<![CDATA[ IEEE Transactions on Evolutionary Computation - new TOC ]]>
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TOC Alert for Publication# 4235 2018February 15<![CDATA[Table of contents]]>221C1C1151<![CDATA[IEEE Transactions on Evolutionary Computation publication information]]>221C2C271<![CDATA[Guest Editorial Evolutionary Many-Objective Optimization]]>2211271<![CDATA[Localized Weighted Sum Method for Many-Objective Optimization]]>a priori. The effectiveness of MOEA/D-LWS is demonstrated by comparing it against three variants of MOEA/D, i.e., MOEA/D using Chebyshev method, MOEA/D with an adaptive use of WS and Chebyshev method, MOEA/D with a simultaneous use of WS and Chebyshev method, and four state-of-the-art many-objective EMO algorithms, i.e., preference-inspired co-evolutionary algorithm, hypervolume-based evolutionary, $boldsymbol {theta }$ -dominance-based algorithm, and SPEA2+SDE for the WFG benchmark problems with up to seven conflicting objectives. Experimental results show that MOEA/D-LWS outperforms the comparison algorithms for most of test problems, and is a competitive algorithm for many-objective optimization.]]>2213181616<![CDATA[On the Performance Degradation of Dominance-Based Evolutionary Algorithms in Many-Objective Optimization]]>${n}$ , grows. This performance degradation has been the subject of several studies in the last years, but the exact mechanism behind this phenomenon has not been fully understood yet. This paper presents an analytical study of this phenomenon under problems with continuous variables, by a simple setup of quadratic objective functions with spherical contour curves and a symmetrical arrangement of the function minima location. Within such a setup, some analytical formulas are derived to describe the probability of the optimization progress as a function of the distance ${lambda }$ to the exact Pareto-set. A main conclusion is stated about the nature and structure of the performance degradation phenomenon in many-objective problems: when a current solution reaches a ${lambda }$ that is an order of magnitude smaller than the length of the Pareto-set, the probability of finding a new point that dominates the current one is given by a power law function of ${lambda }$ with exponent ${(n-1)}$ . The dimension of the space of decision variables has no influence on that exponent. Those results give support to a discussion about some general directions that are currently under consideration within the research community.]]>2211931826<![CDATA[Particle Swarm Optimization With a Balanceable Fitness Estimation for Many-Objective Optimization Problems]]>22132463096<![CDATA[A Set-Based Genetic Algorithm for Interval Many-Objective Optimization Problems]]>22147601073<![CDATA[Multiline Distance Minimization: A Visualized Many-Objective Test Problem Suite]]>22161782381<![CDATA[A Scalability Study of Many-Objective Optimization Algorithms]]>22179961933<![CDATA[A Decision Variable Clustering-Based Evolutionary Algorithm for Large-Scale Many-Objective Optimization]]>221971121927<![CDATA[An Energy Efficient Ant Colony System for Virtual Machine Placement in Cloud Computing]]>2211131282339<![CDATA[A Surrogate-Assisted Reference Vector Guided Evolutionary Algorithm for Computationally Expensive Many-Objective Optimization]]>2211291421471<![CDATA[Turning High-Dimensional Optimization Into Computationally Expensive Optimization]]>2211431562163<![CDATA[Dynamic Multiobjectives Optimization With a Changing Number of Objectives]]>2211571711372<![CDATA[IEEE Congress on Evolutionary Computation]]>221172172645<![CDATA[IEEE Transactions on Evolutionary Computation Society Information]]>221C3C391<![CDATA[IEEE Transactions on Evolutionary Computation information for authors]]>221C4C469