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Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)

16-19 July 2000

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  • Proceedings of the 2000 Congress on Evolutionary Computation

    Publication Year: 2000, Page(s):0_2 - xxvi
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    Freely Available from IEEE
  • Proceedings ASE 2000. Fifteenth IEEE International Conference on Automated Software Engineering

    Publication Year: 2000
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    Freely Available from IEEE
  • Author index

    Publication Year: 2000, Page(s):1585 - 1590
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    Freely Available from IEEE
  • Evolving difference equations to model freshwater phytoplankton

    Publication Year: 2000, Page(s):967 - 973 vol.2
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (564 KB)

    The underlying dynamics of algal species in freshwater systems are a complex non-linear problem. Process-based models have been previously developed to describe the time varying behaviour of chlorophyll-a, a measure of algal concentration, for these systems. This paper describes the application of a genetic programming equation discovery system to study various generalisations of a process-based m... View full abstract»

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  • Integrating economics and genetics models in information ecosystems

    Publication Year: 2000, Page(s):959 - 966 vol.2
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (816 KB)

    Multi-agent software systems can be modeled as complex, dynamic systems in which agent adaptation and interaction occur continuously and concurrently. A genetics inspired view has agent adaptation occurring via the exchange of encoded agent characteristics (genes). An economics inspired view has agent adaptation driven by changes in prices and supply and demand. Interactions of economics and genet... View full abstract»

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  • A Distributed Resource Evolutionary Algorithm Machine (DREAM)

    Publication Year: 2000, Page(s):951 - 958 vol.2
    Cited by:  Papers (13)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (788 KB)

    This paper describes a project funded by the European Commission which seeks to provide the technology and software infrastructure necessary to support the next generation of evolving infohabitants in a way that makes that infrastructure universal, open and scalable. The Distributed Resource Evolutionary Algorithm Machine (DREAM) will use existing hardware infrastructure in a more efficient manner... View full abstract»

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  • Emergent coordination for distributed information management

    Publication Year: 2000, Page(s):946 - 950 vol.2
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (376 KB)

    With an increase in the complexity of information systems, decentralised management techniques are becoming important. Such information systems will generally involve various computational and data nodes, managed by various organisations with differing policies and needs. Furthermore, in the absence of a centralised manager, co-ordination and interaction policies between such nodes become crucial.... View full abstract»

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  • How to evolve a Napoleon complex

    Publication Year: 2000, Page(s):851 - 856 vol.2
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (540 KB)

    In some species of animals, fights for scarce resources tend to be initiated by the smaller contestant, who is also more likely to eventually lose the fight. An evolutionary algorithm is used to study under which conditions such a behavior would evolve. The simulations partially confirm predictions of an earlier mathematical model, but also show an unexpectedly complex evolutionary dynamic of the ... View full abstract»

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  • Convergence properties of incremental Bayesian evolutionary algorithms with single Markov chains

    Publication Year: 2000, Page(s):938 - 945 vol.2
    Cited by:  Papers (1)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (612 KB)

    Bayesian evolutionary algorithms (BEAs) are a probabilistic model of evolutionary computation for learning and optimization. Starting from a population of individuals drawn from a prior distribution, a Bayesian evolutionary algorithm iteratively generates a new population by estimating the posterior fitness distribution of parent individuals and then sampling from the distribution offspring indivi... View full abstract»

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  • Evolving schedule graphs for the vehicle routing problem with time windows

    Publication Year: 2000, Page(s):888 - 895 vol.2
    Cited by:  Papers (2)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (572 KB)

    The vehicle routing problem with time windows (VRPTW) is a very important problem in the transportation industry since it occurs frequently in everyday practice, e.g. in scheduling bank deliveries. Many heuristic algorithms have been proposed for this NP-hard problem. This paper reports the successful application of GrEVeRT (Graph-based Evolutionary algorithm for the Vehicle Routing Problem with T... View full abstract»

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  • Optimization of a dolphin hearing model to relative sensitivity and frequency discrimination through simple aggregate selection

    Publication Year: 2000, Page(s):844 - 850 vol.2
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (532 KB)

    Dolphins demonstrate a broad range of hearing (>120 kHz), a remarkable frequency-dependent auditory sensitivity, and a fine degree of frequency discrimination across the range of hearing. Evolutionary programming has been successfully used to model the relative auditory sensitivity of the dolphin, but similar models of frequency discrimination have not been attempted. Evolutionary programming i... View full abstract»

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  • Modeling epistatic interactions in fitness landscapes

    Publication Year: 2000, Page(s):932 - 937 vol.2
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (484 KB)

    The NK model introduced by Kauffman (1993) has been widely accepted as a formal model of rugged fitness landscapes. It is shown that the NK model is incapable of accurately modeling an important class of combinatorial optimization problems. Most notable is the limitation in modeling the epistatic relationships that exist in many real-world constrained optimization problems. In addition to introduc... View full abstract»

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  • Solving constraint satisfaction problems with heuristic-based evolutionary algorithms

    Publication Year: 2000, Page(s):1571 - 1577 vol.2
    Cited by:  Papers (9)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (568 KB)

    Evolutionary algorithms (EAs) for solving constraint satisfaction problems (CSPs) can be roughly divided into two classes: EAs with adaptive fitness functions and heuristic-based EAs. A.E. Eiben et al. (1998) compared effective EAs of the first class experimentally using a large set of benchmark instances consisting of randomly-generated binary CSPs. In this paper, we complete this comparison by p... View full abstract»

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  • A game-theoretic investigation of selection methods used in evolutionary algorithms

    Publication Year: 2000, Page(s):880 - 887 vol.2
    Cited by:  Papers (8)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (692 KB)

