Thomas Weise's Global Optimization Algorithms - Theory and Application PDF

By Thomas Weise

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4 on page 78 1 Introduction fitness/objective values fitness/objective values 24 (b) smooth multiple (local) optima fitness/objective values fitness/objective values (a) best case ? ? no gradient information (d) rugged region with misleading gradient information fitness/objective values fitness/objective values (c) multimodal ? neutral area ? needle (isolated optimum) (f) neutral ? neutral area or area without much information (g) needle-in-a-haystack fitness/objective values fitness/objective values (e) deceptive ?

A program consisting of 100 instructions, allowing the ant to gather 60 food items when walking a distance of 5000 length units. • A program consisting of 10 instructions, allowing the ant to gather 1 food item when walking a distance of 5 length units (straight ahead). • A program consisting of 0 instructions, allowing the ant to gather 0 food item when walking a distance of 0 length units (straight ahead). The Pareto optimal set obviously contains two useless but non-dominated individuals which occupy space in the population and the non-dominated set.

11 we have illustrated this example. The major problem that results from overfitted solutions is the loss of generality. Definition 26 (Generality). A solution s of an optimization process is general if it is not only valid for the sample inputs x1 , x2 , . . , xn which were used during the optimization process in order to find it, but also for different inputs ξ = xi ∀ 0 < i ≤ n if such inputs ξ exist. A solution is also general if it is valid for all possible inputs. There are two major reasons for overfitting: 1.

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Global Optimization Algorithms - Theory and Application by Thomas Weise

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