Controlling individuals growth in semantic genetic programming through elitist replacement

Castelli, M., Vanneschi, L., & Popovič, A. (2016). Controlling individuals growth in semantic genetic programming through elitist replacement. Computational Intelligence And Neuroscience, 2016, [8326760]. https://doi.org/10.1155/2016/8326760In 2012, Moraglio and coauthors introduced new genetic operators for Genetic Programming, called geometric semantic genetic operators. They have the very interesting advantage of inducing a unimodal error surface for any supervised learning problem. At the same time, they have the important drawback of generating very large data models that are usually very hard to understand and interpret. The objective of this work is to alleviate this drawback, still maintaining the advantage. More in particular, we propose an elitist version of geometric semantic operators, in which offspring are accepted in the new population only if they have better fitness than their parents. We present experimental evidence, on five complex real-life test problems, that this simple idea allows us to obtain results of a comparable quality (in terms of fitness), but with much smaller data models, compared to the standard geometric semantic operators. In the final part of the paper, we also explain the reason why we consider this a significant improvement, showing that the proposed elitist operators generate manageable models, while the models generated by the standard operators are so large in size that they can be considered unmanageable.publishersversionpublishe

Neuroevolution in Deep Neural Networks: Current Trends and Future Challenges

A variety of methods have been applied to the architectural configuration and learning or training of artificial deep neural networks (DNN). These methods play a crucial role in the success or failure of the DNN for most problems and applications. Evolutionary Algorithms (EAs) are gaining momentum as a computationally feasible method for the automated optimisation and training of DNNs. Neuroevolution is a term which describes these processes of automated configuration and training of DNNs using EAs. While many works exist in the literature, no comprehensive surveys currently exist focusing exclusively on the strengths and limitations of using neuroevolution approaches in DNNs. Prolonged absence of such surveys can lead to a disjointed and fragmented field preventing DNNs researchers potentially adopting neuroevolutionary methods in their own research, resulting in lost opportunities for improving performance and wider application within real-world deep learning problems. This paper presents a comprehensive survey, discussion and evaluation of the state-of-the-art works on using EAs for architectural configuration and training of DNNs. Based on this survey, the paper highlights the most pertinent current issues and challenges in neuroevolution and identifies multiple promising future research directions.Comment: 20 pages (double column), 2 figures, 3 tables, 157 reference

Artificial evolution with Binary Decision Diagrams: a study in evolvability in neutral spaces

This thesis develops a new approach to evolving Binary Decision Diagrams, and uses it to study evolvability issues. For reasons that are not yet fully understood, current approaches to artificial evolution fail to exhibit the evolvability so readily exhibited in nature. To be able to apply evolvability to artificial evolution the field must first understand and characterise it; this will then lead to systems which are much more capable than they are currently. An experimental approach is taken. Carefully crafted, controlled experiments elucidate the mechanisms and properties that facilitate evolvability, focusing on the roles and interplay between neutrality, modularity, gradualism, robustness and diversity. Evolvability is found to emerge under gradual evolution as a biased distribution of functionality within the genotype-phenotype map, which serves to direct phenotypic variation. Neutrality facilitates fitness-conserving exploration, completely alleviating local optima. Population diversity, in conjunction with neutrality, is shown to facilitate the evolution of evolvability. The search is robust, scalable, and insensitive to the absence of initial diversity. The thesis concludes that gradual evolution in a search space that is free of local optima by way of neutrality can be a viable alternative to problematic evolution on multi-modal landscapes

Nonlinear Dynamic System Identification and Model Predictive Control Using Genetic Programming

