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Evolutionary Computation, IEEE Transactions on

Issue 4 • Date Aug. 2001

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Displaying Results 1 - 12 of 12
  • Evolutionary robotics: the biology, intelligence, and technology of self-organizing machines [Book Review]

    Publication Year: 2001 , Page(s): 429 - 430
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    Freely Available from IEEE
  • Parallel hybrid method for SAT that couples genetic algorithms and local search

    Publication Year: 2001 , Page(s): 323 - 334
    Cited by:  Papers (14)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (248 KB) |  | HTML iconHTML  

    A parallel hybrid method for solving the satisfiability (SAT) problem that combines cellular genetic algorithms (GAs) and the random walk SAT (WSAT) strategy of greedy SAT (GSAT) is presented. The method, called cellular genetic WSAT (CGWSAT), uses a cellular GA to perform a global search from a random initial population of candidate solutions and a local selective generation of new strings. The global search is then specialized in local search by adopting the WSAT strategy. A main characteristic of the method is that it indirectly provides a parallel implementation of WSAT when the probability of crossover is set to zero. CGWSAT has been implemented on a Meiko CS-2 parallel machine using a 2D cellular automaton as a parallel computation model. The algorithm has been tested on randomly generated problems and some classes of problems from the DIMACS and SATLIB test set View full abstract»

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  • Modeling crossover-induced linkage in genetic algorithms

    Publication Year: 2001 , Page(s): 376 - 387
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (240 KB) |  | HTML iconHTML  

    The dynamics of a genetic algorithm undergoing ranking selection, mutation, and two-point crossover for the ones-counting problem is studied using a statistical mechanics approach. This approach has been used previously to study this problem, but with uniform crossover. Two-point crossover induces additional linkage between nearby loci, which changes the dynamics significantly. To account for this linkage, the evolution of the autocorrelation function is incorporated into a model of the dynamics. This complicates the analysis and requires several additional approximations to be made. However, the model we derive is shown to capture the main features of the dynamics and is in good agreement with simulations View full abstract»

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  • Evolving an expert checkers playing program without using human expertise

    Publication Year: 2001 , Page(s): 422 - 428
    Cited by:  Papers (70)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (288 KB) |  | HTML iconHTML  

    An evolutionary algorithm has taught itself how to play the game of checkers without using features that would normally require human expertise. Using only the raw positions of pieces on the board and the piece differential, the evolutionary program optimized artificial neural networks to evaluate alternative positions in the game. Over the course of several hundred generations, the program taught itself to play at a level that is competitive with human experts (one level below human masters). This was verified by playing the best evolved neural network against 165 human players on an Internet gaming zone. The neural network's performance earned a rating that was better than 99.61% of all registered players at the Website. Control experiments between the best evolved neural network and a program that relies on material advantage indicate the superiority of the neural network both at equal levels of look ahead and CPU time. The results suggest that the principles of Darwinian evolution may he usefully applied to solving problems that have not yet been solved by human expertise View full abstract»

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  • Grammatical evolution

    Publication Year: 2001 , Page(s): 349 - 358
    Cited by:  Papers (94)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (172 KB) |  | HTML iconHTML  

    We present grammatical evolution, an evolutionary algorithm that can evolve complete programs in an arbitrary language using a variable-length binary string. The binary genome determines which production rules in a Backus-Naur form grammar definition are used in a genotype-to-phenotype mapping process to a program. We demonstrate how expressions and programs of arbitrary complexity may be evolved and compare its performance to genetic programming View full abstract»

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  • Parameter optimization of an on-chip voltage reference circuit using evolutionary programming

    Publication Year: 2001 , Page(s): 414 - 421
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (184 KB) |  | HTML iconHTML  

    This paper presents an application of evolutionary programming to parameter optimization in the design of a voltage reference circuit. Designing circuits consists of two steps: topological design and parameter determination. After designing a topology suitable for the circuit, the designer selects an appropriate value for each circuit element from a circuit analysis and his experience. This step is difficult and time consuming because the designer must consider many factors simultaneously. As more precise circuits are required, parameter optimization becomes more complex. The voltage reference circuit, which requires a precise reference voltage independent of power fluctuation and temperature change, is such an example. In this paper, evolutionary programming is used as an effective method of finding good parameter values for the elements of the voltage reference circuit. Simulation results show that this method provides good performance and can be used as an effective method for circuit design View full abstract»

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  • Applying an evolutionary algorithm to telecommunication network design

    Publication Year: 2001 , Page(s): 309 - 322
    Cited by:  Papers (15)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (368 KB) |  | HTML iconHTML  

    This paper deals with the application of evolutionary computation to telecommunication network design. Design of a two-layer network is considered, where the upper-layer (UL) network uses resources of the lower-layer (LL) network. UL links determine demands for the LL and are implemented using LL paths (admissible paths). Within a fixed LL network topology, given the demands and admissible paths, we aim to find the LL link capacities for implementing the UL links, minimizing the cost of the LL. Robust design issues are also taken into consideration, allowing for failure of a certain part of the LL and postulating that, after some re-allocation in the LL, demands are still realized to an assumed extent. An algorithm based on an evolutionary technique is introduced, with problem-specific genetic operators to improve computing efficiency. A theoretical study of properties of the operators is made and several experiments are performed to tune the parameters of the algorithm. Finally, its performance is compared with other design techniques, including integer programming View full abstract»

