Takaya Arita

Diversity control in ant colony optimization

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 trade-off 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.

Evolution of linguistic diversity in a simple communication system

This article reports on the current state of our efforts to shed light on the origin and evolution of linguistic diversity using synthetic modeling and artificial life techniques. We construct a simple abstract model of a communication system that has been designed with regard to referential signaling in nonhuman animals. We analyze the evolutionary dynamics of vocabulary sharing based on these experiments. The results show that mutation rates, population size, and resource restrictions define the classes of vocabulary sharing. We also see a dynamic equilibrium, where two states, a state with one dominant shared word and a state with several dominant shared words, take turns appearing. We incorporate the idea of the abstract model into a more concrete situation and present an agent-based model to verify the results of the abstract model and to examine the possibility of using linguistic diversity in the field of distributed AI and robotics. It has been shown that the evolution of linguistic diversity in vocabulary sharing will support cooperative behavior in a population of agents.

The Dynamic Changes in Roles of Learning through the Baldwin Effect

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 Kauffmans 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.

The Baldwin Effect Revisited: Three Steps Characterized by the Quantitative Evolution of Phenotypic Plasticity

An interaction between evolution and learning called the Baldwin effect has been known for a century, but it is still poorly appreciated. This paper reports on a computational approach focusing on the quantitative evolution of phenotypic plasticity in complex environment so as to investigate its benefit and cost. For this purpose, we investigate the evolution of connection weights in a neural network under the assumption of epistatic interactions. Phenotypic plasticity is introduced into our model, in which whether each connection weight is plastic or not is genetically defined and connection weights with plasticity can be adjusted by learning. The simulation results have clearly shown that the evolutionary scenario consists of three steps characterized by transitions of the phenotypic plasticity and phenotypic variation, in contrast with the standard interpretation of the Baldwin effect that consists of two steps. We also conceptualize this evolutionary scenario by using a hill-climbing image of a population on a fitness landscape.

EVOLUTIONARY ANALYSIS ON SPATIAL LOCALITY IN N-PERSON ITERATED PRISONER'S DILEMMA

The purpose of this paper is to consider the eects of spatial locality on the evolution of cooperative behavior in the N-person iterated Prisoner’s Dilemma (N-IPD) by focusing on two essentially distinct factors: the scale of interaction (which decides the neighboring members playing the N-person games) and the scale of reproduction (which decides the neighboring candidates for an ospring in each cell). We conducted evolutionary experiments of strategies for one-dimensional N-IPD game with various settings of these two factors. Experimental results revealed that these two factors bring qualitatively dieren t eects to the emergence of cooperative behavior. Furthermore, we investigated the dynamics of the evolution of spatial locality in N-IPD. When we introduced the evolution of the scale of interaction into our model, the dynamic evolution of the scale of interaction through generation facilitated the emergence of global cooperation when the scale of reproduction was relatively small. Experiments with the evolution of the scale of reproduction are also discussed.

Evolution of Virtual Creature Foraging in a Physical Environment

We present the results of evolving articulated virtual creature foraging in a 3D physically simulated environment filled with stationary food objects. Simple block creatures with sigmoidal neural networks are evolved through a genetic algorithm using a fitness function based on the consumption amount. The results show the evolution of successful foraging behaviors performing well in environments with various food distributions. We analyze the foraging based on its efficiency, creature morphologies, movement strategies, and the food density and entropy in the simulation environment.

Language Evolution and the Baldwin Effect

Recently, a new constructive approach has emerged characterized by the use of computational models for simulating the evolution of language. This paper investigates the interaction between the two adaptation processes in different time-scales, evolution and learning of language, by using a computational model. Simulation results show that the fitness increases rapidly and remains at a high level, while the phenotypic plasticity increases together with the fitness, but then decreases and gradually converges to a medium value. This is regarded as the two-step transition of the so-called Baldwin effect. We investigate the evolutionary dynamics governing the effect.

Repeated Occurrences of the Baldwin Effect Can Guide Evolution on Rugged Fitness Landscapes

The Baldwin effect is known as a possible scenario of interactions between evolution and learning caused by the balances between benefit and cost of learning. It is still controversial how learning can affect evolution on rugged fitness landscapes because previous studies merely focused on a process in which the population reaches a local optimum through a single occurrence of this effect, even though there exist a lot of local optimums on the landscape. Our purpose is to clarify whether and how learning can facilitate the adaptive evolution of population on rugged fitness landscapes in view of the repeated occurrences of the Baldwin effect. For this purpose, we constructed a simple fitness function that represents a multi-modal fitness landscape in which there is a trade-off between the adaptivity of individual and the strength of the epistatic interactions among its phenotypes. Phenotypic plasticity is introduced into our model, in which whether each phenotype is plastic or not is genetically defined and plastic phenotypes can be adjusted by learning. The evolutionary experiments clearly showed that the Baldwin effect repeatedly occurred through the evolutionary process of the population on this landscape, and facilitated its adaptive evolution as a whole

Evolution of cooperation on different combinations of interaction and replacement networks with various intensity of selection

There are various discussions on the evolution of cooperation on different combinations of interaction network for playing games and the replacement network for imitation of strategies. This paper aims at clarifying the topological relationship between these networks that facilitates the evolution of cooperation by focusing on the intensity of selection for imitation process of strategies. We construct an agent-based model of the evolutionary prisoners dilemma on different combinations of interaction and replacement networks. The relationship between these networks can be adjusted by the scales of interaction and reproduction, and the intensity of selection can be adjusted from the almost deterministic selection of the best strategy to the extremely stochastic selection. The evolutionary experiments shows that the larger scale of reproduction than the scale of interaction brought about higher level cooperation when the intensity of selection is high, and the minimum scale of interaction and reproduction was the best for the evolution of cooperation when the intensity of selection is low.

Heterochrony and artificial embryogeny: A method for analyzing artificial embryogenies based on developmental dynamics

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), 296317) 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.