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Evolutionary Computation, IEEE Transactions on

Issue 2 • Date April 2015

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Displaying Results 1 - 17 of 17
  • Table of contents

    Publication Year: 2015 , Page(s): C1
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  • IEEE Transactions on Evolutionary Computation publication information

    Publication Year: 2015 , Page(s): C2
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  • Tikhonov Regularization as a Complexity Measure in Multiobjective Genetic Programming

    Publication Year: 2015 , Page(s): 157 - 166
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1334 KB) |  | HTML iconHTML  

    In this paper, we propose the use of Tikhonov regularization in conjunction with node count as a general complexity measure in multiobjective genetic programming. We demonstrate that employing this general complexity yields mean squared test error measures over a range of regression problems, which are typically superior to those from conventional node count (but never statistically worse). We also analyze the reason that our new method outperforms the conventional complexity measure and conclude that it forms a decision mechanism that balances both syntactic and semantic information. View full abstract»

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  • Parameter Control in Evolutionary Algorithms: Trends and Challenges

    Publication Year: 2015 , Page(s): 167 - 187
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (615 KB) |  | HTML iconHTML  

    More than a decade after the first extensive overview on parameter control, we revisit the field and present a survey of the state-of-the-art. We briefly summarize the development of the field and discuss existing work related to each major parameter or component of an evolutionary algorithm. Based on this overview, we observe trends in the area, identify some (methodological) shortcomings, and give recommendations for future research. View full abstract»

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  • Convex Hull-Based Multiobjective Genetic Programming for Maximizing Receiver Operating Characteristic Performance

    Publication Year: 2015 , Page(s): 188 - 200
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2913 KB) |  | HTML iconHTML  

    The receiver operating characteristic (ROC) is commonly used to analyze the performance of classifiers in data mining. An important topic in ROC analysis is the ROC convex hull (ROCCH), which is the least convex majorant (LCM) of the empirical ROC curve and covers potential optima for a given set of classifiers. ROCCH maximization problems have been taken as multiobjective optimization problem (MOPs) in some previous work. However, the special characteristics of ROCCH maximization problem makes it different from traditional MOPs. In this paper, the difference will be discussed in detail and a new convex hull-based multiobjective genetic programming (CH-MOGP) is proposed to solve ROCCH maximization problems. Specifically, convex hull-based without redundancy sorting (CWR-sorting) is introduced, which is an indicator-based selection scheme that aims to maximize the area under the convex hull. A novel selection procedure is also proposed based on the proposed sorting scheme. It is hypothesized that by using a tailored indicator-based selection, CH-MOGP becomes more efficient for ROC convex hull approximation than algorithms that compute all Pareto optimal points. Empirical studies are conducted to compare CH-MOGP to both existing machine learning approaches and multiobjective genetic programming (MOGP) methods with classical selection schemes. Experimental results show that CH-MOGP outperforms the other approaches significantly. View full abstract»

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  • An Efficient Approach to Nondominated Sorting for Evolutionary Multiobjective Optimization

    Publication Year: 2015 , Page(s): 201 - 213
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1880 KB) |  | HTML iconHTML  

    Evolutionary algorithms have been shown to be powerful for solving multiobjective optimization problems, in which nondominated sorting is a widely adopted technique in selection. This technique, however, can be computationally expensive, especially when the number of individuals in the population becomes large. This is mainly because in most existing nondominated sorting algorithms, a solution needs to be compared with all other solutions before it can be assigned to a front. In this paper we propose a novel, computationally efficient approach to nondominated sorting, termed efficient nondominated sort (ENS). In ENS, a solution to be assigned to a front needs to be compared only with those that have already been assigned to a front, thereby avoiding many unnecessary dominance comparisons. Based on this new approach, two nondominated sorting algorithms have been suggested. Both theoretical analysis and empirical results show that the ENS-based sorting algorithms are computationally more efficient than the state-of-the-art nondominated sorting methods. View full abstract»

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  • Probabilistic Dominance in Multiobjective Reliability-Based Optimization: Theory and Implementation

    Publication Year: 2015 , Page(s): 214 - 224
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1896 KB) |  | HTML iconHTML  

    This paper is concerned with multiobjective evolutionary optimization under uncertainty modeled through probability distributions, with a focus on reliability-based approaches. The contribution is twofold. First, an in-depth study of the notion of probability of dominance is performed, including state-of-the-art multiobjective reliability-based formulations and their numerical calculation. In particular, the notion of dominance limit state function is defined and its properties are thoroughly investigated. Second, the assessment of the probability of dominance is proposed based on a first-order reliability method tailored for Pareto dominance and incorporated into a multiobjective evolutionary algorithm through a repairing mechanism. The analysis of the numerical results on five biobjective benchmark test cases (from two up to five design variables) by means of two adapted metrics (averaged Hausdorff distance and maximum Pareto front error) demonstrates the potential of the proposed approach to reach reliable nondominated fronts within a limited number of generations. View full abstract»

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  • Visualization of Pareto Front Approximations in Evolutionary Multiobjective Optimization: A Critical Review and the Prosection Method

    Publication Year: 2015 , Page(s): 225 - 245
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (4720 KB) |  | HTML iconHTML  

