 KB) |  Media

The current literature of evolutionary many-objective optimization is merely focused on the scalability to the number of objectives, while little work has considered the scalability to the number of decision variables. Nevertheless, many real-world problems can involve both many objectives and large-scale decision variables. To tackle such large-scale many-objective optimization problems (MaOPs), ... View full abstract»

• ### An Energy Efficient Ant Colony System for Virtual Machine Placement in Cloud Computing

Publication Year: 2018, Page(s):113 - 128
| |PDF (2339 KB) |  Media

Virtual machine placement (VMP) and energy efficiency are significant topics in cloud computing research. In this paper, evolutionary computing is applied to VMP to minimize the number of active physical servers, so as to schedule underutilized servers to save energy. Inspired by the promising performance of the ant colony system (ACS) algorithm for combinatorial problems, an ACS-based approach is... View full abstract»

• ### A Surrogate-Assisted Reference Vector Guided Evolutionary Algorithm for Computationally Expensive Many-Objective Optimization

Publication Year: 2018, Page(s):129 - 142
Cited by:  Papers (2)
| |PDF (1471 KB) |  Media

We propose a surrogate-assisted reference vector guided evolutionary algorithm (EA) for computationally expensive optimization problems with more than three objectives. The proposed algorithm is based on a recently developed EA for many-objective optimization that relies on a set of adaptive reference vectors for selection. The proposed surrogate-assisted EA (SAEA) uses Kriging to approximate each... View full abstract»

• ### Turning High-Dimensional Optimization Into Computationally Expensive Optimization

Publication Year: 2018, Page(s):143 - 156
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Divide-and-conquer (DC) is conceptually well suited to deal with high-dimensional optimization problems by decomposing the original problem into multiple low-dimensional subproblems, and tackling them separately. Nevertheless, the dimensionality mismatch between the original problem and subproblems makes it nontrivial to precisely assess the quality of a candidate solution to a subproblem, which h... View full abstract»

• ### Dynamic Multiobjectives Optimization With a Changing Number of Objectives

Publication Year: 2018, Page(s):157 - 171
| |PDF (1372 KB) |  Media

Existing studies on dynamic multiobjective optimization (DMO) focus on problems with time-dependent objective functions, while the ones with a changing number of objectives have rarely been considered in the literature. Instead of changing the shape or position of the Pareto-optimal front/set (PF/PS) when having time-dependent objective functions, increasing or decreasing the number of objectives ... View full abstract»

• ### IEEE Congress on Evolutionary Computation

Publication Year: 2018, Page(s): 172
| |PDF (645 KB)
• ### IEEE Transactions on Evolutionary Computation Society Information

Publication Year: 2018, Page(s): C3
| |PDF (91 KB)
• ### IEEE Transactions on Evolutionary Computation information for authors

Publication Year: 2018, Page(s): C4
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## 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.

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## 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