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  • Regularization approach to inductive genetic programming

    Publication Year: 2001 , Page(s): 359 - 375
    Cited by:  Papers (15)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (328 KB) |  | HTML iconHTML  

    This paper presents an approach to regularization of inductive genetic programming tuned for learning polynomials. The objective is to achieve optimal evolutionary performance when searching high-order multivariate polynomials represented as tree structures. We show how to improve the genetic programming of polynomials by balancing its statistical bias with its variance. Bias reduction is achieved by employing a set of basis polynomials in the tree nodes for better agreement with the examples. Since this often leads to over-fitting, such tendencies are counteracted by decreasing the variance through regularization of the fitness function. We demonstrate that this balance facilitates the search as well as enables discovery of parsimonious, accurate, and predictive polynomials. The experimental results given show that this regularization approach outperforms traditional genetic programming on benchmark data mining and practical time-series prediction tasks View full abstract»

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  • Evolutionary pattern search algorithms for unconstrained and linearly constrained optimization

    Publication Year: 2001 , Page(s): 388 - 397
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (228 KB) |  | HTML iconHTML  

    We describe a convergence theory for evolutionary pattern search algorithms (EPSA) on a broad class of unconstrained and linearly constrained problems. EPSA adaptively modify the step size of the mutation operator in response to the success of previous optimization steps. The design of EPSA is inspired by recent analyzes of pattern search methods. Our analysis significantly extends the previous convergence theory for EPSA. Our analysis applies to a broader class of EPSA and it applies to problems that are nonsmooth, have unbounded objective functions, and are linearly constrained. Further, we describe a modest change to the algorithmic framework of EPSA for which a nonprobabilistic convergence theory applies. These analyses are also noteworthy because they are considerably simpler than previous analyses of EPSA View full abstract»

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  • GAVEL - a new tool for genetic algorithm visualization

    Publication Year: 2001 , Page(s): 335 - 348
    Cited by:  Papers (15)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (344 KB) |  | HTML iconHTML  

    This paper surveys the state of the art in evolutionary algorithm visualization and describes a new tool called GAVEL. It provides a means to examine in a genetic algorithm (GA) how crossover and mutation operations assembled the final result, where each of the alleles came from, and a way to trace the history of user-selected sets of alleles. A visualization tool of this kind can be very useful in choosing operators and parameters and in analyzing how and, indeed, whether or not a GA works. We describe the new tool and illustrate some of the benefits that can be gained from using it with reference to three different problems: a timetabling problem, a job-shop scheduling problem, and Goldberg and Horn's long-path problem. We also compare the tool to other available visualization tools, pointing out those features which are novel and identifying complementary features in other tools View full abstract»

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  • Self-adaptive mutations may lead to premature convergence

    Publication Year: 2001 , Page(s): 410 - 414
    Cited by:  Papers (30)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (168 KB) |  | HTML iconHTML  

    Self-adaptive mutations are known to endow evolutionary algorithms (EA) with the ability of locating local optima quickly and accurately, whereas it was unknown whether these local optima are finally global optima provided that the EA runs long enough. In order to answer this question, it is assumed that the (1+1)-EA with self-adaptation is located in the vicinity P of a local solution with objective function value ε. In order to exhibit convergence to the global optimum with probability one, the EA must generate an offspring that is an element of the lower level set S containing all solutions (including a global one) with objective function value less than ε. In case of multimodal objective functions, these sets P and S are generally not adjacent, i.e., min{||x-y||:x∈P, y∈S}>0, so that the EA has to surmount the barrier of solutions with objective function values larger than ε by a lucky mutation. It will be proven that the probability of this event is less than one even under an infinite time horizon. This result implies that the EA can get stuck at a nonglobal optimum with positive probability. Some ideas of how to avoid this problem are discussed as well View full abstract»

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  • Dynamics of infinite populations evolving in a landscape of uni and bimodal fitness functions

    Publication Year: 2001 , Page(s): 398 - 409
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (308 KB) |  | HTML iconHTML  

    It is known that the distribution of a population evolving in a landscape of a single adaptive hill is unimodal and centered at the optimum. Evolution of a population in a landscape of two adaptive hills is a more interesting example with consequences to theoretical interpretations and applications in optimization. Its formal analysis is not a trivial task. This paper considers theoretical aspects of evolution. A study of a very simple model of asexual phenotypic evolution is presented under assumptions of infinite populations and a one-dimensional search space. For an infinite population, the evolution of the population is equivalent to the evolution of a density function describing distribution of trials with a given fitness. The evolution of the density distributions is analyzed as the evolution of density parameters, means and variances, in the landscapes of unimodal and bimodal fitness functions. As a result, discrete-time recursive equations on parameters of density distribution in the next generation are obtained based on the parameters of the current generation. Of particular interest is the location of the mean of the stationary distribution and the dynamics of crossing a saddle between optima of the bimodal fitness function. Theoretical considerations are supported by simulations. The evolutionary process is able to localize the global optimum and to pass through saddles between optima. In particular, it is demonstrated that under certain conditions, the equilibrium distribution of traits can be multimodal View full abstract»

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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