Titlebook: Linear Genetic Programming; Markus F. Brameier,Wolfgang Banzhaf Book 2007 Springer-Verlag US 2007 Step Size Control.Syntax.algorithms.code

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A Comparison with Neural Networks to RPROP neural networks with respect to the generalization performance..The runtime performance of genetic programming becomes especially important for time-critical applications or when operating with large data sets from real-world domains like medicine. Two techniques were presented that reduce

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Linear Genetic Operators I — Segment Variations crossover and mutation operators for the linear program representation and compare their influence on prediction performance and the complexity of evolved solutions..We can distinguish between two different levels of variation done by these operators. . operate on the instruction level (or .). In t

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Linear Genetic Operators II — Instruction Mutationslatively low limit to segment length. Second, segment recombination has not been found to be more powerful than segment mutation. Both aspects motivate the use of mutations that affect . instruction only. The following considerations try to point out why linear programs in particular are likely to b

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Analysis of Control Parametersuences of more general system parameters that are relevant in linear genetic programming. In particular, the number of registers, the number of constants, the population size, and the maximum program length will be studied. Additionally, we compare different initialization techniques for linear gene

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Control of Diversity and Variation Step Sizeypes and of phenotypes form a necessary condition for being able to control differences between genetic programs. The two objectives of this chapter are to show [1] how distance information between individuals can be used to control structural diversity of individuals and [2] how variation distance

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Code Growth and Neutral Variationsfor the growth of code in GP runs. Other existing theories about code growth are verified for linear GP and are partly reevaluated from a different perspective..In evolutionary computation neutral variations are argued to explore flat regions of the fitness landscape while non-neutral variations exp

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