We present a graph-based semi-supervised label propagation algorithm for acquiring opendomain labeled classes and their instances from a combination of unstructured and structured text sources. This acquisition method significantly improves coverage compared to a previous set of labeled classes and instances derived from free text, while achieving comparable precision.

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Evolutionary algorithms tend to produce solutions that are not evolvable: Although current fitness may be high, further search is impeded as the effects of mutation and crossover become increasingly detrimental. In nature, in addition to having high fitness, organisms have evolvable genomes: phenotypic variation resulting from random mutation is structured and robust. Evolvability is important because it allows the population to produce meaningful variation, leading to efficient search. However, because evolvability does not improve immediate fitness, it must be selected for indirectly. One way to establish such a selection pressure is to change the fitness function systematically. Under such conditions, evolvability emerges only if the representation allows manipulating how genotypic variation maps onto phenotypic variation and if such manipulations lead to detectable changes in fitness. This research forms a framework for understanding how fitness function and representation interact to produce evolvability. Ultimately evolvable encodings may lead to evolutionary algorithms that exhibit the structured complexity and robustness found in nature.

Adaptive representations allow evolution to explore the space of phenotypes by choosing the most suitable set of genotypic parameters. Although such an approach is believed to be efficient on complex problems, few empirical studies have been conducted in such domains. In this paper, three neural network representations, a direct encoding, a complexifying encoding, and an implicit encoding capable of adapting the genotype-phenotype mapping are compared on Nothello, a complex game playing domain from the AAAI General Game Playing Competition. Implicit encoding makes the search more efficient and uses several times fewer parameters. Random mutation leads to highly structured phenotypic variation that is acquired during the course of evolution rather than built into the representation itself. Thus, adaptive representations learn to become evolvable, and furthermore do so in a way that makes search efficient on difficult coevolutionary problems.