A technique for evaluation of interactive evolutionary systems

Very large scale scheduling and planning tasks cannot be effectively addressed by fully automated schedule optimisation systems, since many key factors which govern 'fitness' in such cases are unformalisable. This raises the question of an interactive (or collaborative) approach, where fitness is assigned by the expert user. Though well-researched in the domains of interactively evolved art and music, this method is as yet rarely used in logistics. This paper concerns a difficulty shared by all interactive evolutionary systems (IESs), but especially those used for logistics or design problems. The difficulty is that objective evaluation of IESs is severely hampered by the need for expert humans in the loop. This makes it effectively impossible to, for example, determine with statistical confidence any ranking among a decent number of configurations for the parameters and strategy choices. We make headway into this difficulty with an Automated Tester (AT) for such systems. The AT replaces the human in experiments, and has parameters controlling its decision-making accuracy (modelling human error) and a built-in notion of a target solution which may typically be at odds with the solution which is optimal in terms of formalisable fitness. Using the AT, plausible evaluations of alternative designs for the IES can be done, allowing for (and examining the effects of) different levels of user error. We describe such an AT for evaluating an IES for very large scale planning.

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.

A economia ecológica e evolucionária de Georgescu-Roegen

The ecological and evolutionary economics of Georgescu-Roegen. The main argument of this paper is that Georgescu-Roegen's contributions represent a major disruption with economics' pre-analytic vision. He rejected at the same time both the closed and circular view of the economy and the mechanic analogies that oriented economics in the past century. Even though his influence has been felt mainly in the field of ecological economics, his epistemological contributions represent a major challenge to equilibrium thinking. Nowadays, treating economic systems as complex and evolutionary systems is becoming not only acceptable, but also a trend in the way political economy is made. We defend that Georgescu-Roegen's disruption represents a scientific revolution in economics, in the sense attributed by Kuhn.

Ciencias de la complejidad: estado actual

Los aportes teóricos y aplicados de la complejidad en economía han tomado tantas direcciones y han sido tan frenéticos en las últimas décadas, que no existe un trabajo reciente, hasta donde conocemos, que los compile y los analice de forma integrada. El objetivo de este proyecto, por tanto, es desarrollar un estado situacional de las diferentes aplicaciones conceptuales, teóricas, metodológicas y tecnológicas de las ciencias de la complejidad en la economía. Asimismo, se pretende analizar las tendencias recientes en el estudio de la complejidad de los sistemas económicos y los horizontes que las ciencias de la complejidad ofrecen de cara al abordaje de los fenómenos económicos del mundo globalizado contemporáneo.

Robust parameter settings of evolutionary algorithms for the optimisation of agricultural systems models

Numerical optimisation methods are being more commonly applied to agricultural systems models, to identify the most profitable management strategies. The available optimisation algorithms are reviewed and compared, with literature and our studies identifying evolutionary algorithms (including genetic algorithms) as superior in this regard to simulated annealing, tabu search, hill-climbing, and direct-search methods. Results of a complex beef property optimisation, using a real-value genetic algorithm, are presented. The relative contributions of the range of operational options and parameters of this method are discussed, and general recommendations listed to assist practitioners applying evolutionary algorithms to the solution of agricultural systems. (C) 2001 Elsevier Science Ltd. All rights reserved.

CIDER - envisaging a COTS communication infrastructure for evolutionary dependable real-time systems

It is foreseen that future dependable real-time systems will also have to meet flexibility, adaptability and reconfigurability requirements. Considering the distributed nature of these computing systems, a communication infrastructure that permits to fulfil all those requirements is thus of major importance. Although Ethernet has been used primarily as an information network, there is a strong belief that some very recent technological advances will enable its use in dependable applications with real-time requirements. Indeed, several recently standardised mechanisms associated with Switched-Ethernet seem to be promising to enable communication infrastructures to support hard real-time, reliability and flexible distributed applications. This paper describes the motivation and the work being developed within the CIDER (Communication Infrastructure for Dependable Evolvable Real-Time Systems) project, which envisages the use of COTS Ethernet as an enabling technology for future dependable real-time systems. It is foreseen that the CIDER approach will constitute a relevant stream of research since it will bring together cutting edge research in the field of real-time and dependable distributed systems and the industrial eagerness to expand Ethernet responsabilities to support dependable real-time applications.

The evolution of inbred social systems in spiders and other organisms: from short-term gains to long-term evolutionary dead-ends?

The question of why some social systems have evolved close inbreeding is particularly intriguing given expected short- and long-term negative effects of this breeding system. Using social spiders as a case study, we quantitatively show that the potential costs of avoiding inbreeding through dispersal and solitary living could have outweighed the costs of inbreeding depression in the origin of inbred spider sociality. We further review the evidence that despite being favored in the short term, inbred spider sociality may constitute in the long run an evolutionary dead end. We also review other cases, such as the naked mole rats and some bark and ambrosia beetles, mites, psocids, thrips, parasitic ants, and termites, in which inbreeding and sociality are associated and the evidence for and against this breeding system being, in general, an evolutionary dead end.

