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Evolvability in Evolutionary Robotics: Evolving the Genotype-Phenotype Mapping

机译:进化机器人中的可进化性:基因型-表型作图的发展

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A completely evolvable genotype-phenotype mapping (ceGPM) is studied with respect to its capability of improving the flexibility of artificial evolution. By letting mutation affect not only controller genotypes, but also the mapping from genotype to phenotype, the future e effects of mutation can change over time. In this way, the need for prior parameter adaptation can be reduced. Experiments indicate that the ceGPM is capable of robustly adapting to a benchmark behavior. A comparison to a related approach shows significant improvements in evolvability.
机译:关于完全可进化的基因型-表型作图(ceGPM),它具有提高人工进化的灵活性的能力。通过让突变不仅影响控制基因型,而且影响从基因型到表型的映射,突变的未来影响会随着时间而改变。以这种方式,可以减少对先前参数自适应的需要。实验表明,ceGPM能够强大地适应基准行为。与相关方法的比较显示出可进化性的显着改善。

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