186,556 research outputs found

    Conservation, Creation, and Evolution: Revising the Darwinian Project

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    There is hardly anything more central to our universe than conservation. Many scientific fields and disciplines view the law of conservation as one of the most fundamental universal laws. The Darwinian model pivots the process of evolution on variability, reproduction, and natural selection. Conservation plays a marginal role in this model and is not really universal, as the model allows exceptions to conservation, i.e. non-conservation, to play an equally important role in evolution. This anomalous role of conservation in the Darwinian model raises questions: What is the reason for this anomaly? Is conservation really universal, as we tend to believe or is it not, as the Darwinian model suggests? This contribution proposes a new model of evolution that focuses on levels of organization, rather than of species, organisms, or populations. It argues that conservation is central to evolution. Not only does this new model restores the universal status of conservation but it also makes possible to resolve some outstanding problems and controversies that continue to plague the Darwinian model. The article tries to advance the broad Darwinian project that seeks to explain the process of evolution as a product of the spontaneous processes in nature

    Evolutionary Robotics: a new scientific tool for studying cognition

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    We survey developments in Artificial Neural Networks, in Behaviour-based Robotics and Evolutionary Algorithms that set the stage for Evolutionary Robotics in the 1990s. We examine the motivations for using ER as a scientific tool for studying minimal models of cognition, with the advantage of being capable of generating integrated sensorimotor systems with minimal (or controllable) prejudices. These systems must act as a whole in close coupling with their environments which is an essential aspect of real cognition that is often either bypassed or modelled poorly in other disciplines. We demonstrate with three example studies: homeostasis under visual inversion; the origins of learning; and the ontogenetic acquisition of entrainment

    Digital Tectonics as a Morphogenetic Process

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    p. 938-948Tectonics is a seminal concept that defines the nature of the relationship between architecture and its structural properties. The changing definition of the symbiotic relationship between structural engineering and architectural design may be considered one of the formative influences on the conceptual evolution of tectonics in different historical periods. Recent developments in the field of morphogenesis, digital media, theories techniques and methods of digital design have contributed a new models of integration between structure, material and form in digital tectonics. The objective of this paper is to propose and define tectonics as a model of morphogenetic process. The paper identifies and presents the manner in which theory and emerging concepts of morphogenesis as well as digital models of design are contributing to this new model. The paper first analyzes the historical evolution of tectonics as a concept and characterizes the emergence of theoretical framework reflected in concepts and terms related to morphogenesis.Oxman, R. (2010). Digital Tectonics as a Morphogenetic Process. Editorial Universitat PolitĂšcnica de ValĂšncia. http://hdl.handle.net/10251/695

    Embodied Evolution in Collective Robotics: A Review

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    This paper provides an overview of evolutionary robotics techniques applied to on-line distributed evolution for robot collectives -- namely, embodied evolution. It provides a definition of embodied evolution as well as a thorough description of the underlying concepts and mechanisms. The paper also presents a comprehensive summary of research published in the field since its inception (1999-2017), providing various perspectives to identify the major trends. In particular, we identify a shift from considering embodied evolution as a parallel search method within small robot collectives (fewer than 10 robots) to embodied evolution as an on-line distributed learning method for designing collective behaviours in swarm-like collectives. The paper concludes with a discussion of applications and open questions, providing a milestone for past and an inspiration for future research.Comment: 23 pages, 1 figure, 1 tabl

    An evolutionary behavioral model for decision making

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    For autonomous agents the problem of deciding what to do next becomes increasingly complex when acting in unpredictable and dynamic environments pursuing multiple and possibly conflicting goals. One of the most relevant behavior-based model that tries to deal with this problem is the one proposed by Maes, the Bbehavior Network model. This model proposes a set of behaviors as purposive perception-action units which are linked in a nonhierarchical network, and whose behavior selection process is orchestrated by spreading activation dynamics. In spite of being an adaptive model (in the sense of self-regulating its own behavior selection process), and despite the fact that several extensions have been proposed in order to improve the original model adaptability, there is not a robust model yet that can self-modify adaptively both the topological structure and the functional purpose\ud of the network as a result of the interaction between the agent and its environment. Thus, this work proffers an innovative hybrid model driven by gene expression programming, which makes two main contributions: (1) given an initial set of meaningless and unconnected units, the evolutionary mechanism is able to build well-defined and robust behavior networks which are adapted and specialized to concrete internal agent's needs and goals; and (2)\ud the same evolutionary mechanism is able to assemble quite\ud complex structures such as deliberative plans (which operate in the long-term) and problem-solving strategies

    Evolutionary Dynamics in a Simple Model of Self-Assembly

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    We investigate the evolutionary dynamics of an idealised model for the robust self-assembly of two-dimensional structures called polyominoes. The model includes rules that encode interactions between sets of square tiles that drive the self-assembly process. The relationship between the model's rule set and its resulting self-assembled structure can be viewed as a genotype-phenotype map and incorporated into a genetic algorithm. The rule sets evolve under selection for specified target structures. The corresponding, complex fitness landscape generates rich evolutionary dynamics as a function of parameters such as the population size, search space size, mutation rate, and method of recombination. Furthermore, these systems are simple enough that in some cases the associated model genome space can be completely characterised, shedding light on how the evolutionary dynamics depends on the detailed structure of the fitness landscape. Finally, we apply the model to study the emergence of the preference for dihedral over cyclic symmetry observed for homomeric protein tetramers
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