    The replicator equation used in evolutionary game theory (EGT) assumes that strategies reproduce in direct proportion to their payoffs; this is akin to the use of fitness-proportionate selection in an evolutionary algorithm (EA). In this paper, we investigate how various other selection methods commonly used in EAs can affect the discrete-time dynamics of EGT. In particular, we show that the exist... View full abstract»

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  • Dynamics of a distance-based population diversity measure

    Publication Year: 2000, Page(s):1002 - 1009 vol.2
    Cited by:  Papers (7)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (600 KB)

    We study a class of steady-state genetic algorithms where, at each time step, two parents are selected to produce a child which then replaces one member of the population at the next time step. We consider the finite-population case. A general crossover and mutation operation are defined, as well as a genomic distance between individuals. Certain specific properties are required to hold for such o... View full abstract»

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  • Generalisation and domain specific functions in genetic programming

    Publication Year: 2000, Page(s):1393 - 1400 vol.2
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (684 KB)

    This research presents an evaluation of user defined domain specific functions of genetic programming using relational learning problems, generalisation for this class of learning problems and learning bias. After providing a brief theoretical background, two sets of experiments are detailed: experiments and results concerning the Monk-2 problem and experiments attempting to evolve generalising so... View full abstract»

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  • Evolutionary neural networks for time series prediction based on L-system and DNA coding method

    Publication Year: 2000, Page(s):1467 - 1474 vol.2
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (472 KB)

    The authors propose a method of constructing neural networks using bio-inspired emergent and evolutionary concepts. This method is an algorithm that is based on the characteristics of biological DNA and the growth of plants. The authors propose a construction method to make a DNA coding method for production rule of L-system. The L-system is based on the so-called parallel rewriting mechanism. The... View full abstract»

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  • Ant colonies are good at solving constraint satisfaction problems

    Publication Year: 2000, Page(s):1190 - 1195 vol.2
    Cited by:  Papers (13)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (524 KB)

    We define an ant algorithm for solving random binary constraint satisfaction problems (CSPs). We empirically investigate the behavior of the algorithm on this type of problems and establish the parameter settings under which the ant algorithm performs best for a specific class of CSPs. The ant algorithm is compared to six other state-of-the-art stochastic algorithms from the field of evolutionary ... View full abstract»

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  • Optimization is easy and learning is hard in the typical function

    Publication Year: 2000, Page(s):924 - 931 vol.2
    Cited by:  Papers (9)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (676 KB)

    Elementary results in algorithmic information theory are invoked to show that almost all finite functions are highly random. That is, the shortest program generating a given function description is rarely much shorter than the description. It is also shown that the length of a program for learning or optimization poses a bound on the algorithmic information it supplies about any description. For h... View full abstract»

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  • Modeling the spread of antibiotic resistance

    Publication Year: 2000, Page(s):1152 - 1159 vol.2
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (612 KB)

    This paper describes a stochastic implementation of Austin et al.'s (1999) model of the spread of antibiotic resistance in a population of fixed size under varying conditions of antibiotic use. The population is divided into sub-groups: individuals colonized by commensal bacteria and an uncolonized group. The colonized group is further divided according to whether the commensal bacteria are sensit... View full abstract»

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  • A robust evolutionary algorithm for optical thin-film designs

    Publication Year: 2000, Page(s):978 - 985 vol.2
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (660 KB)

    This paper presents an evolutionary approach, called the family competition evolutionary algorithm (FCEA), for optical thin film design. The proposed approach, based on family competition and multiple adaptive rules, integrates decreasing-based Gaussian mutation and two self-adaptive mutations to balance the exploitation and exploration. It is implemented and applied to two coating systems. Numeri... View full abstract»

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  • Computationally intensive and noisy tasks: co-evolutionary learning and temporal difference learning on Backgammon

    Publication Year: 2000, Page(s):872 - 879 vol.2
    Cited by:  Papers (8)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (760 KB)

    The most difficult but realistic learning tasks are both noisy and computationally intensive. This paper investigates how, for a given solution representation, co-evolutionary learning can achieve the highest ability from the least computation time. Using a population of Backgammon strategies, this paper examines ways to make computational costs reasonable. With the same simple architecture Gerald... View full abstract»

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  • Analog circuit design with a variable length chromosome

    Publication Year: 2000, Page(s):994 - 1001 vol.2
    Cited by:  Papers (17)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (516 KB)

    This paper proposes a system of evolving analog circuits based on a variable length chromosome. Methods featured are the chromosome of a component list, the multi-stage evolution, and the pressure on the circuit size. A set of experiments are described to confirm the system's robustness, the scalability of a circuit, and the efficiency of time and the memory consumption. The first experiment shows... View full abstract»

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  • Genetic algorithm portfolios

    Publication Year: 2000, Page(s):1304 - 1311 vol.2
    Cited by:  Papers (8)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (688 KB)

    Comparative studies of sets of control parameter values are commonly performed when tuning an evolutionary algorithm for a class of problem instances. The standard approach is to identify the most useful set of control parameter settings for a domain. In this paper, we propose an alternative anytime algorithm portfolio technique in which computational resources are allocated among multiple sets of... View full abstract»

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  • Exploratory data modeling with Bayesian-driven evolutionary search

    Publication Year: 2000, Page(s):1385 - 1392 vol.2
    Cited by:  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (676 KB)

    We present a methodology for exploratory data modeling that combines evolutionary search with two levels of statistical inference provided by Bayesian interpolation (MacKay, 1992). Evolutionary methods are used to search in a space of model structures, whereas Bayesian interpolation is used to infer parameter values for candidate models as well as to evaluate the relative fitness of these models f... View full abstract»

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