Applications of Evolutionary Computing: EvoWorkshops 2002: by Peter Cowling, Graham Kendall, Eric Soubeiga (auth.),

By Peter Cowling, Graham Kendall, Eric Soubeiga (auth.), Stefano Cagnoni, Jens Gottlieb, Emma Hart, Martin Middendorf, Günther R. Raidl (eds.)

This e-book constitutes the refereed complaints of 3 workshops at the program of evolutionary programming and algorithms in a variety of domain names; those workshops have been held along with the fifth eu convention on Genetic Programming, EuroGP 2002, in Kinsale, eire, in April 2002.
The 33 revised complete papers provided have been conscientiously reviewed and chosen via the respective software committees. according to the 3 workshops EvoCOP, EvoIASP, and EvoSTIM/EvoPLAN, the papers are geared up in topical sections on combinatorial optimization difficulties; photo research and sign processing; and scheduling, timetabling, and AI planning.

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That the global optimum has a large basin of attraction, and is located ‘near’ to other (local) optima. The class of ‘low autocorrelation binary sequences’ (LABS), however, is notorious for generating problems where the global optima are very hard to find. The task is to find a vector y ∈ {−1, +1}N that minimizes N −1 E= c2p p=1 where cp is the autocorrelation of the sequence (y1 , . . e. N −p cp = yi yi+p i=1 An alternative version is often used, where the objective is to maximize N2 . 2E The obvious neighbourhood to use for this problem is again a ‘bit-flip’ neighbourhood.

Updating ACO Pheromones 25 pheromone values. Now, the original optimization problem may be replaced with the following equivalent continuous maximization problem: T ∗ = argmax E(T ), (3) T where E(T ) = ET Qf (s), ET denotes expectation with respect to PT , and Qf (s) is a fixed quality function, which is strictly decreasing with respect to f . It may be easily verified that PT ∗ is greater than zero only over S ∗ , hence solving problem (3) is equivalent to solving the original combinatorial optimization problem.

They start from the premise that the best estimator we have for an unknown distribution is the empirical distribution that is embedded in the data. 1 Jackknife Estimate Better estimates appeared to be obtained by using a non-parametric approach— the jackknife [6]. , the actual number of distinct optima found), and assumes that the bias decreases with increasing r. If we now leave out one point of the original sample at a time, we obtain r ‘re-samples’ of the original data, and thus r estimates of ν, which can be combined to give a new estimate νˆ(r−1) = k − β1 r where β1 is the number of optima seen only once.

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