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IEEE Transactions on Evolutionary Computation

Issue 1 • Feb. 2017

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Displaying Results 1 - 16 of 16
  • Table of contents

    Publication Year: 2017, Page(s): C1
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  • IEEE Transactions on Evolutionary Computation publication information

    Publication Year: 2017, Page(s): C2
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  • Necessary and Sufficient Conditions for Surrogate Functions of Pareto Frontiers and Their Synthesis Using Gaussian Processes

    Publication Year: 2017, Page(s):1 - 13
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (684 KB) | HTML iconHTML

    This paper introduces necessary and sufficient conditions that surrogate functions must satisfy to properly define frontiers of nondominated solutions in multiobjective optimization (MOO) problems. These new conditions work directly on the objective space, and thus are agnostic about how the solutions are evaluated. Therefore, real objectives or user-designed objectives' surrogates are allowed, op... View full abstract»

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  • Continuous Dynamic Constrained Optimization With Ensemble of Locating and Tracking Feasible Regions Strategies

    Publication Year: 2017, Page(s):14 - 33
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1858 KB) | HTML iconHTML

    Dynamic constrained optimization problems (DCOPs) are difficult to solve because both objective function and constraints can vary with time. Although DCOPs have drawn attention in recent years, little work has been performed to solve DCOPs with multiple dynamic feasible regions from the perspective of locating and tracking multiple feasible regions in parallel. Moreover, few benchmarks have been p... View full abstract»

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  • Ranking Vectors by Means of the Dominance Degree Matrix

    Publication Year: 2017, Page(s):34 - 51
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2095 KB) | HTML iconHTML

    In multi-/many-objective evolutionary algorithms (MOEAs), there are varieties of vector ranking schemes, including nondominated sorting, dominance counting, and so on. Usually, these vector ranking schemes in the classical MOEAs are of high computational complexity. Thus, in recent years, many researchers put emphasis on the further improvement of the computational complexity of the vector ranking... View full abstract»

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  • Efficient Use of Partially Converged Simulations in Evolutionary Optimization

    Publication Year: 2017, Page(s):52 - 64
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2808 KB) | HTML iconHTML

    For many real-world optimization problems, evaluating a solution involves running a computationally expensive simulation model. This makes it challenging to use evolutionary algorithms that usually have to evaluate thousands of solutions before converging. On the other hand, in many cases, even a prematurely stopped run of the simulation may serve as a cheaper, albeit less accurate (low fidelity),... View full abstract»

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  • A Steady-State and Generational Evolutionary Algorithm for Dynamic Multiobjective Optimization

    Publication Year: 2017, Page(s):65 - 82
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (4362 KB) | HTML iconHTML Multimedia Media

    This paper presents a new algorithm, called steady-state and generational evolutionary algorithm, which combines the fast and steadily tracking ability of steady-state algorithms and good diversity preservation of generational algorithms, for handling dynamic multiobjective optimization. Unlike most existing approaches for dynamic multiobjective optimization, the proposed algorithm detects environ... View full abstract»

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  • Automatically Evolving Rotation-Invariant Texture Image Descriptors by Genetic Programming

    Publication Year: 2017, Page(s):83 - 101
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3933 KB) | HTML iconHTML

    In computer vision, training a model that performs classification effectively is highly dependent on the extracted features, and the number of training instances. Conventionally, feature detection and extraction are performed by a domain expert who, in many cases, is expensive to employ and hard to find. Therefore, image descriptors have emerged to automate these tasks. However, designing an image... View full abstract»

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  • Structured Memetic Automation for Online Human-Like Social Behavior Learning

    Publication Year: 2017, Page(s):102 - 115
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1845 KB) | HTML iconHTML

    Meme automaton is an adaptive entity that autonomously acquires an increasing level of capability and intelligence through embedded memes evolving independently or via social interactions. This paper begins a study on memetic multiagent system (MeMAS) toward human-like social agents with memetic automaton. We introduce a potentially rich meme-inspired design and operational model, with Darwin's th... View full abstract»

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  • Artificial Immune System With Local Feature Selection for Short-Term Load Forecasting

    Publication Year: 2017, Page(s):116 - 130
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2390 KB)

    In this paper, a new forecasting model based on artificial immune system (AIS) is proposed. The model is used for short-term electrical load forecasting as an example of forecasting time series with multiple seasonal cycles. Artificial immune system learns to recognize antigens (AGs) representing two fragments of the time series: 1) fragment preceding the forecast (input vector) and 2) forecasted ... View full abstract»

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  • A Vector Angle-Based Evolutionary Algorithm for Unconstrained Many-Objective Optimization

    Publication Year: 2017, Page(s):131 - 152
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3638 KB) | HTML iconHTML

    Taking both convergence and diversity into consideration, this paper suggests a vector angle-based evolutionary algorithm for unconstrained (with box constraints only) many-objective optimization problems. In the proposed algorithm, the maximum-vector-angle-first principle is used in the environmental selection to guarantee the wideness and uniformity of the solution set. With the help of the wors... View full abstract»

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  • The Effect of Information Utilization: Introducing a Novel Guiding Spark in the Fireworks Algorithm

    Publication Year: 2017, Page(s):153 - 166
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1801 KB) | HTML iconHTML

    The fireworks algorithm (FWA) is a competitive swarm intelligence algorithm which has been shown to be very useful in many applications. In this paper, a novel guiding spark (GS) is introduced to further improve its performance by enhancing the information utilization in the FWA. The idea is to use the objective function's information acquired by explosion sparks to construct a guiding vector (GV)... View full abstract»

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  • Member Get-A-Member (MGM) Program

    Publication Year: 2017, Page(s): 167
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  • IEEE World Congress on Computational Intelligence

    Publication Year: 2017, Page(s): 168
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  • IEEE Transactions on Evolutionary Computation Society Information

    Publication Year: 2017, Page(s): C3
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  • IEEE Transactions on Evolutionary Computation information for authors

    Publication Year: 2017, 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.
 

Full Aims & Scope

Meet Our Editors

Editor-in-Chief

Dr. Kay Chen Tan (IEEE Fellow)

Department of Electrical and Computer Engineering

National University of Singapore

Singapore 117583

Email: eletankc@nus.edu.sg

Website: http://vlab.ee.nus.edu.sg/~kctan