We present empirical evidence that long-term evolutionary dynamics fall into three distinct classes, depending on whether adaptive evolutionary activity isabsent (class 1), bounded (class 2), or unbounded (class 3). These classes are de ned using three statistics: diversity, new evolutionary activity (Bedau & Packard 1992), and mean cumulative evolutionary activity (Bedau et al. 1997). The three classes partition all the longterm evolutionary dynamics observed in Holland's Echo model (Holland 1992), in a random-selection adaptivelyneutral "shadow" of Echo, and in the biosphere as reected in the Phanerozoic fossil record. This classi-cation provides quantitative evidence that Echo lacks the unbounded growth in adaptive evolutionary activity observed in the fossil record.

Abstract not found

After reviewing current approaches in Evolutionary Robotics, we point to directions of research that are likely to bring interesting results in the future. We then address two crucial aspects for future developments of Evolutionary Robotics: choice of fitness functions and scalability to real-world situations. In the first case we suggest a framework to describe fitness functions, choose them according to the situation constraints, and compare available experiments in the literature on evolutionary robotics. In the second case, we suggest a way to make experimental results applicable to realworld situations by evolving online continuous adaptive controllers. We also give an overview of recent experimental results showing that the suggested approaches produce qualitatively superior abilities, scale up to more complex architectures, smoothly transfer from simulations to real robots and across different robotic platforms, and autonomously adapt in few seconds to several sources of strong variability that were not included during the evolutionary run. 1

Questions about the important essential properties of biological systems are both di cult to answer and worthwhile to try to answer. Here are three examples of deep open questions in theoretical biology: 1. Is robust multi-level emergent activity anintrinsic property of certain homeostatic self-organizing systems like cells or organisms, and if so, how is this possible? 2. Is open-ended adaptive evolution an intrinsic property of certain evolving systems like the biosphere, and if so, how? 3. Is unbounded complexity or diversity growth an intrinsic property of certain evolving systems like the biosphere, and if so, how? These questions concern apparent fundamental properties of living systems|properties which, furthermore, seem to be shared bymany other complex adaptive systems, such as the global economy. It is especially hard to address these questions, largely because they concern the global emergent behavior of overwhelmingly complex systems. One way to pursue answers is with a certain sort of unrealistic computational model. Although this may sound paradoxical, I shall argue that, properly understood, it makes perfect sense. I could not agree more when Levin et al. [10] say that \[i]maginative and e cient computational approaches are essential in dealing with the overwhelming complexity of biological systems " (p. 341). There are at least two quite di erent kinds of computational models of complex biological systems. One strives for maximal delity to the details of particular natural systems, exploiting prodigious computer power to push the envelope on micro-mechanical realism. But I am

Bedau et al.’s statistical classification system for long-term evolutionary dynamics provides a test for open-ended evolution. Making this test more rigorous, and passing it, are two of the most important open problems for research into systems of agents that actively process and transform their environment. This paper presents a detailed description of the application of this test to ‘Geb’, a system designed to verify and extend theories behind the generation of evolutionarily emergent systems. The result is that, according to these statistics, Geb exhibits unbounded evolutionary dynamics, making it the first autonomous artificial system to pass this test. However, having passed it, the most prudent course of action is to look for weaknesses in the test. The test is criticized, most significantly with regard to its normalization method for artificial systems. Furthermore, this paper presents a modified normalization method, based on component activity normalization, that overcomes these criticisms. The results of the revised test, when applied to Geb, indicate that this system does indeed exhibit open-ended evolution.

We examine a simple form of the evolution of evolvability---the evolution of mutation rates---in a simple model system. The system is composed of many agents moving, reproducing, and dying in a twodimensional resource-limited world. We first examine various macroscopic quantities (three types of genetic diversity, a measure of population fitness, and a measure of evolutionary activity) as a function of fixed mutation rates. The results suggest that (i) mutation rate is a control parameter that governs a transition between two qualitatively different phases of evolution, an ordered phase characterized by punctuated equilibria of diversity, and a disordered phase of characterized by noisy fluctuations around an equilibrium diversity, and (ii) the ability of evolution to create adaptive structure is maximized when the mutation rate is just below the transition between these two phases of evolution. We hypothesize that this transition occurs when the demands for evolutionary memory and evolutionary novelty are typically balanced. We next allow the mutation rate itself to evolve, and we observe that evolving mutation rates adapt to values at this transition. Furthermore, the mutation rates adapt up (or down) as the evolutionary demands for novelty (or memory) increase, thus supporting the balance hypothesis.

The role of contingency (random events) in an artificial evolutionary system is investigated by running the system a number of times under exactly the same conditions except for the seed used to initialize the random number generator at the beginning of each run. Twelve different measures were used to track the course of evolution in each run, and "activity wave diagrams" were also produced (Bedau & Brown 1997). The results of 19 runs are presented and analyzed. The performance of every run was compared with each of the others using a non-parametric test (a randomization version of the paired-sample t test). When comparing absolute values of the measures between the runs, some significant differences were found. However, looking at the difference in values between adjacent sample points for a run, no run was significantly different to any other for any of the measures. This suggests that the general behaviour is the same in all runs, but the accumulation of differences results in signi...

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

Abstract not found

Contemporary artificial life (also known as “ALife”) is an interdisciplinary study of life and life-like processes. Its two most important qualities are that it focuses on the essential rather than the contingent features of living systems and that it attempts to understand living systems by artificially synthesizing extremely simple forms of them. These two qualities are connected. By synthesizing simple systems that are very life-like and yet very unfamiliar, artificial life constructively explores the boundaries of what is possible for life. At the moment, artificial life uses three different kinds of synthetic methods. “Soft ” artificial life creates computer simulations or other purely digital constructions that exhibit life-like behavior. “Hard” artificial life produces hardware implementations of life-like systems. “Wet ” artificial life involves the creation of life-like systems in a laboratory using biochemical materials. Contemporary artificial life is vigorous and diverse. So this chapter’s first goal is to convey what artificial life is like. It first briefly reviews the history of artificial life and illustrates the current research thrusts in contemporary “soft”, “hard”, and