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.

Long-range translational coupling in single-stranded RNA bacteriophages: an evolutionary analysis

In coliphage MS2 RNA a long-distance interaction (LDI) between an internal segment of the upstream coat gene and the start region of the replicase gene prevents initiation of replicase synthesis in the absence of coat gene translation. Elongating ribosomes break up the repressor LDI and thus activate the hidden initiation site. Expression studies on partial MS2 cDNA clones identified base pairing between 1427-1433 and 1738-1744, the so-called Min Jou (MJ) interaction, as the molecular basis for the long-range coupling mechanism. Here, we examine the biological significance of this interaction for the control of replicase gene translation. The LDI was disrupted by mutations in the 3'-side and the evolutionary adaptation was monitored upon phage passaging. Two categories of pseudorevertants emerged. The first type had restored the MJ interaction but not necessarily the native sequence. The pseudorevertants of the second type acquired a compensatory substitution some 80 nt downstream of the MJ interaction that stabilizes an adjacent LDI. In one examined case we confirmed that the second site mutations had restored coat-replicase translational coupling. Our results show the importance of translational control for fitness of the phage. They also reveal that the structure that buries the replicase start extends to structure elements bordering the MJ interaction.

Evolutionary neurobusiness

Numerous scientific disciplines suffer from a common epistemological ailment. They tend to generate impressive bodies of empirical knowledge that are otherwise disjointed. The key force that shapes this reality is the lack of organizing meta-frameworks that are capable of otherwise creating a consilient body of core knowledge. In the current paper, we seek to demonstrate the synthetic value of evolutionary theory across a wide range of neuro-business disciplines including neuroeconomics, neuromarketing, neuroentrepreneurship, and organizational neuroscience. Neuroscientists operating at the junction of the brain sciences and a wide range of business disciplines stand to benefit in recognizing that the minds of Homo consumericus, Homo corporaticus, and Homo economicus have been forged by Darwinian forces that have shaped all living organisms. A complete and accurate understanding of most neuro-business phenomena requires that these be tackled at both the proximate (i.e., how something operates) and ultimate (its adaptive function) levels.

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.

Evolutionary neural networks : models and applications

The scaling problems which afflict attempts to optimise neural networks (NNs) with genetic algorithms (GAs) are disclosed. A novel GA-NN hybrid is introduced, based on the bumptree, a little-used connectionist model. As well as being computationally efficient, the bumptree is shown to be more amenable to genetic coding lthan other NN models. A hierarchical genetic coding scheme is developed for the bumptree and shown to have low redundancy, as well as being complete and closed with respect to the search space. When applied to optimising bumptree architectures for classification problems the GA discovers bumptrees which significantly out-perform those constructed using a standard algorithm. The fields of artificial life, control and robotics are identified as likely application areas for the evolutionary optimisation of NNs. An artificial life case-study is presented and discussed. Experiments are reported which show that the GA-bumptree is able to learn simulated pole balancing and car parking tasks using only limited environmental feedback. A simple modification of the fitness function allows the GA-bumptree to learn mappings which are multi-modal, such as robot arm inverse kinematics. The dynamics of the 'geographic speciation' selection model used by the GA-bumptree are investigated empirically and the convergence profile is introduced as an analytical tool. The relationships between the rate of genetic convergence and the phenomena of speciation, genetic drift and punctuated equilibrium arc discussed. The importance of genetic linkage to GA design is discussed and two new recombination operators arc introduced. The first, linkage mapped crossover (LMX) is shown to be a generalisation of existing crossover operators. LMX provides a new framework for incorporating prior knowledge into GAs.Its adaptive form, ALMX, is shown to be able to infer linkage relationships automatically during genetic search.

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.

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.

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).

Exposing market mechanism design trade-offs via multi-objective evolutionary search

Market mechanisms are a means by which resources in contention can be allocated between contending parties, both in human economies and those populated by software agents. Designing such mechanisms has traditionally been carried out by hand, and more recently by automation. Assessing these mechanisms typically involves them being evaluated with respect to multiple conflicting objectives, which can often be nonlinear, noisy, and expensive to compute. For typical performance objectives, it is known that designed mechanisms often fall short on being optimal across all objectives simultaneously. However, in all previous automated approaches, either only a single objective is considered, or else the multiple performance objectives are combined into a single objective. In this paper we do not aggregate objectives, instead considering a direct, novel application of multi-objective evolutionary algorithms (MOEAs) to the problem of automated mechanism design. This allows the automatic discovery of trade-offs that such objectives impose on mechanisms. We pose the problem of mechanism design, specifically for the class of linear redistribution mechanisms, as a naturally existing multi-objective optimisation problem. We apply a modified version of NSGA-II in order to design mechanisms within this class, given economically relevant objectives such as welfare and fairness. This application of NSGA-II exposes tradeoffs between objectives, revealing relationships between them that were otherwise unknown for this mechanism class. The understanding of the trade-off gained from the application of MOEAs can thus help practitioners with an insightful application of discovered mechanisms in their respective real/artificial markets.

Fitness and novelty in evolutionary art

In this paper the effects of introducing novelty search in evolutionary art are explored. Our algorithm combines fitness and novelty metrics to frame image evolution as a multi-objective optimisation problem, promoting the creation of images that are both suitable and diverse. The method is illustrated by using two evolutionary art engines for the evolution of figurative objects and context free design grammars. The results demonstrate the ability of the algorithm to obtain a larger set of fit images compared to traditional fitness-based evolution, regardless of the engine used.

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.