Biomimetic Engineering

Humankind is a privileged animal species for many reasons. A remarkable one is its ability to conceive and manufacture objects. Human industry is indeed leading the various winning strategies (along with language and culture) that has permitted this primate to extraordinarily increase its life expectancy and proliferation rate. (It is indeed so successful, that it now threatens the whole planet.) The design of this industry kicks off in the brain, a computing machine particularly good at storing, recognizing and associating patterns. Even in a time when human beings tend to populate non-natural, man-made environments, the many forms, colorings, textures and behaviors of nature continuously excite our senses and blend in our thoughts, even more deeply during childhood. Then, it would be exaggerated to say that Biomimetics is a brand new strategy. As long as human creation is based on previously acquired knowledge and experiences, it is not surprising that engineering, the arts, and any form of expression, is influenced by nature’s way to some extent. The design of human industry has evolved from very simple tools, to complex engineering devices. Nature has always provided us with a rich catalog of excellent materials and inspiring designs. Now, equipped with new machinery and techniques, we look again at Nature. We aim at mimicking not only its best products, but also its design principles. Organic life, as we know it, is indeed a vast pool of diversity. Living matter inhabits almost every corner of the terrestrial ecosphere. From warm open-air ecosystems to the extreme conditions of hot salt ponds, living cells have found ways to metabolize the sources of energy, and get organized in complex organisms of specialized tissues and organs that adapt themselves to the environment, and can modify the environment to their own needs as well. Life on Earth has evolved such a diverse portfolio of species that the number of designs, mechanisms and strategies that can actually be abstracted is astonishing. As August Krogh put it: "For a large number of problems there will be some animal of choice, on which it can be most conveniently studied". The scientific method starts with a meticulous observation of natural phenomena, and humans are particularly good at that game. In principle, the aim of science is to understand the physical world, but an observer’s mind can behave either as an engineer or as a scientist. The minute examination of the many living forms that surround us has led to the understanding of new organizational principles, some of which can be imported in our production processes. In practice, bio-inspiration can arise at very different levels of observation: be it social organization, the shape of an organism, the structure and functioning of organs, tissular composition, cellular form and behavior, or the detailed structure of molecules. Our direct experience of the wide portfolio of species found in nature, and their particular organs, have clearly favored that the initial models would come from the organism and organ levels. But the development of new techniques (on one hand to observe the micro- and nanostructure of living beings, and on the other to simulate the complex behavior of social communities) have significantly extended the domain of interest

Selfo: A class of self-organizing connection games

Selfo is defined as a class of abstract strategy board games subscribed to the category of connection games. Its name derives from the phenomenon of self-organization (i.e. the increase in a system’s organization without external guidance), since during the game the sets of pieces might flow in a coordinated way as they step on the board. Despite its very simple definition (“group all your pieces by moving in turns to adjacent cells”) complex self-organization processes takes place under concrete circumstances (a balanced distribution of pieces and similar levels of expertise in the players), and are the result of abrupt and deep changes in the tactics. Since a big number of variants have been found to meet the conditions for self-organization, the particular values given to the traditional parameters that define a game (i.e., board tiling, size and initial position, or number of pieces and players) are not so relevant. The Selfo class of connection games is defined, instead, by the interrelations among parameters in order to favor selforganization

Computación evolutiva: El legado de Darwin en la Ingeniería Informática

La biomimética es la disciplina que estudia la aplicación de diseños naturales en áreas como la ingeniería o la medicina. El tren bala japonés, el nylon o el velcro se crearon a partir de características propias de la rapidez del martín pescador, la elasticidad de la tela de araña o la sujeción al tejido del espinoso cardo alpino. Otra prueba más de que la naturaleza es sabia

AI Methods in Algorithmic Composition: A Comprehensive Survey

Algorithmic composition is the partial or total automation of the process of music composition by using computers. Since the 1950s, different computational techniques related to Artificial Intelligence have been used for algorithmic composition, including grammatical representations, probabilistic methods, neural networks, symbolic rule-based systems, constraint programming and evolutionary algorithms. This survey aims to be a comprehensive account of research on algorithmic composition, presenting a thorough view of the field for researchers in Artificial Intelligence.This study was partially supported by a grant for the MELOMICS project (IPT-300000-2010-010) from the Spanish Ministerio de Ciencia e Innovación, and a grant for the CAUCE project (TSI-090302-2011-8) from the Spanish Ministerio de Industria, Turismo y Comercio. The first author was supported by a grant for the GENEX project (P09-TIC- 5123) from the Consejería de Innovación y Ciencia de Andalucía

Evolution of form and function in a model of differentiated multicellular organisms with gene regulatory networks

