Gene Regulatory Network Evolution Through Augmenting Topologies

International audienceArtificial gene regulatory networks (GRNs) are biologically inspired dynamical systems used to control various kinds of agents, from the cells in developmental models to embodied robot swarms. Most recent work uses a genetic algorithm (GA) or an evolution strategy in order to optimize the network for a specific task. However, the empirical performances of these algorithms are unsatisfactory. This paper presents an algorithm that primarily exploits a network distance metric, which allows genetic similarity to be used for speciation and variation of GRNs. This algorithm, inspired by the successful neuroevolution of augmenting topologies algorithm's use in evolving neural networks and compositional pattern-producing networks, is based on a specific initialization method, a crossover operator based on gene alignment, and speciation based upon GRN structures. We demonstrate the effectiveness of this new algorithm by comparing our approach both to a standard GA and to evolutionary programming on four different experiments from three distinct problem domains, where the proposed algorithm excels on all experiments

Traveling Salesman Problem

The idea behind TSP was conceived by Austrian mathematician Karl Menger in mid 1930s who invited the research community to consider a problem from the everyday life from a mathematical point of view. A traveling salesman has to visit exactly once each one of a list of m cities and then return to the home city. He knows the cost of traveling from any city i to any other city j. Thus, which is the tour of least possible cost the salesman can take? In this book the problem of finding algorithmic technique leading to good/optimal solutions for TSP (or for some other strictly related problems) is considered. TSP is a very attractive problem for the research community because it arises as a natural subproblem in many applications concerning the every day life. Indeed, each application, in which an optimal ordering of a number of items has to be chosen in a way that the total cost of a solution is determined by adding up the costs arising from two successively items, can be modelled as a TSP instance. Thus, studying TSP can never be considered as an abstract research with no real importance

Intelligent Control Architecture For Motion Learning in Robotics Applications

Abstract: The investigation of this Thesis was focused on how motion abilities can be learned by a robot. The main goal was to design and test a control architecture capable of learning how to properly move different simulated robots, through the use of Arti�cial Intelligence (AI) methods. With this purpose, a simulation environment and a set of simulated robots were created in order to test the control architecture. The robots were constructed with a simple geometry using links and joints. A fuzzy controller was designed to control the motors position. The control architecture design was based on subsumption and some AI methods that allowed the simulated robot to find and learn a set of motions based on targets. These methods were a genetic algorithm (GA) and a set of artificial neural networks (ANN). The GA was used to find the adequate robot movements for an specific target, while the ANNs were used to learn and perform such movements eficiently. The advantage of this approach was that, no knowledge of the environment or robot model is needed. The robot learns how to move its own body in order to achieve a determined task. In addition, the learned motions can be used to achieve complex movement execution in a further research. A set of experiments were performed in the simulator in order to show the performance of the control architecture in every one of its stages. The results showed that the proposed architecture was able to learn and perform basic movements of a robot independently of the environment or the robot defined structure.En esta Tesis, se investiga cómo las habilidades de movimiento en un robot, pueden ser aprendidas de forma automática. El objetivo principal fue dise~nar y probar una arquitectura de control capaz de aprender a mover adecuadamente diferentes robots simulados, mediante el uso de métodos de Inteligencia Artificial (IA). Con este propósito, se dise~no un entorno de simulación y un conjunto de robots simulados con el fin de probar la arquitectura de control. Los robots fueron construidos con una geometría muy simple utilizando enlaces y uniones (actuadores), y un controlador difuso fue dise~nado para controlar la posición de los actuadores. El dise~no de la arquitectura de control se basa en el concepto de subsunción (subsumption) y algunos métodos de IA que permiten al robot simulado determinar y aprender una serie de movimientos basados en objetivos. Los métodos usados son un algoritmo genético (GA) y un conjunto de redes neuronales artificiales (ANN). El GA se utiliza para encontrar los movimientos adecuados que el robot debe realizar para alcanzar un objetico específico, mientras que las redes neuronales se utilizan para aprender y realizar estos movimientos de forma eficiente. La ventaja de este enfoque es que, no es necesario conocer el entorno o tener un modelo del robot, sino que el robot aprende cómo mover su propio cuerpo en un ambiente definido con el fin de lograr una tarea determinada. Además, en una posterior investigación, es posible utilizar los movimientos aprendidos para realizar movimientos o tareas más complejas con los robots. Un conjunto de experimentos se llevaron a cabo en el simulador para mostrar el desempe~no de la arquitectura de control en cada una de sus etapas. Los resultados muestran que la arquitectura propuesta es capaz de aprender y realizar los movimientos del robot independientemente del medio ambiente o la estructura definida del robot.Maestrí