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TOC Alert for Publication# 4235 2016December 05<![CDATA[Table of contents]]>206C1C1145<![CDATA[IEEE Transactions on Evolutionary Computation publication information]]>206C2C270<![CDATA[Decomposition-Based Algorithms Using Pareto Adaptive Scalarizing Methods]]> methods, and shows that the value is crucial to balance the selective pressure toward the Pareto optimal and the algorithm robustness to Pareto optimal front (PF) geometries. It demonstrates that an method that can maximize the search ability of a decomposition-based algorithm exists and guarantees that, given some weight, any solution along the PF can be found. Moreover, a simple yet effective method called Pareto adaptive scalarizing (PaS) approximation is proposed to approximate the optimal value. In order to demonstrate the effectiveness of PaS, we incorporate PaS into a state-of-the-art decomposition-based algorithm, i.e., multiobjective evolutionary algorithm based on decomposition (MOEA/D), and compare the resultant MOEA/D-PaS with some other MOEA/D variants on a set of problems with different PF geometries and up to seven conflicting objectives. Experimental results demonstrate that the PaS is effective.]]>2068218372144<![CDATA[Adaptive Multisubpopulation Competition and Multiniche Crowding-Based Memetic Algorithm for Automatic Data Clustering]]>2068388581874<![CDATA[A Cooperative Control Framework for a Collective Decision on Movement Behaviors of Particles]]>2068598733342<![CDATA[Cooperative Co-Evolutionary Module Identification With Application to Cancer Disease Module Discovery]]>2068748912522<![CDATA[An Effective Hybrid Memetic Algorithm for the Minimum Weight Dominating Set Problem]]>2068929071587<![CDATA[Memetic Search for the Generalized Quadratic Multiple Knapsack Problem]]>2069089231447<![CDATA[Stochastic Ranking Algorithm for Many-Objective Optimization Based on Multiple Indicators]]>2069249382883<![CDATA[Data-Driven Surrogate-Assisted Multiobjective Evolutionary Optimization of a Trauma System]]>2069399521607<![CDATA[Accuracy-Based Learning Classifier Systems for Multistep Reinforcement Learning: A Fuzzy Logic Approach to Handling Continuous Inputs and Learning Continuous Actions]]> -learning like credit assignment methods to continuous spaces. To enable direct learning of stochastic strategies for action selection, we have also proposed to use a new fuzzy logic system with stochastic action outputs. Moreover, fine-grained learning of fuzzy rules has been achieved effectively in our algorithm by using a natural gradient learning method. It is the first time that these techniques are utilized substantially in any accuracy-based LFCSs. Meanwhile, in comparison with several recently proposed learning algorithms, our algorithm is shown to perform highly competitively on four benchmark learning problems and a robotics problem. The practical usefulness of our algorithm is also demonstrated by improving the performance of a wireless body area network.]]>2069539712649<![CDATA[Minimum Manhattan Distance Approach to Multiple Criteria Decision Making in Multiobjective Optimization Problems]]>a posteriori decision making, i.e., determining the final solution after a set of Pareto optimal solutions is available, the proposed MMD approach can be combined with them to form a powerful solution method of solving MOPs. Furthermore, the approach enables scalable definitions of the knee and knee solutions.]]>2069729851903<![CDATA[Acknowledgment to Reviewer—2016]]>20698698955<![CDATA[2017: Congress on Evolutionary Computation]]>206990990911<![CDATA[Introducing IEEE Collabratec]]>2069919912129<![CDATA[Member Get-A-Member (MGM) Program]]>2069929923407<![CDATA[IEEE Transactions on Evolutionary Computation Society Information]]>206C3C3146<![CDATA[IEEE Transactions on Evolutionary Computation information for authors]]>206C4C468