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Evolutionary Computation, IEEE Transactions on

Issue 3 • Date June 2007

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Displaying Results 1 - 19 of 19
  • Table of contents

    Publication Year: 2007 , Page(s): C1
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  • IEEE Transactions on Evolutionary Computation publication information

    Publication Year: 2007 , Page(s): C2
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  • In Memoriam: Dr. Lawrence J. Fogel

    Publication Year: 2007 , Page(s): 289
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (251 KB)  

    Xin Yao recounts the life and career of Dr. Lawrence J. Fogel, a pioneer in evolutionary computation. View full abstract»

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  • In Memoriam: Dr. Lawrence J. Fogel

    Publication Year: 2007 , Page(s): 290 - 293
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  • Imperfect Evolutionary Systems

    Publication Year: 2007 , Page(s): 294 - 307
    Cited by:  Papers (10)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (703 KB) |  | HTML iconHTML  

    In this paper, we propose a change from a perfect paradigm to an imperfect paradigm in evolving intelligent systems. An imperfect evolutionary system (IES) is introduced as a new approach in an attempt to solve the problem of an intelligent system adapting to new challenges from its imperfect environment, with an emphasis on the incompleteness and continuity of intelligence. We define an IES as a system where intelligent individuals optimize their own utility, with the available resources, while adapting themselves to the new challenges from an evolving and imperfect environment. An individual and social learning paradigm (ISP) is presented as a general framework for developing IESs. A practical implementation of the ISP framework, an imperfect evolutionary market, is described. Through experimentation, we demonstrate the absorption of new information from an imperfect environment by artificial stock traders and the dissemination of new knowledge within an imperfect evolutionary market. Parameter sensitivity of the ISP framework is also studied by employing different levels of individual and social learning View full abstract»

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  • Learning Finite-State Transducers: Evolution Versus Heuristic State Merging

    Publication Year: 2007 , Page(s): 308 - 325
    Cited by:  Papers (3)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (976 KB) |  | HTML iconHTML  

    Finite-state transducers (FSTs) are finite-state machines (FSMs) that map strings in a source domain into strings in a target domain. While there are many reports in the literature of evolving FSMs, there has been much less work on evolving FSTs. In particular, the fitness functions required for evolving FSTs are generally different from those used for FSMs. In this paper, three string distance-based fitness functions are evaluated, in order of increasing computational complexity: string equality, Hamming distance, and edit distance. The fitness-distance correlation (FDC) and evolutionary performance of each fitness function is analyzed when used within a random mutation hill-climber (RMHC). Edit distance has the strongest FDC and also provides the best evolutionary performance, in that it is more likely to find the target FST within a given number of fitness function evaluations. Edit distance is also the most expensive to compute, but in most cases this extra computation is more than justified by its performance. The RMHC was compared with the best known heuristic method for learning FSTs, the onward subsequential transducer inference algorithm (OSTIA). On noise-free data, the RMHC performs best on problems with sparse training sets and small target machines. The RMHC and OSTIA offer similar performance for large target machines and denser data sets. When noise-corrupted data is used for training, the RMHC still performs well, while OSTIA performs poorly given even small amounts of noise. The RMHC is also shown to outperform a genetic algorithm. Hence, for certain classes of FST induction problem, the RMHC presented in this paper offers the best performance of any known algorithm View full abstract»

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  • Clustering-Based Adaptive Crossover and Mutation Probabilities for Genetic Algorithms

    Publication Year: 2007 , Page(s): 326 - 335
    Cited by:  Papers (42)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (796 KB) |  | HTML iconHTML  

    Research into adjusting the probabilities of crossover and mutation pm in genetic algorithms (GAs) is one of the most significant and promising areas in evolutionary computation. px and pm greatly determine whether the algorithm will find a near-optimum solution or whether it will find a solution efficiently. Instead of using fixed values of px and pm , this paper presents the use of fuzzy logic to adaptively adjust the values of px and pm in GA. By applying the K-means algorithm, distribution of the population in the search space is clustered in each generation. A fuzzy system is used to adjust the values of px and pm. It is based on considering the relative size of the cluster containing the best chromosome and the one containing the worst chromosome. The proposed method has been applied to optimize a buck regulator that requires satisfying several static and dynamic operational requirements. The optimized circuit component values, the regulator's performance, and the convergence rate in the training are favorably compared with the GA using fixed values of px and pm. The effectiveness of the fuzzy-controlled crossover and mutation probabilities is also demonstrated by optimizing eight multidimensional mathematical functions View full abstract»

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  • An Evolutionary Algorithm-Based Approach to Automated Design of Analog and RF Circuits Using Adaptive Normalized Cost Functions

    Publication Year: 2007 , Page(s): 336 - 353
    Cited by:  Papers (11)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1310 KB) |  | HTML iconHTML  

    Typical analog and radio frequency (RF) circuit sizing optimization problems are computationally hard and require the handling of several conflicting cost criteria. Many researchers have used sequential stochastic refinement methods to solve them, where the different cost criteria can either be combined into a single-objective function to find a unique solution, or they can be handled by multiobjective optimization methods to produce tradeoff solutions on the Pareto front. This paper presents a method for solving the problem by the former approach. We propose a systematic method for incorporating the tradeoff wisdom inspired by the circuit domain knowledge in the formulation of the composite cost function. Key issues have been identified and the problem has been divided into two parts: a) normalization of objective functions and b) assignment of weights to objectives in the cost function. A nonlinear, parameterized normalization strategy has been proposed and has been shown to be better than traditional linear normalization functions. Further, the designers' problem specific knowledge is assembled in the form of a partially ordered set, which is used to construct a hierarchical cost graph for the problem. The scalar cost function is calculated based on this graph. Adaptive mechanisms have been introduced to dynamically change the structure of the graph to improve the chances of reaching the near-optimal solution. A correlated double sampling offset-compensated switched capacitor analog integrator circuit and an RF low-noise amplifier in an industry-standard 0.18mum CMOS technology have been chosen for experimental study. Optimization results have been shown for both the traditional and the proposed methods. The results show significant improvement in both the chosen design problems View full abstract»

