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.

Evaluating the performance of a EuroDivisia index using artificial intelligence techniques

We compare two methods in order to predict inflation rates in Europe. One method uses a standard back propagation neural network and the other uses an evolutionary approach, where the network weights and the network architecture is evolved. Results indicate that back propagation produces superior results. However, the evolving network still produces reasonable results with the advantage that the experimental set-up is minimal. Also of interest is the fact that the Divisia measure of money is superior as a predictive tool over simple sum.

Evaluating the performance of a EuroDivisia index using artificial intelligence techniques

This paper compares two methods to predict in°ation rates in Europe. One method uses a standard back propagation neural network and the other uses an evolutionary approach, where the network weights and the network architecture are evolved. Results indicate that back propagation produces superior results. However, the evolving network still produces reasonable results with the advantage that the experimental set-up is minimal. Also of interest is the fact that the Divisia measure of money is superior as a predictive tool over simple sum.

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).

Identification of DNA-binding proteins by incorporating evolutionary information into pseudo amino acid composition via the top-n-gram approach

DNA-binding proteins are crucial for various cellular processes and hence have become an important target for both basic research and drug development. With the avalanche of protein sequences generated in the postgenomic age, it is highly desired to establish an automated method for rapidly and accurately identifying DNA-binding proteins based on their sequence information alone. Owing to the fact that all biological species have developed beginning from a very limited number of ancestral species, it is important to take into account the evolutionary information in developing such a high-throughput tool. In view of this, a new predictor was proposed by incorporating the evolutionary information into the general form of pseudo amino acid composition via the top-n-gram approach. It was observed by comparing the new predictor with the existing methods via both jackknife test and independent data-set test that the new predictor outperformed its counterparts. It is anticipated that the new predictor may become a useful vehicle for identifying DNA-binding proteins. It has not escaped our notice that the novel approach to extract evolutionary information into the formulation of statistical samples can be used to identify many other protein attributes as well.

Evolutionary neurobiology and art

In his important book on evolutionary theory, Darwin's Dangerous Idea, Daniel Dennett warns that Darwin's idea seeps through every area of human discourse like a "universal acid" (Dennett, 1995). Art and the aesthetic response cannot escape its influence. So my approach in this chapter is essentially naturalistic. Friedrich Nietzsche writes of observing the human comedy from afar, "like a cold angel...without anger, but without warmth" (Nietzsche, 1872, p. 164). Whether Nietzsche, of all people, could have done this is a matter of debate. But we know what he means. It describes a stance outside the human world as if looking down on human folly from Mount Olympus. From this stance, humans, their art and neurology are all part of the natural world, all part of the evolutionary process, the struggle for existence. The anthropologist David Dutton, in his contribution to the Routledge Companion to Aesthetics, says that all humans have an aesthetic sense (Dutton, 2001). It is a human universal. Biologists argue that such universals have an evolutionary basis. Furthermore, many have argued that not only humans but also animals, at least the higher mammals and birds, have an appreciation of the beautiful and the ugly (Eibl-Eibesfeldt, 1988).11Charles Darwin indeed writes "Birds appear to be the most aesthetic of all animals, excepting, of course, man, and they have nearly the same sense of the beautiful that we have" (1871, The Descent of Man and Selection in Relation to Sex, London: John Murray, vol.2, xiii, 39). This again suggests that aesthetics has an evolutionary origin. In parenthesis here, I should perhaps say that I am well aware of the criticism leveled at evolutionary psychology. I am well aware that it has been attacked as just so many "just-so" stories. This is neither the time nor the place to mount a defense but simply just to say that I believe that a defense is eminently feasible. © 2006 Elsevier Inc. All rights reserved.

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.

Operation sequencing optimization for five-axis prismatic parts using a particle swarm optimization approach

Operation sequencing is one of the crucial tasks in process planning. However, it is an intractable process to identify an optimized operation sequence with minimal machining cost in a vast search space constrained by manufacturing conditions. Also, the information represented by current process plan models for three-axis machining is not sufficient for five-axis machining owing to the two extra degrees of freedom and the difficulty of set-up planning. In this paper, a representation of process plans for five-axis machining is proposed, and the complicated operation sequencing process is modelled as a combinatorial optimization problem. A modern evolutionary algorithm, i.e. the particle swarm optimization (PSO) algorithm, has been employed and modified to solve it effectively. Initial process plan solutions are formed and encoded into particles of the PSO algorithm. The particles 'fly' intelligently in the search space to achieve the best sequence according to the optimization strategies of the PSO algorithm. Meanwhile, to explore the search space comprehensively and to avoid being trapped into local optima, several new operators have been developed to improve the particle movements to form a modified PSO algorithm. A case study used to verify the performance of the modified PSO algorithm shows that the developed PSO can generate satisfactory results in optimizing the process planning problem. © IMechE 2009.