Alan Turing y los orígenes de la investigación multidisciplinar

En marzo de 2013 el Museo de la Ciencia de Londres daba la noticia de que uno de los hallazgos de Alan Turing, la invención de una máquina teórica –fundamento de los ordenadores actuales-, había sido elegida por el público como la invención británica más importante del siglo XX. Sin embargo, más allá de su formidable e influyente legado científico, otro de los legados de este genial científico fue la forma en que Turing “hacía la ciencia”, contribuyendo con su figura al nacimiento de lo que hoy se conoce como investigación multidisciplina

The \u27Crisis of Noosphere\u27 as a Limiting Factor to Achieve the Point of Technological Singularity

One of the most significant developments in the history of human being is the invention of a way of keeping records of human knowledge, thoughts and ideas. In 1926, the work of several thinkers such as Edouard Le Roy, Vladimir Vernadsky and Teilhard de Chardin led to the concept of noosphere, the idea that human cognition and knowledge transforms the biosphere into something like a thinking layer of the planet. At present, it is commonly accepted by some thinkers that the Internet is the medium that will give life to noosphere. According to Vinge and Kurzweil’s technological singularity hypothesis, noosphere would in a future be the natural environment in which a \u27human machine superintelligence\u27 would emerge to reach the point of technological singularity. In this article we show by means of numerical models that it is impossible for our civilization to reach the point of technological singularity in a near future. We propose that this point could be reached only if Internet data centers were based on "computer machines" that are more effective in terms of hardware and power consumption than the current ones. Finally, we speculate about \u27Nooscomputers\u27 or N-computers, as hypothetical machines oriented not only to the management of information, but also knowledge, and much more efficient in terms of electricity consumption than current computers. Possibly a civilization based on N-computers would allow us to successfully reach the point of technological singularity

Modeling, Simulation and Application of Bacterial Transduction in Genetic Algorithms

At present, all methods in Evolutionary Computation are bioinspired in the fundamental principles of neo-Darwinism as well as on a vertical gene transfer. Thus, on a mechanism in which an organism receives genetic material from its ancestor. Horizontal, lateral or cross-population gene transfer is any process in which an organism transfers a genetic segment to another one that is not its offspring. Virus transduction is one of the key mechanisms of horizontal gene propagation in microorganism (e.g. bacteria). In the present paper, we model and simulate a transduction operator, exploring a possible role and usefulness of transduction in a genetic algorithm. The genetic algorithm including transduction has been named PETRI (abbreviation of Promoting Evolution Through Reiterated Infection). The efficiency and performance of this algorithm was evaluated using a benchmark function and the 0/1 knapsack problem. The utility was illustrated designing an AM radio receiver, optimizing the main features of the electronic components of the AM radio circuit as well as those of the radio enclosure. Our results shown how PETRI approaches to higher fitness values as transduction probability comes near to 100%. The conclusion is that transduction improves the performance of a genetic algorithm, assuming a population divided among several sub-populations or ‘bacterial colonies’

Cheating for problem solving: a genetic algorithm with social interactions

We propose a variation of the standard genetic algorithm that incorporates social interaction between the individuals in the population. Our goal is to understand the evolutionary role of social systems and its possible application as a non-genetic new step in evolutionary algorithms. In biological populations, i.e. animals, even human beings and microorganisms, social interactions often affect the fitness of individuals. It is conceivable that the perturbation of the fitness via social interactions is an evolutionary strategy to avoid trapping into local optimum, thus avoiding a fast convergence of the population. We model the social interactions according to Game Theory. The population is, therefore, composed by cooperator and defector individuals whose interactions produce payoffs according to well known game models (prisoner's dilemma, chicken game, and others). Our results on Knapsack problems show, for some game models, a significant performance improvement as compared to a standard genetic algorithm

The beauty of the mammalian vascular system

Beauty is a characteristic of objects that provides a perceptual experience of pleasure. In nature, aesthetic appreciation thereof has given rise to the mathematical search for good series (e.g. the Fibonacci series) and proportions (e.g. the Golden proportion) as important elements of beauty. In 1928 the mathematician George David Birkhoff introduced a formula for aesthetic measurement of an object. Birkhoff equation defines the aesthetic value as the amount of order divided by the complexity of the product. These two features can be measured easily in poetry, music, painting, architecture, etc. In the fine arts, it is the artist who manipulates both these features, but how does nature manage order and complexity in living organisms or their parts? Here we show how Birkhoff equation, applied to the mammalian vascular system of eight representative animals, results in new insights into the organization of the animal vascular system. We found that order and complexity are highly correlated in the mammalian vascular system (_R^2^_=0.9511). Accordingly, in nature both features are not independently managed in the manner of artists. We found significant differences among the Birkhoff aesthetic values in the mammalian arterial system, whereas no such differences exist in the venous system. We anticipate our approach to be useful in the study of morphogenesis and evolution of tree-like structures, employing the Birkhoff aesthetic value as a simple tool for conducting such studies

Cheating for problem solving: a genetic algorithm with social interactions

We propose a variation of the standard genetic algorithm that incorporates social interaction between the individuals in the population. Our goal is to understand the evolutionary role of social systems and its possible application as a non-genetic new step in evolutionary algorithms. In biological populations, i.e. animals, even human beings and microorganisms, social interactions often affect the fitness of individuals. It is conceivable that the perturbation of the fitness via social interactions is an evolutionary strategy to avoid trapping into local optimum, thus avoiding a fast convergence of the population. We model the social interactions according to Game Theory. The population is, therefore, composed by cooperator and defector individuals whose interactions produce payoffs according to well known game models (prisoner's dilemma, chicken game, and others). Our results on Knapsack problems show, for some game models, a significant performance improvement as compared to a standard genetic algorithm

Eye evolution simulation with a genetic algorithm based on the hypothesis of Nilsson and Pelger

The present work addresses for the first time the simulation of the evolution of an elemental eye by means of a simple genetic algorithm. The problem of the gradual evolution of a structure as complex as the eye was raised by Darwin, being still at the beginning of the 21st century a source of controversy between creationists and evolutionists. Taking as a starting point the paper of Nilsson and Pelger and their hypothesis that the evolution of the eye can be studied if we limit ourselves to its optical geometry, we show how eye evolution could take place gradually applying the principle of natural selection. Our model is limited to studying how an array of photosensitive epithelial cells is bent gradually to achieve a camera obscura

Prion crystalization model and its application to recognition pattern

This paper introduces APA (?Artificial Prion Assembly?): a pattern recognition system based on artificial prion crystalization. Specifically, the system exhibits the capability to classify patterns according to the resulting prion self- assembly simulated with cellular automata. Our approach is inspired in the biological process of proteins aggregation, known as prions, which are assembled as amyloid fibers related with neurodegenerative disorders

Modeling, simulation and application of bacterial transduction in genetic algorithms

At present, all methods in Evolutionary Computation are bioinspired by the fundamental principles of neo-Darwinism, as well as by a vertical gene transfer. Virus transduction is one of the key mechanisms of horizontal gene propagation in microorganisms (e.g. bacteria). In the present paper, we model and simulate a transduction operator, exploring the possible role and usefulness of transduction in a genetic algorithm. The genetic algorithm including transduction has been named PETRI (abbreviation of Promoting Evolution Through Reiterated Infection). Our results showed how PETRI approaches higher fitness values as transduction probability comes close to 100%. The conclusion is that transduction improves the performance of a genetic algorithm, assuming a population divided among several sub-populations or ?bacterial colonies?