Most of the faster community extraction algorithms are based on the Clauset, Newman and Moore (CNM), which is employed for networks with sizes up to 500,000 nodes. The modification proposed by Danon, Diaz and Arenas (DDA) obtains better modularity among CNM and its variations, but there is no improvement in speed as its authors expressed. In this paper, we identify some inefficiencies in the data structure employed by former algorithms. We propose a new framework for the algorithm and a modification of the DDA to make it applicable to large-scale networks. For instance, the community extraction of a network with 1 million nodes and 5 million edges was performed in about 14 minutes in contrast to former CNM that required 45 hours (192 times the former CNM, obtaining better modularity).

Extending the notion of inheritable genotype in genetic programming (GP) from the common model of DNA into chromatin (DNA and histones), we propose epigenetic programming as an approach, embedding an explicitly controlled gene expression via modification of histones in GP. We propose double cell representation of the simulated individuals, comprising somatic cell and germ cell, both represented by their respective chromatin structures. Following biologically plausible concepts, we regard the plastic phenotype of the somatic cell, achieved via controlled gene expression owing to modifications to histones (epigenetic learning, EL) as relevant for fitness evaluation, while the genotype of the germ cell - to the phylogenesis of the individuals. The approach is verified on evolution of social behavior of team of predator agents in predator-prey pursuit problem. The empirically obtained performance evaluation results indicate that EL contributes to more than 2-fold improvement of computational effort of the phylogenesis via GP. We view the cause for that in the cumulative effect of polyphenism and epigenetic stability. The former allows for phenotypic diversity of genotypically similar individuals, while the latter robustly preserves the individuals from the destructive effects of crossover by silencing of certain genotypic combinations and explicitly activating them only when they are most likely to be expressed in corresponding beneficial phenotypic traits.

In order for an artificial life (Alife) system to evolve complex creatures, an artificial environment prepared by a designer has to satisfy several conditions. To clarify this requirement, we first assume that an artificial environment implemented in the computational medium is composed of an information space in which elementary symbols move around and react with each other according to human-prepared elementary rules. As fundamental properties of these factors (space, symbols, transportation, and reaction), we present ten criteria from a comparison with the biochemical reaction space in the real world. Then, in the latter half of the article, we take several computational Alife systems one by one, and assess them in terms of the proposed criteria. The assessment can be used not only for improving previous Alife systems but also for devising new Alife models in which complex forms of artificial creatures can be expected to evolve.