José Antonio Becerra

UniDA: Uniform Device Access Framework for Human Interaction Environments

Human interaction environments (HIE) must be understood as any place where people carry out their daily life, including their work, family life, leisure and social life, interacting with technology to enhance or facilitate the experience. The integration of technology in these environments has been achieved in a disorderly and incompatible way, with devices operating in isolated islands with artificial edges delimited by the manufacturers. In this paper we are presenting the UniDA framework, an integral solution for the development of systems that require the integration and interoperation of devices and technologies in HIEs. It provides developers and installers with a uniform conceptual framework capable of modelling an HIE, together with a set of libraries, tools and devices to build distributed instrumentation networks with support for transparent integration of other technologies. A series of use case examples and a comparison to many of the existing technologies in the field has been included in order to show the benefits of using UniDA.

Real-valued multimodal fitness landscape characterization for evolution

This paper deals with the characterization of the fitness landscape of multimodal functions and how it can be used to choose the most appropriate evolutionary algorithm for a given problem. An algorithm that obtains a general description of real valued multimodal fitness landscapes in terms of the relative number of optima, their sparseness, the size of their attraction basins and the evolution of this size when moving away from the global optimum is presented and used to characterize a set of well-known multimodal benchmark functions. To illustrate the relevance of the information obtained and its relationship to the performance of evolutionary algorithms over different fitness landscapes, two evolutionary algorithms, Differential Evolution and Covariance Matrix Adaptation, are compared over the same benchmark set showing their behavior depending on the multimodal features of each landscape.

Considerations in the application of evolution to the generation of robot controllers

Abstract This paper is concerned with different aspects of the use of evolution for the successful generation of real robot Artificial Neural Network (ANN) controllers. Several parameters of an evolutionary/genetic algorithm (GA) and the way they influence the evolution of ANN behavioral controllers for real robots have been contemplated. These parameters include the way the initial populations are distributed, how the individuals are evaluated, the implementation of race schemes, etc. A batch of experiments on the evolution of three types of behaviors with different population sizes have been carried out in order to ascertain their effect on the evolution of the controllers and their validity in real implementations. The results provide a guide to the design of evolutionary algorithms for generating ANN based robot controllers, especially when, due to computational constraints, the populations to be used are small with respect to the complexity of the problem to be solved. The problem of transferring the controllers evolved in simulated environments to the real systems operating in real environments are also considered and we present results of this transference to reality with a robot which has few and extremely noisy sensors.

JEAF: A Java Evolutionary Algorithm Framework

There are not many tools in the evolutionary computing field that allow researchers to implement, modify or compare different algorithms. Additionally, those tools usually lack flexibility, maintenance or some other characteristic, so researchers program their own solutions most of the time, reimplementing algorithms that have already been implemented hundreds of times. This paper introduces a new framework for evolutionary computation called JEAF (Java Evolutionary Algorithm Framework) that tries to offer a platform to facilitate the tasks of comparing, analyzing, modifying and implementing evolutionary algorithms, reusing components and programming as few as possible. JEAF also aims to be a tool for evolutionary algorithm users that employ these algorithms to solve other problems not related with evolutionary computation. In this sense, JEAF provides methods to distribute an evolutionary process and to plug external tools to perform the evaluation of candidate solutions.

Using promoters and functional introns in genetic algorithms for neuroevolutionary learning in non-stationary problems

This paper addresses the problem of adaptive learning in non-stationary problems through neuroevolution. It is a general problem that is very relevant in many tasks, for example, in the context of robot model learning from interaction with the world. Traditional learning algorithms fail in this task as they have mostly been designed for learning a single model in a static setting. Neuroevolutionary techniques have obtained promising results in this non-stationary context but are still lacking in certain types of problems, especially those dealing with information streams where different portions correspond to different models. An extension through the introduction of the concept of introns and promoter genes enables neuroevolutionary algorithms to improve their performance on this type of problems. Following this approach, an implementation of these concepts on a genetic algorithm for neuroevolution is presented here. This algorithm is called promoter based genetic algorithm (PBGA) and it uses a genotypic representation with a set of features that allows for an intrinsic memory in the population that is self-regulated, in the sense that functional parts of the individuals are preserved through generations without an explicit knowledge about the number of different tasks or models that have to arise from the data stream. Some illustrative tests of the potential of these techniques based on the continuous switch between completely different objective functions that must be learnt are presented and the results are analyzed and compared to other neuroevolutionary algorithms.