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  • An Investigation on Noisy Environments in Evolutionary Multiobjective Optimization

    Publication Year: 2007 , Page(s): 354 - 381
    Cited by:  Papers (26)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2804 KB) |  | HTML iconHTML  

    In addition to satisfying several competing objectives, many real-world applications are also characterized by a certain degree of noise, manifesting itself in the form of signal distortion or uncertain information. In this paper, extensive studies are carried out to examine the impact of noisy environments in evolutionary multiobjective optimization. Three noise-handling features are then proposed based upon the analysis of empirical results, including an experiential learning directed perturbation operator that adapts the magnitude and direction of variation according to past experiences for fast convergence, a gene adaptation selection strategy that helps the evolutionary search in escaping from local optima or premature convergence, and a possibilistic archiving model based on the concept of possibility and necessity measures to deal with problem of uncertainties. In addition, the performances of various multiobjective evolutionary algorithms in noisy environments, as well as the robustness and effectiveness of the proposed features are examined based upon five benchmark problems characterized by different difficulties in local optimality, nonuniformity, discontinuity, and nonconvexity View full abstract»

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  • Emerging Cooperation With Minimal Effort: Rewarding Over Mimicking

    Publication Year: 2007 , Page(s): 382 - 396
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (853 KB) |  | HTML iconHTML  

    This paper compares supervised and unsupervised learning mechanisms for the emergence of cooperative multiagent spatial coordination using a top-down approach. By observing the global performance of a group of homogeneous agents-supported by a nonglobal knowledge of their environment-we attempt to extract information about the minimum size of the agent neurocontroller and the type of learning mechanism that collectively generate high-performing and robust behaviors with minimal computational effort. Consequently, a methodology for obtaining controllers of minimal size is introduced and a comparative study between supervised and unsupervised learning mechanisms for the generation of successful collective behaviors is presented. We have developed a prototype simulated world for our studies. This case study is primarily a computer games inspired world but its main features are also biologically plausible. The two specific tasks that the agents are tested in are the competing strategies of obstacle-avoidance and target-achievement. We demonstrate that cooperative behavior among agents, which is supported only by limited communication, appears to be necessary for the problem's efficient solution and that learning by rewarding the behavior of agent groups constitutes a more efficient and computationally preferred generic approach than supervised learning approaches in such complex multiagent worlds View full abstract»

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  • Finding Feasible Timetables Using Group-Based Operators

    Publication Year: 2007 , Page(s): 397 - 413
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (725 KB) |  | HTML iconHTML  

    This paper describes the applicability of the so-called "grouping genetic algorithm" to a well-known version of the university course timetabling problem. We note that there are, in fact, various scaling up issues surrounding this sort of algorithm and, in particular, see that it behaves in quite different ways with different sized problem instances. As a by-product of these investigations, we introduce a method for measuring population diversities and distances between individuals with the grouping representation. We also look at how such an algorithm might be improved: first, through the introduction of a number of different fitness functions and, second, through the use of an additional stochastic local-search operator (making in effect a grouping memetic algorithm). In many cases, we notice that the best results are actually returned when the grouping genetic operators are removed altogether, thus highlighting many of the issues that are raised in the study View full abstract»

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  • Interactive Evolutionary Computation-Based Hearing Aid Fitting

    Publication Year: 2007 , Page(s): 414 - 427
    Cited by:  Papers (10)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1526 KB) |  | HTML iconHTML  

    An interactive evolutionary computation (EC) fitting method is proposed that applies interactive EC to hearing aid fitting and the method is evaluated using a hearing aid simulator with human subjects. The advantages of the method are that it can optimize a hearing aid based on how a user hears and that it realizes whatever+whenever+wherever (W3) fitting. Conventional fitting methods are based on the user's partially measured auditory characteristics, the fitting engineer's experience, and the user's linguistic explanation of his or her hearing. These conventional methods, therefore, suffer from the fundamental problem that no one can experience another person's hearing. However, as interactive EC fitting uses EC to optimize a hearing aid based on the user's evaluation of his or her hearing, this problem is addressed. Moreover, whereas conventional fitting methods must use pure tones and bandpass noise for measuring hearing characteristics, our proposed method has no such restrictions. Evaluating the proposed method using speech sources, we demonstrate that it shows significantly better results than either the conventional method or the unprocessed case in terms of both speech intelligibility and speech quality. We also evaluate our method using musical sources, unusable for evaluation by conventional methods, and demonstrate that its sound quality is preferable to the unprocessed case View full abstract»

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  • IEEE Congress on Evolutionary Computation

    Publication Year: 2007 , Page(s): 428
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  • 2008 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology

    Publication Year: 2007 , Page(s): 429
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  • Pre-submission professional editing services now available

    Publication Year: 2007 , Page(s): 430
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    Publication Year: 2007 , Page(s): 431
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  • Order form for reprints

    Publication Year: 2007 , Page(s): 432
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  • IEEE Computational Intelligence Society Information

    Publication Year: 2007 , Page(s): C3
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  • IEEE Transactions on Evolutionary Computation Information for authors

    Publication Year: 2007 , 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