La protesta campesina como protesta ambiental, siglos XVIII-XX

This article analyses peasant conflicts from an environmental perspective. First, we show a general theory about environmental conflicts making a clear differentiation among environmental conflicts making a clear differentiation among environmental, environmentalist and green movement conflicts. This differentiation is accomplished using as criterion the goals of the actors in relation to both the agroecosystems sustainability and the types of discourses used in the protests. Second, we analyse the peasant use of agroecosystems and the differences between agricultural systems based on organic energy in contrast with those based on fossil energy. Finally, we identify several types of peasant environmental conflicts between 18th and 20th centuries: environmental peasant protests in Mediterranean Europe, Latin America, Asia and Africa; conflicts produced around resources like water, common goods or against pollution, Land Reform, reactions to environmental policies or defence of indigenous territories. As these examples show, attention must be paid to the environmental dimension of the peasant protest in order to understand the conflicts, although sometimes mixed with gender and class dimensions

An enhanced classifier system for autonomous robot navigation in dynamic environments

In many cases, a real robot application requires the navigation in dynamic environments. The navigation problem involves two main tasks: to avoid obstacles and to reach a goal. Generally, this problem could be faced considering reactions and sequences of actions. For solving the navigation problem a complete controller, including actions and reactions, is needed. Machine learning techniques has been applied to learn these controllers. Classifier Systems (CS) have proven their ability of continuos learning in these domains. However, CS have some problems in reactive systems. In this paper, a modified CS is proposed to overcome these problems. Two special mechanisms are included in the developed CS to allow the learning of both reactions and sequences of actions. The learning process has been divided in two main tasks: first, the discrimination between a predefined set of rules and second, the discovery of new rules to obtain a successful operation in dynamic environments. Different experiments have been carried out using a mini-robot Khepera to find a generalised solution. The results show the ability of the system to continuous learning and adaptation to new situations.Publicad

Uniform coevolution for solving the density classification problem in cellular automata

Genetic and Evolutionary Computation Conference (GECCO 2000). Las Vegas, Nevada (USA), July 8-12 2000.Uniform Coevolution is based on competitive evolution ideas where the solution and example sets are evolving by means of a competition to generate difficult test beds for the solutions in a gradual way. The method has been tested with the density parity problem in cellular automata, where the selected examples can biased the solutions founded. The results show a high value of generality using Uniform coevolution, compared with no Co-evolutive approaches.Publicad

Hierarchical genetic algorithms for composite laminate panels stress optimisation

IEEE International Conference on Systems, Man, and Cybernetics. Tokyo, 12-15 October 1999.Genetic algorithms (GAs) have demonstrated to be a powerful technique for solving optimisation problems. In this article, the problem of optimising the number of plies and their stacking sequence in the design of laminated composite panels is considered. This problem has special features that makes it different from traditional problems in which GAs have been applied, which make the problem a multiobjective optimisation one. Symmetry and equilibrium constraints have also been included in the solution. A modification of the canonical GA is needed and a new perspective for solving this problem by using GA techniques is introduced

Hydroelectric power plant management relying on neural networks and expert system integration

The use of Neural Networks (NN) is a novel approach that can help in taking decisions when integrated in a more general system, in particular with expert systems. In this paper, an architecture for the management of hydroelectric power plants is introduced. This relies on monitoring a large number of signals, representing the technical parameters of the real plant. The general architecture is composed of an Expert System and two NN modules: Acoustic Prediction (NNAP) and Predictive Maintenance (NNPM). The NNAP is based on Kohonen Learning Vector Quantization (LVQ) Networks in order to distinguish the sounds emitted by electricity-generating machine groups. The NNPM uses an ART-MAP to identify different situations from the plant state variables, in order to prevent future malfunctions. In addition, a special process to generate a complete training set has been designed for the ART-MAP module. This process has been developed to deal with the absence of data about abnormal plant situations, and is based on neural nets trained with the backpropagation algorithm.Publicad

Neural network controller against environment: A coevolutive approach to generalize robot navigation behavior

In this paper, a new coevolutive method, called Uniform Coevolution, is introduced to learn weights of a neural network controller in autonomous robots. An evolutionary strategy is used to learn high-performance reactive behavior for navigation and collisions avoidance. The introduction of coevolutive over evolutionary strategies allows evolving the environment, to learn a general behavior able to solve the problem in different environments. Using a traditional evolutionary strategy method, without coevolution, the learning process obtains a specialized behavior. All the behaviors obtained, with/without coevolution have been tested in a set of environments and the capability of generalization is shown for each learned behavior. A simulator based on a mini-robot Khepera has been used to learn each behavior. The results show that Uniform Coevolution obtains better generalized solutions to examples-based problems.Publicad

A computational model of evolution: haploidy versus diploidy

In this paper, the study of diploidy is introduced like and important mechanism for memory reinforcement in artificial environments where adaptation is very important. The individuals of this ecosystem are able to genetically "learn" the best behaviour for survival. Critical changes, happening in the environmental conditions, require the presence of diploidy to ensure the survival of species. By means of new gene-dominance configurations, a way to shield the individuals from erroneous selection is provided. These two concepts appear like important elements for artificial systems which have to evolve in environments with some degree of instability.Publicad

Neural networks robot controller trained with evolution strategies

Congress on Evolutionary Computation. Washington, DC, 6-9 July 1999.Neural networks (NN) can be used as controllers in autonomous robots. The specific features of the navigation problem in robotics make generation of good training sets for the NN difficult. An evolution strategy (ES) is introduced to learn the weights of the NN instead of the learning method of the network. The ES is used to learn high performance reactive behavior for navigation and collision avoidance. No subjective information about “how to accomplish the task” has been included in the fitness function. The learned behaviors are able to solve the problem in different environments; therefore, the learning process has the proven ability to obtain a specialized behavior. All the behaviors obtained have been tested in a set of environments and the capability of generalization is shown for each learned behavior. A simulator based on the mini-robot, Khepera, has been used to learn each behavior

A general learning co-evolution method to generalize autonomous robot navigation behavior

Congress on Evolutionary Computation. La Jolla, CA, 16-19 July 2000.A new coevolutive method, called Uniform Coevolution, is introduced, to learn weights for a neural network controller in autonomous robots. An evolutionary strategy is used to learn high-performance reactive behavior for navigation and collision avoidance. The coevolutive method allows the evolution of the environment, to learn a general behavior able to solve the problem in different environments. Using a traditional evolutionary strategy method without coevolution, the learning process obtains a specialized behavior. All the behaviors obtained, with or without coevolution have been tested in a set of environments and the capability for generalization has been shown for each learned behavior. A simulator based on the mini-robot Khepera has been used to learn each behavior. The results show that Uniform Coevolution obtains better generalized solutions to example-based problems

Distance modulation competitive co-evolution method to find initial configuration independent cellular automata rules

IEEE International Conference on Systems, Man, and Cybernetics. Tokyo, 12-15 October 1999.One of the main problems in machine learning methods based on examples is the over-adaptation. This problem supposes the exact adaptation to the training examples losing the capability of generalization. A solution of these problems arises in using large sets of examples. In most of the problems, to achieve generalized solutions, almost infinity examples sets are needed. This make the method useless in practice. In this paper, one way to overcome this problem is proposed, based on biological competitive evolution ideas. The evolution is produced as a result of a competition between sets of solutions and sets of examples, trying to beat each other. This mechanism allows the generation of generalized solutions using short example sets