1,720,975 research outputs found
Modular Interdependency in Complex Dynamical Systems
No full textSimon’s characterisation of modularity in dynamical systems describes subsystems as having dynamics that are approximately independent of those of other subsystems (in the short term). This fits with the general intuition that modules must, by definition, be approximately independent. In the evolution of complex systems, such modularity may enable subsystems to be modified and adapted independently of other subsystems whereas in a non-modular system, modifications to one part of the system may result in deleterious side-effects elsewhere in the system. But this notion of modularity and its effect on evolvability is not well-quantified and is rather simplistic. In particular, modularity need not imply that inter-module dependencies are weak or unimportant. In dynamical systems this is acknowledged by Simon’s suggestion that, in the long term, the dynamical behaviours of subsystems do interact with one another, albeit in an ‘aggregate’ manner – but this kind of inter-module interaction is omitted in models of modularity for evolvability. In this brief discussion paper we seek to unify notions of modularity in dynamical systems with notions of how modularity affects evolvability. This leads to a quantifiable measure of modularity and a different understanding of its impact on evolvability
Embodied Evolution: Distributing an evolutionary algorithm in a population of robots
No full textWe introduce Embodied Evolution (EE) as a new methodology for evolutionary robotics (ER). EE uses a population of physical robots that autonomously reproduce with one another while situated in their task environment. This constitutes a fully distributed evolutionary algorithm embodied in physical robots. Several issues identified by researchers in the evolutionary robotics community as problematic for the development of ER are alleviated by the use of a large number of robots being evaluated in parallel. Particularly, EE avoids the pitfalls of the simulate-and-transfer method and allows the speed-up of evaluation time by utilizing parallelism. The more novel features of EE are that the evolutionary algorithm is entirely decentralized, which makes it inherently scalable to large numbers of robots, and that it uses many robots in a shared task environment, which makes it an interesting platform for future work in collective robotics and Artificial Life. We have built a population of eight robots and successfully implemented the first example of Embodied Evolution by designing a fully decentralized, asynchronous evolutionary algorithm. Controllers evolved by EE outperform a hand-designed controller in a simple application. We introduce our approach and its motivations, detail our implementation and initial results, and discuss the advantages and limitations of EE
A Computational Model of Symbiotic Composition in Evolutionary Transitions
Full text linkSeveral of the major transitions in evolutionary history, such as the symbiogenic origin of eukaryotes from prokaryotes, share the feature that existing entities became the components of composite entities at a higher level of organisation. This composition of pre-adapted extant entities into a new whole is a fundamentally different source of variation from the gradual accumulation of small random variations, and it has some interesting consequences for issues of evolvability. Intuitively, the pre-adaptation of sets of features in reproductively independent specialists suggests a form of ‘divide and conquer’ decomposition of the adaptive domain. Moreover, the compositions resulting from one level may become the components for compositions at the next level, thus scaling-up the variation mechanism. In this paper, we explore and develop these concepts using a simple abstract model of symbiotic composition to examine its impact on evolvability. To exemplify the adaptive capacity of the composition model, we employ a scale-invariant fitness landscape exhibiting significant ruggedness at all scales. Whilst innovation by mutation and by conventional evolutionary algorithms becomes increasingly more difficult as evolution continues in this landscape, innovation by composition is not impeded as it discovers and assembles component entities through successive hierarchical levels
Mutualism, Parasitism, and Evolutionary Adaptation
No full textOur investigations concern the role of symbiosis as an enabling mechanism in evolutionary adaptation. Previous work has illustrated how the formation of mutualist groups can guide genetic variation so as to enable the evolution of ultimately independent organisms that would otherwise be unobtainable. The new experiments reported here show that this effect applies not just in genetically related organisms but may also occur from symbiosis between distinct species. In addition, a new detail is revealed: when the symbiotic group members are drawn from two separate species only one of these species achieves eventual independence and the other remains parasitic. It is nonetheless the case that this second species, formerly mutualistic, was critical in enabling the independence of the first. We offer a biological example that is suggestive of the effect and discuss the implications for evolving complex organisms, natural and artificial
Coevolutionary Dynamics in a Minimal Substrate