The emergence of novelties, as a generator of diversity, in the form and function of the organisms have long puzzled biologists. The study of the developmental process and the anatomical properties of an organism provides scarce information into the means by which its morphology evolved. Some have argued that the very nature of novelty is believed to be linked to the evolution of gene regulation, rather than to the emergence of new structural genes. In order to gain further insight into the evolution of novelty and diversity, we describe a simple computational model of gene regulation that controls the development of locomotive multicellular organisms through a fixed set of simple structural genes. Organisms, modeled as two-dimensional spring networks, are simulated in a virtual environment to evaluate their steering skills for path-following. Proposed as a behavior-finding problem, this fitness function guides an evolutionary algorithm that produces structures whose function is well-adapted to the environment (i.e., good path-followers). We show that, despite the fixed simple set of structural genes, the evolution of gene regulation yields a rich variety of body plans, including symmetries, body segments, and modularity, resulting in a diversity of original behaviors to follow a simple path. These results suggest that the sole variation in the regulation of gene expression is a sufficient condition for the emergence of novelty and diversity.This work has been partially funded by the Sixth European Union Framework Program for Research and Technological Development, contract #028892

On the performance of some bioinspired genetic operators in complex structures evolution

Indirect encoding methods demand operators that minimize the disruption of conventional operators, widely studied in direct encoding approaches. While some efforts have already been done in this direction, the growing field of Genetics sheds new light on the dynamics of the nucleic acids, and their implications in the evolution of life on Earth. Here we model basic mechanisms of gene duplication and horizontal gene transfer, presenting preliminary results of its application to L-systems evolution. The first interesting finding is that, in the particular simplified framework proposed, most of these operations are only slightly disruptive allowing the structures to evolve without loosing what has been gained in the past. Large populations of L-systems have been evolved to meet simple restrictions on their phenotypic readout. A case-study is described: evolution of a form under changing conditions. Genotypic and phenotypic evolutions are discussed.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. This work has been partially funded by FP6 of the European Union under BioEmergences projec

Complex and Diverse Morphologies Can Develop from a Minimal Genomic Model

While development plays a critical role in the emergence of diversity, its mechanical and chemical actions are considered to be inextricably correlated with genetic control. Since in most extant species the complex growth from zygote to adult organism is orchestrated by a complex gene regulatory network (GRN), the prevalent view is that the evolution of diverse morphologies must result from the evolution of diverse GRN topologies. By contrast, this work focuses on the unique e ect of developmental processes through an abstract model of self-regulated structure without genetic regulation|only modulation of initial conditions. Here, morphologies are generated by a simple evolutionary algorithm searching for the longest instances of unfolding dynamics based on tensegrity graphs. The usual regulatory function of the genome is taken over by physical constraints in the graphs, making morphological diversity a pure product of structural complexi cation. By highlighting the potential of structural development, our model is relevant to both "structuralist" biological models and bio-inspired systems engineering.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

The Evolution of Controller-Free Molecular Motors from Spatial Constraints

Locomotion of robotic and virtual agents is a challenging task requiring the control of several degrees of freedom as well as the coordination of multiple subsystems. Traditionally, it is engineered by top-down design and finetuning of the agent’s morphology and controller. A relatively recent trend in fields such as evolutionary robotics, computer animation and artificial life has been the coevolution and mutual adaptation of the morphology and controller in computational agent models. However, the controller is generally modeled as a complex system, often a neural or gene regulatory network. In the present study, inspired by molecular biology and based on normal modal analysis, we formulate a behavior-finding framework for the design of bipedal agents that are able to walk along a filament and have no explicit control system. Instead, agents interact with their environment in a purely reactive way. A simple mutation operator, based on physical relaxation, is used to drive the evolutionary search. Results show that gait patterns can be evolutionarily engineered from the spatial interaction between precisely tuned morphologies and the environment.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Behavior finding: Morphogenetic Designs Shaped by Function

Evolution has shaped an incredible diversity of multicellular living organisms, whose complex forms are self-made through a robust developmental process. This fundamental combination of biological evolution and development has served as an inspiration for novel engineering design methodologies, with the goal to overcome the scalability problems suffered by classical top-down approaches. Top-down methodologies are based on the manual decomposition of the design into modular, independent subunits. In contrast, recent computational morphogenetic techniques have shown that they were able to automatically generate truly complex innovative designs. Algorithms based on evolutionary computation and artificial development have been proposed to automatically design both the structures, within certain constraints, and the controllers that optimize their function. However, the driving force of biological evolution does not resemble an enumeration of design requirements, but much rather relies on the interaction of organisms within the environment. Similarly, controllers do not evolve nor develop separately, but are woven into the organism’s morphology. In this chapter, we discuss evolutionary morphogenetic algorithms inspired by these important aspects of biological evolution. The proposed methodologies could contribute to the automation of processes that design “organic” structures, whose morphologies and controllers are intended to solve a functional problem. The performance of the algorithms is tested on a class of optimization problems that we call behavior-finding. These challenges are not explicitly based on morphology or controller constraints, but only on the solving abilities and efficacy of the design. Our results show that morphogenetic algorithms are well suited to behavior-finding