We show how the concept of metamorphosis, together with a biologically inspired model of multicellular development, can be used to evolve soft-bodied robots that are adapted to two very different tasks, such as being able to move in an aquatic and in a terrestrial environment. Each evolved solution defines two pairs of morphologies and controllers, together with a process of transforming one pair into the other. Animats develop from a single cell and grow through cellular divisions and deaths until they reach an initial larval form adapted to a first environment. To obtain the adult form adapted to a second environment, the larva undergoes metamorphosis, during which new cells are added or removed and its controller is modified. Importantly, our approach assumes nothing about what morphologies or methods of locomotion are preferred. Instead, it successfully searches the vast space of possible designs and comes up with complex, surprising, lifelike solutions that are reminiscent of amphibian metamorphosis. We analyze obtained solutions and investigate whether the morphological changes during metamorphosis are indeed adaptive. We then compare the effectiveness of three different types of selective pressures used to evolve metamorphic individuals. Finally, we investigate potential advantages of using metamorphosis to automatically produce soft-bodied designs by comparing the performance of metamorphic individuals with their specialized counterparts and designs that are robust to both environments.
Optimization inspired by cooperative food retrieval in ants has been unexpectedly successful and has been known as ant colony optimization (ACO) in recent years. One of the most important factors to improve the performance of the ACO algorithms is the complex tradeoff between intensification and diversification. This article investigates the effects of controlling the diversity by adopting a simple mechanism for random selection in ACO. The results of computer experiments have shown that it can generate better solutions stably for the traveling salesmen problem than ASrank which is known as one of the newest and best ACO algorithms by utilizing two types of diversity.
d C-type lectin SIGNR1 directly recognizes Candida albicans and zymosan and has been considered to share properties of polysaccharide recognition with human DC-SIGN (hDC-SIGN). However, the precise specificity of SIGNR1 and the difference from that of hDC-SIGN remain to be elucidated. We prepared soluble forms of SIGNR1 and hDC-SIGN and conducted experiments to examine their respective specificities. Soluble SIGNR1 (sSIGNR1) bound several types of live C. albicans clinical isolate strains in an EDTA-sensitive manner. Inhibition analyses of sSIGNR1 binding by glycans from various yeast strains demonstrated that SIGNR1 preferentially recognizes N-glycan ␣-mannose side chains in Candida mannoproteins, as reported in hDC-SIGN. Unlike shDC-SIGN, however, sSIGNR1 recognized not only Saccharomyces cerevisiae, but also C. albicans J-1012 glycan, even after ␣-mannosidase treatment that leaves only 1,2-mannose-capped ␣-mannose side chains. In addition, glycomicroarray analyses showed that sSIGNR1 binds mannans from C. albicans and S. cerevisiae but does not recognize Lewis a/b/x/y antigen polysaccharides as in shDC-SIGN. Consistent with these results, RAW264.7 cells expressing hDC-SIGN in which the carbohydrate recognition domain (CRD) was replaced with that of SIGNR1 (RAW-chimera) produced comparable amounts of interleukin 10 (IL-10) in response to glycans from C. albicans and S. cerevisiae, but those expressing hDC-SIGN produced less IL-10 in response to S. cerevisiae than C. albicans. Furthermore, RAW-hDC-SIGN cells remarkably reduced IL-10 production after ␣-mannosidase treatment compared with RAW-chimera cells. These results indicate that SIGNR1 recognizes C. albicans/yeast through a specificity partly distinct from that of its homologue hDC-SIGN.
This paper aims at understanding coevolutionary dynamics of cooperative behaviors and network structures of interactions. We constructed an evolutionary model in which each individual not only has a strategy for prisoner's dilemma to play with its neighboring members on the network, but also has a strategy for changing its neighboring structure of the network. By conducting evolutionary experiments with various settings of the payoff matrix, we found that the coevolutionary cycles of cooperative behaviors of individuals and their network structures repeatedly occurred when both the temptation to defect and the cost for playing a game were moderate.
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