Francisco Fernández de Vega

Experimental Study of Multipopulation Parallel Genetic Programming

The parallel execution of several populations in evolutionary algorithms has usually given good results. Nevertheless, researchers have to date drawn conflicting conclusions when using some of the parallel genetic programming models. One aspect of the conflict is population size, since published GP works do not agree about whether to use large or small populations. This paper presents an experimental study of a number of common GP test problems. Via our experiments, we discovered that an optimal range of values exists. This assists us in our choice of population size and in the selection of an appropriate parallel genetic programming model. Finding efficient parameters helps us to speed up our search for solutions. At the same time, it allows us to locate features that are common to parallel genetic programming and the classic genetic programming technique.

Evolution of artificial terrains for video games based on accessibility

Diverse methods have been developed to generate terrains under constraints to control terrain features, but most of them use strict restrictions. However, there are situations were more flexible restrictions are sufficient, such as ensuring that terrains have enough accessible area, which is an important trait for video games. The Genetic Terrain Program technique, based on genetic programming, was used to automatically evolve Terrain Programs (TPs - which are able to generate terrains procedurally) for the desired accessibility parameters. Results showed that the accessibility parameters have negligible influence on the evolutionary system and that the terminal set has a major role on the terrain look. TPs produced this way are already being used on Chapas video game.

Breeding terrains with genetic terrain programming: the evolution of terrain generators

Although a number of terrain generation techniques have been proposed during the last few years, all of them have some key constraints. Modelling techniques depend highly upon designers skills, time, and effort to obtain acceptable results, and cannot be used to automatically generate terrains. The simpler methods allow only a narrow variety of terrain types and offer little control on the outcome terrain. The Genetic Terrain Programming technique, based on evolutionary design with Genetic Programming, allows designers to evolve terrains according to their aesthetic feelings or desired features. This technique evolves Terrain Programmes (TPs) that are capable of generating a family of terrains—different terrains that consistently present the same morphological characteristics. This paper presents a study about the persistence of morphological characteristics of terrains generated with different resolutions by a given TP. Results show that it is possible to use low resolutions during the evolutionary phase without compromising the outcome, and that terrain macrofeatures are scale invariant.

Bloat control operators and diversity in genetic programming: A comparative study

This paper reports a comparison of several bloat control methods and also evaluates a recent proposal for limiting the size of the individuals: a genetic operator called prune and plant. The aim of this work is to test the adequacy of this method. Since a preliminary study of the method has already shown promising results, we have performed a thorough study in a set of benchmark problems aiming at demonstrating the utility of the new approach. Prune and plant has obtained results that maintain the quality of the final solutions in terms of fitness while achieving a substantial reduction of the mean tree size in all four problem domains considered. In addition, in one of these problem domains, prune and plant has demonstrated to be better in terms of fitness, size reduction, and time consumption than any of the other bloat control techniques under comparison. The experimental part of the study presents a comparison of performance in terms of phenotypic and genotypic diversity. This comparison study can provide the practitioner with some relevant clues as to which bloat control method is better suited to a particular problem and whether the advantage of a method does or does not derive from its influence on the genetic pool diversity.

Is the island model fault tolerant

In this paper, we present a study on the fault tolerance nature of the island model when applied to Genetic Algorithms. Parallel and distributed models have been extensively applied to GAs when researchers tackle hard problems. The idea is both to reduce computing time while also improving diversity of populations and therefore quality of solutions. Nevertheless, there are few works dealing with the problem of faults that are usually present when a distributed infrastructure is employed for running the parallel algorithm. This paper studies the behavior of the Island Model when faults appear on a parallel computer or a network of computers. Two benchmark problems have been employed, and good results obtained for each of them allow us to reliably consider Island Model as a fault tolerant parallel algorithm.

Test Case Evaluation and Input Domain Reduction strategies for the Evolutionary Testing of Object-Oriented software

In Evolutionary Testing, meta-heuristic search techniques are used for generating test data. The focus of our research is on employing evolutionary algorithms for the structural unit-testing of Object-Oriented programs. Relevant contributions include the introduction of novel methodologies for automation, search guidance and Input Domain Reduction; the strategies proposed were empirically evaluated with encouraging results. Test cases are evolved using the Strongly-Typed Genetic Programming technique. Test data quality evaluation includes instrumenting the test object, executing it with the generated test cases, and tracing the structures traversed in order to derive coverage metrics. The methodology for efficiently guiding the search process towards achieving full structural coverage involves favouring test cases that exercise problematic structures. Purity Analysis is employed as a systematic strategy for reducing the search space.

