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.

Recent studies have reported that population dynamics and evolutionary dynamics, occurring at different time scales, can be affected by each other. Our purpose is to explore the interaction between population and evolutionary dynamics using an artificial life approach based on a 3D physically simulated environment in the context of predator–prey and morphology–behavior coevolution. The morphologies and behaviors of virtual prey creatures are evolved using a genetic algorithm based on the predation interactions between predators and prey. Both population sizes are also changed, depending on the fitness. We observe two types of cyclic behaviors, corresponding to short-term and long-term dynamics. The former can be interpreted as a simple population dynamics of Lotka–Volterra type. It is shown that the latter cycle is based on the interaction between the changes in the prey strategy against predators and the long-term change in both population sizes, resulting partly from a tradeoff between their defensive success and the cost of defense.

Artificial embryogenies are an extension to evolutionary algorithms, in which genotypes specify a process to grow phenotypes. This approach has become rather popular recently, with new kinds of embryogenies being increasingly reported in the literature. Nevertheless, it is still difficult to analyze and compare the available embryogenies, especially if they are based on very different paradigms. We propose a method to analyze embryogenies based on growth dynamics, and how evolution is able to change them (heterochrony). We define several quantitative measures that allow us to establish the variation in growth dynamics that an embryogeny can create, the degree of change in growth dynamics caused by mutations, and the degree to which an embryogeny allows mutations to change the growth of a genotype, but without changing the final phenotype reached. These measures are based on an heterochrony framework, due to Alberch, Gould, Oster, & Wake (1979 Size and shape in ontogeny and phylogeny, Paleobiology, 5 (3), 296–317) that is used in real biological organisms. The measures are general enough to be applied to any embryogeny, and can be easily computed from simple experiments. We further illustrate how to compute these measures by applying them to two simple embryogenies. These embryogenies exhibit rather different growth dynamics, and both allow for mutations that changed growth without affecting the final phenotype.

The interaction between evolution and learning called the Baldwin effect is a two-step evolutionary scenario caused by the balances between benefit and cost of learning in general. However, little is known about the dynamic evolution of these balances in complex environments. Our purpose is to give a new insight into the benefit and cost of learning by focusing on the quantitative evolution of phenotypic plasticity under the assumption of epistatic interactions. For this purpose, we have constructed an evolutionary model of quantitative traits by using an extended version of Kauffman's NK fitness landscape. Phenotypic plasticity is introduced into our model; whether each phenotype is plastic or not is genetically defined, and plastic phenotypes can be adjusted by learning. The simulation results clearly show that drastic changes in roles of learning cause three-step evolution through the Baldwin effect and also cause the evolution of genetic robustness against mutations. We also conceptualize four different roles of learning by using a hill-climbing image of a population on a fitness landscape.