Zarathustra's evolutionary epistemology

Friedrich Nietzsche was the first great philosopher to be influenced at the core by Darwinian ideas. He regarded Also Sprach Zarathustra as his masterpiece and most subsequent commentators have agreed. There have been many interpretations of the Zarathustra, and like all great works it has many levels of meaning. An exposition in terms of evolutionary epistemology, however, has not yet been attempted. This article rectifies this omission and shows how Nietzsche's work carries Darwinian ideas into the domain of philosophical anthropology. It shows through the prism of Nietzsche's mature thought some of the consequences of an evolutionary epistemology both in opening up alternative visions of the world and in permitting a profound criticism of our commonsense metaphysics and ontology. © 1992.

Resource allocation in decentralised computational systems:an evolutionary market-based approach

We present a novel market-based method, inspired by retail markets, for resource allocation in fully decentralised systems where agents are self-interested. Our market mechanism requires no coordinating node or complex negotiation. The stability of outcome allocations, those at equilibrium, is analysed and compared for three buyer behaviour models. In order to capture the interaction between self-interested agents, we propose the use of competitive coevolution. Our approach is both highly scalable and may be tuned to achieve specified outcome resource allocations. We demonstrate the behaviour of our approach in simulation, where evolutionary market agents act on behalf of service providing nodes to adaptively price their resources over time, in response to market conditions. We show that this leads the system to the predicted outcome resource allocation. Furthermore, the system remains stable in the presence of small changes in price, when buyers' decision functions degrade gracefully. © 2009 The Author(s).

Evolutionary market agents for resource allocation in decentralised systems

We introduce self-interested evolutionary market agents, which act on behalf of service providers in a large decentralised system, to adaptively price their resources over time. Our agents competitively co-evolve in the live market, driving it towards the Bertrand equilibrium, the non-cooperative Nash equilibrium, at which all sellers charge their reserve price and share the market equally. We demonstrate that this outcome results in even load-balancing between the service providers. Our contribution in this paper is twofold; the use of on-line competitive co-evolution of self-interested service providers to drive a decentralised market towards equilibrium, and a demonstration that load-balancing behaviour emerges under the assumptions we describe. Unlike previous studies on this topic, all our agents are entirely self-interested; no cooperation is assumed. This makes our problem a non-trivial and more realistic one.

Biology of sensory systems

Since publication of the first edition, huge developments have taken place in sensory biology research and new insights have been provided in particular by molecular biology. These show the similarities in the molecular architecture and in the physiology of sensory cells across species and across sensory modality and often indicate a common ancestry dating back over half a billion years. Biology of Sensory Systems has thus been completely revised and takes a molecular, evolutionary and comparative approach, providing an overview of sensory systems in vertebrates, invertebrates and prokaryotes, with a strong focus on human senses. Written by a renowned author with extensive teaching experience, the book covers, in six parts, the general features of sensory systems, the mechanosenses, the chemosenses, the senses which detect electromagnetic radiation, other sensory systems including pain, thermosensitivity and some of the minority senses and, finally, provides an outline and discussion of philosophical implications. New in this edition: - Greater emphasis on molecular biology and intracellular mechanisms - New chapter on genomics and sensory systems - Sections on TRP channels, synaptic transmission, evolution of nervous systems, arachnid mechanosensitive sensilla and photoreceptors, electroreception in the Monotremata, language and the FOXP2 gene, mirror neurons and the molecular biology of pain - Updated passages on human olfaction and gustation. Over four hundred illustrations, boxes containing supplementary material and self-assessment questions and a full bibliography at the end of each part make Biology of Sensory Systems essential reading for undergraduate students of biology, zoology, animal physiology, neuroscience, anatomy and physiological psychology. The book is also suitable for postgraduate students in more specialised courses such as vision sciences, optometry, neurophysiology, neuropathology, developmental biology.

Biology of sensory systems

A comprehensive and highly illustrated text providing a broad and invaluable overview of sensory systems at the molecular, cellular and neurophysiological level of vertebrates, invertebrates and prokaryotes. It retains a strong focus on human systems, and takes an evolutionary and comparative approach to review the mechanosenses, chemosenses, photosenses, and other sensory systems including those for detecting pain, temperature electric and magnetic fields etc. It incorporates exciting and significant new insights provided by molecular biology which demonstrate how similar the molecular architecture and physiology of sensory cells are across species and across sensory modality, often indicationg a common ancestry dating back over half a billion years. Written by a renowned author, with extensive teaching experience in the biology of sensory systems, this book includes: - Over 400 illustrations - Self–assessment questions - Full bibliography preceded by short bibliographical essays - Boxes containing useful supplementary material. It will be invaluable for undergraduates and postgraduates studying biology, zoology, animal physiology, neuroscience, anatomy, molecular biology, physiological psychology and related courses.