Internal and External Memory in Neuroevolution for Learning in Non-stationary Problems

This paper deals with the topic of learning through neuroevolutionary algorithms in non-stationary settings. This kind of algorithms that evolve the parameters and/or the topology of a population of Artificial Neural Networks have provided successful results in optimization problems in stationary settings. Their application to non-stationary problems, that is, problems that involve changes in the objective function, still requires more research. In this paper we address the problem through the integration of implicit, internal or genotypic, memory structures and external explicit memories in an algorithm called Promoter Based Genetic Algorithm with External Memory (PBGA-EM). The capabilities introduced in a simple genetic algorithm by these two elements are shown on different tests where the objective function of a problem is changed in an unpredictable manner.

Applying synaptic delays for virtual sensing and actuation in mobile robots

In this article we describe the use of Artificial Neural Networks (ANN) with synaptic time delays between the nodes as a means to increase the capabilities of the usual control modules used in behavior based robotics. This inclusion allows the controllers to manage explicit temporal information in different levels. In the sensing level it permits the use of virtual sensors that improve the precision of the information provided by sensors through a temporal correlation of their values. In the actuation level we use the network with an infrasensorized robot in a problem that requires active sensing, where the control and actuation mechanisms are coordinated in order to obtain a better sensorial image of the environment by means of a spatio-temporal representation of a perception sequence. The decision of the appropriate delays is automated through learning and evolution.

Progressive Construction of Compound Behavior Controllers for Autonomous Robots Using Temporal Information

In this work we present a methodology for the progressive construction of compound behavior controllers for real autonomous robots. Some of these behaviors require temporal processing which is achieved through the inclusion of temporal delays in the synapses of the artificial neural networks used for their implementation. Starting from a set of simple behaviors implemented by means of evolved monolithic controllers, the evolution strategy employed obtains behaviors in higher levels either choosing the necessary low level behaviors from the previously selected set or through the coevolution of part of the low level behaviors and the higher level one. Emphasis is placed on making the behaviors robust and capable of performing in a real robot.

Application domain study of evolutionary algorithms in optimization problems

This paper deals with the problem of comparing and testing evolutionary algorithms, that is, the benchmarking problem, from an analysis point of view. A practical study of the application domain of four representative evolutionary algorithms is carried out using a relevant set of real-parameter function optimization benchmarks. The four selected algorithms are the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) and the Differential Evolution (DE), due to their successful results in recent studies, a Genetic Algorithm with real parameter operators, used here as a reference approach because it is probably the most familiar to researchers, and the Macroevolutionary algorithm (MA), which is not widely known but it shows a very remarkable behavior in some problems. The algorithms have been compared running several tests over the benchmark function set to analyze their capabilities from a practical point of view, in other words, in terms of their usability. The characterization of the algorithms is based on accuracy, stability and time consumption parameters thus establishing their operational scope and the type of optimization problems they are more suitable for.

Incremental Evolution of Stigmergy-Based Multi Robot Controllers Through Utility Functions

This paper deals with the problem of jointly designing the behaviors of a group of robots so as to produce a particular desired collaborative behavior in stigmergy-based settings. In this line, the approach followed has its roots on traditional evolutionary behavior based robotics techniques, but instead of trying to evolve the whole controller for a particular complex behavior in one step, an incremental approach is used by combining the results from different evolutionary/learning processes in different settings for the construction of a complete controller architecture. The key aspects when trying to generalize the process of obtaining the controllers from single robots to multi robot systems are discussed in the framework of a set of simple collaborative tasks, focusing the discussion on the utility functions that guide the evolution.