    In evolutionary multiobjective optimization, it is very important to be able to visualize approximations of the Pareto front (called approximation sets) that are found by multiobjective evolutionary algorithms. While scatter plots can be used for visualizing 2-D and 3-D approximation sets, more advanced approaches are needed to handle four or more objectives. This paper presents a comprehensive review of the existing visualization methods used in evolutionary multiobjective optimization, showing their outcomes on two novel 4-D benchmark approximation sets. In addition, a visualization method that uses prosection (projection of a section) to visualize 4-D approximation sets is proposed. The method reproduces the shape, range, and distribution of vectors in the observed approximation sets well and can handle multiple large approximation sets while being robust and computationally inexpensive. Even more importantly, for some vectors, the visualization with prosections preserves the Pareto dominance relation and relative closeness to reference points. The method is analyzed theoretically and demonstrated on several approximation sets. View full abstract»

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  • Inducing Niching Behavior in Differential Evolution Through Local Information Sharing

    Publication Year: 2015 , Page(s): 246 - 263
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3530 KB) |  | HTML iconHTML  

    In practical situations, it is very often desirable to detect multiple optimally sustainable solutions of an optimization problem. The population-based evolutionary multimodal optimization algorithms can be very helpful in such cases. They detect and maintain multiple optimal solutions during the run by incorporating specialized niching operations to aid the parallel localized convergence of population members around different basins of attraction. This paper presents an improved information-sharing mechanism among the individuals of an evolutionary algorithm for inducing efficient niching behavior. The mechanism can be integrated with stochastic real-parameter optimizers relying on differential perturbation of the individuals (candidate solutions) based on the population distribution. Various real-coded genetic algorithms (GAs), particle swarm optimization (PSO), and differential evolution (DE) fit the example of such algorithms. The main problem arising from differential perturbation is the unequal attraction toward the different basins of attraction that is detrimental to the objective of parallel convergence to multiple basins of attraction. We present our study through DE algorithm owing to its highly random nature of mutation and show how population diversity is preserved by modifying the basic perturbation (mutation) scheme through the use of random individuals selected probabilistically. By integrating the proposed technique with DE framework, we present three improved versions of well-known DE-based niching methods. Through an extensive experimental analysis, a statistically significant improvement in the overall performance has been observed upon integrating of our technique with the DE-based niching methods. View full abstract»

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  • Behavior of Multiobjective Evolutionary Algorithms on Many-Objective Knapsack Problems

    Publication Year: 2015 , Page(s): 264 - 283
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (3307 KB) |  | HTML iconHTML  

    We examine the behavior of three classes of evolutionary multiobjective optimization (EMO) algorithms on many-objective knapsack problems. They are Pareto dominance-based, scalarizing function-based, and hypervolume-based algorithms. NSGA-II, MOEA/D, SMS-EMOA, and HypE are examined using knapsack problems with 2-10 objectives. Our test problems are generated by randomly specifying coefficients (i.e., profits) in objectives. We also generate other test problems by combining two objectives to create a dependent or correlated objective. Experimental results on randomly generated many-objective knapsack problems are consistent with well-known performance deterioration of Pareto dominance-based algorithms. That is, NSGA-II is outperformed by the other algorithms. However, it is also shown that NSGA-II outperforms the other algorithms when objectives are highly correlated. MOEA/D shows totally different search behavior depending on the choice of a scalarizing function and its parameter value. Some MOEA/D variants work very well only on two-objective problems while others work well on many-objective problems with 4-10 objectives. We also obtain other interesting observations such as the performance improvement by similar parent recombination and the necessity of diversity improvement for many-objective knapsack problems. View full abstract»

    Open Access
  • Toward the Coevolution of Novel Vertical-Axis Wind Turbines

    Publication Year: 2015 , Page(s): 284 - 294
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1763 KB) |  | HTML iconHTML  

    The production of renewable and sustainable energy is one of the most important challenges currently facing mankind. Wind has made an increasing contribution to the world's energy supply mix, but remains a long way from reaching its full potential. In this paper, we investigate the use of artificial evolution to design vertical-axis wind turbine prototypes that are physically instantiated and evaluated under approximated wind tunnel conditions. Initially, a conventional evolutionary algorithm is used to explore the design space of a single wind turbine and later a cooperative coevolutionary algorithm is used to explore the design space of an array of wind turbines. Artificial neural networks are used throughout as surrogate models to assist learning and found to reduce the number of fabrications required to reach a higher aerodynamic efficiency. Unlike other approaches, such as computational fluid dynamics simulations, no mathematical formulations are used and no model assumptions are made. View full abstract»

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  • On the Easiest and Hardest Fitness Functions

    Publication Year: 2015 , Page(s): 295 - 305
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (473 KB) |  | HTML iconHTML  

    The hardness of fitness functions is an important research topic in the field of evolutionary computation. In theory, this paper can help with understanding the ability of evolutionary algorithms (EAs). In practice, this paper may provide a guideline to the design of benchmarks. The aim of this paper is to answer the following research questions. Given a fitness function class, which functions are the easiest with respect to an EA? Which are the hardest? How are these functions constructed? This paper provides theoretical answers to these questions. The easiest and hardest fitness functions are constructed for an elitist (1 + 1) EA to maximize a class of fitness functions with the same optima. It is demonstrated that the unimodal functions are the easiest and deceptive functions are the hardest in terms of the time-based fitness landscape. This paper also reveals that in a fitness function class, the easiest function to one algorithm may become the hardest to another algorithm, and vice versa. View full abstract»

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  • IEEE World Congress on Computational Intelligence

    Publication Year: 2015 , Page(s): 306
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  • IEEE membership can help you reach your personal goals

    Publication Year: 2015 , Page(s): 307
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  • Expand your professional network with IEEE

    Publication Year: 2015 , Page(s): 308
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  • IEEE Computational Intelligence Society Information

    Publication Year: 2015 , Page(s): C3
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  • IEEE Transactions on Evolutionary Computation information for authors

    Publication Year: 2015 , 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

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