Abstract Evolutionary activity statistics and their visualization are introduced, and their motivation is explained. Examples of their use are described, and their strengths and limitations are discussed. References to more extensive or general accounts of these techniques

An individual-based model of the process of niche construction is presented, whereby organisms disturb the environment experienced by their neighbours. This disturbance in local conditions creates a niche that potentially could be filled by another species (which would then create still more niches and so on). The model is unique in allowing the complexity of the organisms---measured by the number of genes they possess in order to be well adapted to their local environment---to evolve over time, and is therefore the first model with which it is possible to study the contribution of niche construction to the evolution of organism complexity. Results of experiments demonstrate that the process of niche construction does indeed introduce an active drive for organisms with more genes. This is the first explicit example of a model which possesses an intrinsic drive for the evolution of complexity.

We review the applications of artificial life (ALife), the creation of synthetic life on computers to study, simulate, and understand living systems. The definition and features of ALife are shown by application studies. ALife application fields treated include robot control, robot manufacturing, practical robots, computer graphics, natural phenomenon modeling, entertainment, games, music, economics, Internet, information processing, industrial design, simulation software, electronics, security, data mining, and telecommunications. In order to show the status of ALife application research, this review primarily features a survey of about 180 ALife application articles rather than a selected representation of a few articles. Evolutionary computation is the most popular method for designing such applications, but recently swarm intelligence, artificial immune network, and agent-based modeling have also produced results. Applications were initially restricted to the robotics

This paper proposes a method of visualizing and measuring evolution in Artificial Life simulations. The evolving population of agents is treated as a dynamical system. The proposed method is inspired by the notion of trajectory. This paper provides examples of tracking of trajectories of evolutionary system in the spaces of genotypes, strategies and some global characteristics. Visualization similar to bifurcation diagram is used to represent results of series of simulations.

Emergence is largely used as an explanation: such and such an object --- ranging from atoms through multicellular organisms to consciousness --- is an emergent property of some ensemble of parts. But this leaves an inadequate level of understanding, making the term a representation of something almost mystical. For emergence to be useful an understanding of the mechanisms of emergence must be brought out. This paper proposes a system of orders of organizing relations that are the means by which complex objects "emerge" from the interactions of their constituent parts.

We mowen nat, although we hadden it sworn, It overtake, it slit awey so faste. It wole us maken beggers atte laste! Evolution is notorious for its creative power, but also for giving rise to complex, unpredictable dynamics. As a result, practitioners of artificial evolution have encountered difficulties in predicting, analysing, or even understanding the outcome of their experiments. In particular, the concept of evolutionary “progress ” (whether in the sense of performance increase or complexity growth) has given rise to much debate and confusion. After a careful description of the mechanisms of evolution and natural selection, we provide usable concepts of performance and progress in coevolution. In particular, we introduce a distinction between three types of progress: local, historical, and global, which we suggest underlies much of the confusion that surrounds coevolutionary dynamics. Similarly, we provide a comprehensive answer to the question of whether an “arrow of complexity ” exists in evolution. We introduce several methods to detect and analyse performance and progress in coevolutionary experiments. We propose a statistical

If markets are efficient then returns are non-predictable