Towards robust network based complex systems : from evolutionary cellular automata to biological models

Abstract Sitting between your past and your future doesn't mean you are in the present. Dakota Skye Complex systems science is an interdisciplinary field grouping under the same umbrella dynamical phenomena from social, natural or mathematical sciences. The emergence of a higher order organization or behavior, transcending that expected of the linear addition of the parts, is a key factor shared by all these systems. Most complex systems can be modeled as networks that represent the interactions amongst the system's components. In addition to the actual nature of the part's interactions, the intrinsic topological structure of underlying network is believed to play a crucial role in the remarkable emergent behaviors exhibited by the systems. Moreover, the topology is also a key a factor to explain the extraordinary flexibility and resilience to perturbations when applied to transmission and diffusion phenomena. In this work, we study the effect of different network structures on the performance and on the fault tolerance of systems in two different contexts. In the first part, we study cellular automata, which are a simple paradigm for distributed computation. Cellular automata are made of basic Boolean computational units, the cells; relying on simple rules and information from- the surrounding cells to perform a global task. The limited visibility of the cells can be modeled as a network, where interactions amongst cells are governed by an underlying structure, usually a regular one. In order to increase the performance of cellular automata, we chose to change its topology. We applied computational principles inspired by Darwinian evolution, called evolutionary algorithms, to alter the system's topological structure starting from either a regular or a random one. The outcome is remarkable, as the resulting topologies find themselves sharing properties of both regular and random network, and display similitudes Watts-Strogtz's small-world network found in social systems. Moreover, the performance and tolerance to probabilistic faults of our small-world like cellular automata surpasses that of regular ones. In the second part, we use the context of biological genetic regulatory networks and, in particular, Kauffman's random Boolean networks model. In some ways, this model is close to cellular automata, although is not expected to perform any task. Instead, it simulates the time-evolution of genetic regulation within living organisms under strict conditions. The original model, though very attractive by it's simplicity, suffered from important shortcomings unveiled by the recent advances in genetics and biology. We propose to use these new discoveries to improve the original model. Firstly, we have used artificial topologies believed to be closer to that of gene regulatory networks. We have also studied actual biological organisms, and used parts of their genetic regulatory networks in our models. Secondly, we have addressed the improbable full synchronicity of the event taking place on. Boolean networks and proposed a more biologically plausible cascading scheme. Finally, we tackled the actual Boolean functions of the model, i.e. the specifics of how genes activate according to the activity of upstream genes, and presented a new update function that takes into account the actual promoting and repressing effects of one gene on another. Our improved models demonstrate the expected, biologically sound, behavior of previous GRN model, yet with superior resistance to perturbations. We believe they are one step closer to the biological reality.

Achieving EU energy targets – Impacts of evolutionary and revolutionary transition paths on power systems

Ambitious energy targets set by EU put pressures to increase share of renewable electricity supply in this and next decades and therefore, some EU member countries have boosted increasing renewable energy generation capacity by implementing subsidy schemes on national level. In this study, two different change approaches to increase renewable energy supply and increase self-sufficiency of supply are assessed with respect to their impacts on power system, electricity market and electricity generation costs in Finland. It is obtained that the current electricity generation costs are high compared to opportunities of earnings from present-day investor’s perspective. In addition, the growth expectations of consumptions and the price forecasts do not stimulate investing in new generation capacity. Revolutionary transition path is driven by administrative and political interventions to achieve the energy targets. Evolutionary transition path is driven by market-based mechanisms, such as market itself and emission trading scheme. It is obtained in this study that in the revolutionary transition path operation of market-based mechanisms is distorted to some extent and it is likely that this path requires providing more public financial resources compared to evolutionary transition path. In the evolutionary transition path the energy targets are not achieved as quickly but market-based mechanisms function better and investment environment endures more stable compared to revolutionary transition path.

The evolutionary basis for differences between the immune systems of man, mouse, pig and ruminants

Studying the pathogenesis of an infectious disease like colibacillosis requires an understanding of the responses of target hosts to the organism both as a pathogen and as a commensal. The mucosal immune system constitutes the primary line of defence against luminal micro-organisms. The immunoglobulin-superfamily-based adaptive immune system evolved in the earliest jawed vertebrates, and the adaptive and innate immune system of humans, mice, pigs and ruminants co-evolved in common ancestors for approximately 300 million years. The divergence occurred only 100 mya and, as a consequence, most of the fundamental immunological mechanisms are very similar. However, since pressure on the immune system comes from rapidly evolving pathogens, immune systems must also evolve rapidly to maintain the ability of the host to survive and reproduce. As a consequence, there are a number of areas of detail where mammalian immune systems have diverged markedly from each other, such that results obtained in one species are not always immediately transferable to another. Thus, animal models of specific diseases need to be selected carefully, and the results interpreted with caution. Selection is made simpler where specific host species like cattle and pigs can be both target species and reservoirs for human disease, as in infections with Escherichia coli.

A Diagnostic Assessment Of Evolutionary Multiobjective Optimization For Water Resources Systems

This study contributes a rigorous diagnostic assessment of state-of-the-art multiobjective evolutionary algorithms (MOEAs) and highlights key advances that the water resources field can exploit to better discover the critical tradeoffs constraining our systems. This study provides the most comprehensive diagnostic assessment of MOEAs for water resources to date, exploiting more than 100,000 MOEA runs and trillions of design evaluations. The diagnostic assessment measures the effectiveness, efficiency, reliability, and controllability of ten benchmark MOEAs for a representative suite of water resources applications addressing rainfall-runoff calibration, long-term groundwater monitoring (LTM), and risk-based water supply portfolio planning. The suite of problems encompasses a range of challenging problem properties including (1) many-objective formulations with 4 or more objectives, (2) multi-modality (or false optima), (3) nonlinearity, (4) discreteness, (5) severe constraints, (6) stochastic objectives, and (7) non-separability (also called epistasis). The applications are representative of the dominant problem classes that have shaped the history of MOEAs in water resources and that will be dominant foci in the future. Recommendations are provided for which modern MOEAs should serve as tools and benchmarks in the future water resources literature.