Modelling human preference in evolutionary art

Creative activities including arts are characteristic to humankind. Our understanding of creativity is limited, yet there is substantial research trying to mimic human creativity in artificial systems and in particular to produce systems that automatically evolve art appreciated by humans. We propose here to model human visual preference by a set of aesthetic measures identified through observation of human selection of images and then use these for automatic evolution of aesthetic images. © 2011 Springer-Verlag.

Modelling the underlying principles of human aesthetic preference in evolutionary art

Our understanding of creativity is limited, yet there is substantial research trying to mimic human creativity in artificial systems and in particular to produce systems that automatically evolve art appreciated by humans. We propose here to study human visual preference through observation of nearly 500 user sessions with a simple evolutionary art system. The progress of a set of aesthetic measures throughout each interactive user session is monitored and subsequently mimicked by automatic evolution in an attempt to produce an image to the liking of the human user.

A diversity dilemma in evolutionary markets

Markets are useful mechanisms for performing resource al- location in fully decentralised computational and other systems, since they can possess a range of desirable properties, such as efficiency, decentralisation, robustness and scalability. In this paper we investigate the behaviour of co-evolving evolutionary market agents as adaptive offer generators for sellers in a multi-attribute posted-offer market. We demonstrate that the evolutionary approach enables sellers to automatically position themselves in market niches, created by heterogeneous buyers. We find that a trade-off exists for the evolutionary sellers between maintaining high population diversity to facilitate movement between niches and low diversity to exploit the current niche and maximise cumulative payoff. We characterise the trade-off from the perspective of the system as a whole, and subsequently from that of an individual seller. Our results highlight a decision on risk aversion for resource providers, but crucially we show that rational self-interested sellers would not adopt the behaviour likely to lead to the ideal result from the system point of view.

Evolutionary market agents and heterogeneous service providers:achieving desired resource allocations

In future massively distributed service-based computational systems, resources will span many locations, organisations and platforms. In such systems, the ability to allocate resources in a desired configuration, in a scalable and robust manner, will be essential.We build upon a previous evolutionary market-based approach to achieving resource allocation in decentralised systems, by considering heterogeneous providers. In such scenarios, providers may be said to value their resources differently. We demonstrate how, given such valuations, the outcome allocation may be predicted. Furthermore, we describe how the approach may be used to achieve a stable, uneven load-balance of our choosing. We analyse the system's expected behaviour, and validate our predictions in simulation. Our approach is fully decentralised; no part of the system is weaker than any other. No cooperation between nodes is assumed; only self-interest is relied upon. A particular desired allocation is achieved transparently to users, as no modification to the buyers is required.

The evolution of learning systems:to Bayes or not to be

Bayesian algorithms pose a limit to the performance learning algorithms can achieve. Natural selection should guide the evolution of information processing systems towards those limits. What can we learn from this evolution and what properties do the intermediate stages have? While this question is too general to permit any answer, progress can be made by restricting the class of information processing systems under study. We present analytical and numerical results for the evolution of on-line algorithms for learning from examples for neural network classifiers, which might include or not a hidden layer. The analytical results are obtained by solving a variational problem to determine the learning algorithm that leads to maximum generalization ability. Simulations using evolutionary programming, for programs that implement learning algorithms, confirm and expand the results. The principal result is not just that the evolution is towards a Bayesian limit. Indeed it is essentially reached. In addition we find that evolution is driven by the discovery of useful structures or combinations of variables and operators. In different runs the temporal order of the discovery of such combinations is unique. The main result is that combinations that signal the surprise brought by an example arise always before combinations that serve to gauge the performance of the learning algorithm. This latter structures can be used to implement annealing schedules. The temporal ordering can be understood analytically as well by doing the functional optimization in restricted functional spaces. We also show that there is data suggesting that the appearance of these traits also follows the same temporal ordering in biological systems. © 2006 American Institute of Physics.

Temperature management for heterogeneous multi-core FPGAs using adaptive evolutionary multi-objective approaches

Heterogeneous multi-core FPGAs contain different types of cores, which can improve efficiency when used with an effective online task scheduler. However, it is not easy to find the right cores for tasks when there are multiple objectives or dozens of cores. Inappropriate scheduling may cause hot spots which decrease the reliability of the chip. Given that, our research builds a simulating platform to evaluate all kinds of scheduling algorithms on a variety of architectures. On this platform, we provide an online scheduler which uses multi-objective evolutionary algorithm (EA). Comparing the EA and current algorithms such as Predictive Dynamic Thermal Management (PDTM) and Adaptive Temperature Threshold Dynamic Thermal Management (ATDTM), we find some drawbacks in previous work. First, current algorithms are overly dependent on manually set constant parameters. Second, those algorithms neglect optimization for heterogeneous architectures. Third, they use single-objective methods, or use linear weighting method to convert a multi-objective optimization into a single-objective optimization. Unlike other algorithms, the EA is adaptive and does not require resetting parameters when workloads switch from one to another. EAs also improve performance when used on heterogeneous architecture. A efficient Pareto front can be obtained with EAs for the purpose of multiple objectives.