Speciation in Behavioral Space for Evolutionary Robotics

In Evolutionary Robotics, population-based evolutionary computation is used to design robot neurocontrollers that produce behaviors which allow the robot to fulfill a user-defined task. However, the standard approach is to use canonical evolutionary algorithms, where the search tends to make the evolving population converge towards a single behavioral solution, even if the high-level task could be accomplished by structurally different behaviors. In this work, we present an approach that preserves behavioral diversity within the population in order to produce a diverse set of structurally different behaviors that the robot can use. In order to achieve this, we employ the concept of speciation, where the population is dynamically subdivided into sub-groups, or species, each one characterized by a particular behavioral structure that all individuals within that species share. Speciation is achieved by describing each neurocontroller using a representations that we call a behavior signature, these are descriptors that characterize the traversed path of the robot within the environment. Behavior signatures are coded using character strings, this allows us to compare them using a string similarity measure, and three measures are tested. The proposed behavior-based speciation is compared with canonical evolution and a method that speciates based on network topology. Experimental tests were carried out using two robot tasks (navigation and homing behavior), several training environments, and two different robots (Khepera and Pioneer), both real and simulated. Results indicate that behavior-based speciation increases the diversity of the behaviors based on their structure, without sacrificing performance. Moreover, the evolved controllers exhibit good robustness when the robot is placed within environments that were not used during training. In conclusion, the speciation method presented in this work allows an evolutionary algorithm to produce several robot behaviors that are structurally different but all are able to solve the same robot task.

Discovering several robot behaviors through speciation

This contribution studies speciation from the standpoint of evolutionary robotics (ER). A common approach to ER is to design a robots control system using neuro-evolution during training. An extension to this methodology is presented here, where speciation is incorporated to the evolution process in order to obtain a varied set of solutions for a robotics problem using a single algorithmic run. Although speciation is common in evolutionary computation, it has been less explored in behavior-based robotics. When employed, speciation usually relies on a distance measure that allows different individuals to be compared. The distance measure is normally computed in objective or phenotypic space. However, the speciation process presented here is intended to produce several distinct robot behaviors; hence, speciation is sought in behavioral space. Thence, individual neurocontrollers are described using behavior signatures, which represent the traversed path of the robot within the training environment and are encoded using a character string. With this representation, behavior signatures are compared using the normalized Levenshtein distance metric (N-GLD). Results indicate that speciation in behavioral space does indeed allow the ER system to obtain several navigation strategies for a common experimental setup. This is illustrated by comparing the best individual from each species with those obtained using the Neuro-Evolution of Augmenting Topologies (NEAT) method which speciates neural networks in topological space.

EvoSpace-Interactive: a framework to develop distributed collaborative-interactive evolutionary algorithms for artistic design

Currently, a large number of computing systems and user applications are focused on distributed and collaborative models for heterogeneous devices, exploiting cloud-based approaches and social networking. However, such systems have not been fully exploited by the evolutionary computation community. This work is an attempt to bridge this gap, and integrate interactive evolutionary computation with a distributed cloud-based approach that integrates with social networking for collaborative design of artistic artifacts. Such an approach to evolutionary art could fully leverage the concept of memes as an idea that spreads from person to person, within a computational system. In particular, this work presents EvoSpace-Interactive, an open source framework for the development of collaborative-interactive evolutionary algorithms, a computational tool that facilitates the development of interactive algorithms for artistic design. A proof of concept application is developed on EvoSpace-Interactive called Shapes that incorporates the popular social network Facebook for the collaborative evolution of artistic images generated using the Processing programming language. Initial results are encouraging, Shapes illustrates that it is possible to use EvoSpace-Interactive to effectively develop and deploy a collaborative system.

Modelling video games' landscapes by means of genetic terrain programming: a new approach for improving users' experience

Terrain generation algorithms can provide a realistic scenario for video game experience and can help keep users interested in playing by providing new landscapes each time they play. Nowadays there are a wide range of techniques for terrain generation, but all of them are focused on providing realistic terrains. This paper proposes a new technique, Genetic Terrain Programming, based on evolutionary design with GP to allow game designers to evolve terrains according to their aesthetic feelings or desired features. The developed application produces Terrains Programs that will always generate different terrains, but consistently with the same features (e.g. valleys, lakes).