No full textOne of the central difficulties of coevolutionary methods arises from 'intransitive superiority' - in a two-player game, for example, the fact that A beats B, and B beats C, does not exclude the possibility that C beats A. Such cyclic superiority in a coevolutionary substrate is hypothesized to cause cycles in the dynamics of the population such that it 'chases its own tail' - traveling through some part of strategy space more than once despite apparent improvement with each step. It is often difficult to know whether an application domain contains such difficulties and to verify this hypothesis in the failure of a given coevolutionary set-up. In this paper we wish to elucidate some of the issues and concepts in an abstract domain where the dynamics of coevolution can be studied simply and directly. We define three simple 'number games' that illustrate intransitive superiority and resultant oscillatory dynamics, as well as some other relevant concepts. These include the distinction between a player's perceived performance and performance with respect to an external metric, and the significance of strategies with a multi-dimensional nature. These features alone can also cause oscillatory behavior and coevolutionary failure
Symbiotic Composition and Evolvability
No full textSeveral of the Major Transitions in natural evolution, such as the symbiogenic origin of eukaryotes from prokaryotes, share the feature that existing entities became the components of composite entities at a higher level of organisation. This composition of pre-adapted extant entities into a new whole is a fundamentally different source of variation from the gradual accumulation of small random variations, and it has some interesting consequences for issues of evolvability. In this paper we present a very abstract model of 'symbiotic composition' to explore its possible impact on evolvability. A particular adaptive landscape is used to exemplify a class where symbiotic composition has an adaptive advantage with respect to evolution under mutation and sexual recombination. Whilst innovation using conventional evolutionary algorithms becomes increasingly more difficult as evolution continues in this problem, innovation via symbiotic composition continues through successive hierarchical levels unimpeded. <br/
How Symbiosis Can Guide Evolution
No full textHinton and Nowlan have demonstrated a model of how lifetime plasticity can guide evolution. They show how acquired traits change the shape of the reward landscape in which subsequent genetic variation takes place, and in so doing encourage the discovery of equivalent heritable traits. This enables the seemingly Lamarkian inheritance of acquired characteristics without the direct transfer of information from the phenotype to the genotype. This paper draws direct inspiration from their work to illustrate a different phenomenon. We demonstrate how the formation of symbiotic relationships in an ecosystem can guide the course of subsequent genetic variation. This phenomenon can be described as two phases: First, symbiotic groups find solutions where individual organisms cannot, simply because lifetime interaction produces new combinations of abilities more rapidly than the relatively slow genetic variation of individuals. Second, these symbiotic groups subsequently change the shape of the reward landscape for evolution, providing a gradient that guides genetic variation to the same solution. Ultimately, an individual organism exhibits the capabilities formerly exhibited by the group. This process enables the combination of characteristics from organisms of distinct species without direct transfer of genetic information
Artificial Life IX: Proceedings of the Ninth International Conference on the Simulation and Synthesis of Living Systems
No full textArtificial Life is an interdisciplinary effort to investigate the fundamental properties of living systems through the simulation and synthesis of life-like processes. The young field brings a powerful set of tools to the study of how high-level behavior can arise in systems governed by simple rules of interaction. Some of the fundamental questions include: What are the principles of evolution, learning, and growth that can be understood well enough to simulate as an information process? Can robots be built faster and more cheaply by mimicking biology than by the product design process used for automobiles and airplanes? How can we unify theories from dynamical systems, game theory, evolution, computing, geophysics, and cognition? The field has contributed fundamentally to our understanding of life itself through computer models, and has led to novel solutions to complex real-world problems across high technology and human society. This elite biennial meeting has grown from a small workshop in Santa Fe to a major international conference. This ninth volume of the proceedings of the international A-life conference reflects the growing quality and impact of this interdisciplinary scientific community
Modeling Building Block Interdependency
No full textThe Building-Block Hypothesis appeals to the notion of problem decomposition and the assembly of solutions from sub-solutions. Accordingly, there have been many varieties of GA test problems with a structure based on building-blocks. Many of these problems use deceptive fitness functions to model interdependency between the bits within a block. However, very few have any model of interdependency between building-blocks; those that do are not consistent in the type of interaction used intra-block and inter-block. This paper discusses the inadequacies of the various test problems in the literature and clarifies the concept of building-block interdependency. We formulate a principled model of hierarchical interdependency that can be applied through many levels in a consistent manner and introduce Hierarchical If-and-only-if (H-IFF) as a canonical example. We present some empirical results of GAs on H-IFF showing that if population diversity is maintained and linkage is tight then the GA is able to identify and manipulate building-blocks over many levels of assembly, as the Building-Block Hypothesis suggests
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