During the last century, a lot of developments have been made in research of complex nonlinear process control. As a powerful control methodology, model predictive control (MPC) has been extensively applied to chemical industrial applications. Core to MPC is a predictive model of the dynamics of the system being controlled. Most practical systems exhibit complex nonlinear dynamics, which imposes big challenges in system modelling. Being able to automatically evolve both model structure and numeric parameters, Genetic Programming (GP) shows great potential in identifying nonlinear dynamic systems. This thesis is devoted to GP based system identification and model-based control of nonlinear systems. To improve the generalization ability of GP models, a series of experiments that use semantic-based local search within a multiobjective GP framework are reported. The influence of various ways of selecting target subtrees for local search as well as different methods for performing that search were investigated; a comparison with the Random Desired Operator (RDO) of Pawlak et al. was made by statistical hypothesis testing. Compared with the corresponding baseline GP algorithms, models produced by a standard steady state or generational GP followed by a carefully-designed single-objective GP implementing semantic-based local search are statistically more accurate and with smaller (or equal) tree size, compared with the RDO-based GP algorithms. Considering the practical application, how to correctly and efficiently apply an evolved GP model to other larger systems is a critical research concern. Currently, the replication of GP models is normally done by repeating other’s work given the necessary algorithm parameters. However, due to the empirical and stochastic nature of GP, it is difficult to completely reproduce research findings. An XML-based standard file format, named Genetic Programming Markup Language (GPML), is proposed for the interchange of GP trees. A formal definition of this standard and details of implementation are described. GPML provides convenience and modularity for further applications based on GP models. The large-scale adoption of MPC in buildings is not economically viable due to the time and cost involved in designing and adjusting predictive models by expert control engineers. A GP-based control framework is proposed for automatically evolving dynamic nonlinear models for the MPC of buildings. An open-loop system identification was conducted using the data generated by a building simulator, and the obtained GP model was then employed to construct the predictive model for the MPC. The experimental result shows GP is able to produce models that allow the MPC of building to achieve the desired temperature band in a single zone space

Bioinformatics Applications Based On Machine Learning

The great advances in information technology (IT) have implications for many sectors, such as bioinformatics, and has considerably increased their possibilities. This book presents a collection of 11 original research papers, all of them related to the application of IT-related techniques within the bioinformatics sector: from new applications created from the adaptation and application of existing techniques to the creation of new methodologies to solve existing problems

Systolic genetic search, a parallel metaheuristic for GPUs

La utilización de unidades de procesamiento gráfico (GPUs) para la resolución de problemas de propósito general ha experimentado un crecimiento vertiginoso en los últimos años, sustentado en su amplia disponibilidad, su bajo costo económico y en contar con una arquitectura inherentemente paralela, así como en la aparición de lenguajes de programación de propósito general que han facilitado el desarrollo de aplicaciones en estas plataformas. En este contexto, el diseño de nuevos algoritmos paralelos que puedan beneficiarse del uso de GPUs es una línea de investigación prometedora e interesante. Las metaheurísticas son algoritmos estocásticos capaces de encontrar soluciones muy precisas (muchas veces óptimas) a problemas de optimización en un tiempo razonable. Sin embargo, como muchos problemas de optimización involucran tareas que exigen grandes recursos computacionales y/o el tamaño de las instancias que se están abordando actualmente se están volviendo muy grandes, incluso las metaheurísticas pueden ser computacionalmente muy costosas. En este escenario, el paralelismo surge como una alternativa exitosa con el fin de acelerar la búsqueda de este tipo de algoritmos. Además de permitir reducir el tiempo de ejecución de los algoritmos, las metaheurísticas paralelas a menudo son capaces de mejorar la calidad de los resultados obtenidos por los algoritmos secuenciales tradicionales.Si bien el uso de GPUs ha representado un dominio inspirador también para la investigación en metaheurísticas paralelas, la mayoría de los trabajos previos tenían como objetivo portar una familia existente de algoritmos a este nuevo tipo de hardware. Como consecuencia, muchas publicaciones están dirigidas a mostrar el ahorro en tiempo de ejecución que se puede lograr al ejecutar los diferentes tipos paralelos de metaheurísticas existentes en GPU. En otras palabras, a pesar de que existe un volumen considerable de trabajo sobre este tópico, se han propuesto pocas ideas novedosas que busquen diseñar nuevos algoritmos y/o modelos de paralelismo que exploten explícitamente el alto grado de paralelismo disponible en las arquitecturas de las GPUs. Esta tesis aborda el diseño de una propuesta innovadora de algoritmo de optimización paralelo denominada Búsqueda Genética Sistólica (SGS), que combina ideas de los campos de metaheurísticas y computación sistólica. SGS, así como la computación sistólica, se inspiran en el mismo fenómeno biológico: la contracción sistólica del corazón que hace posible la circulación de la sangre. En SGS, las soluciones circulan de forma síncrona a través de una grilla (rejilla) de celdas. Cuando dos soluciones se encuentran en una celda se aplican operadores evolutivos adaptados para generar nuevas soluciones que continúan moviéndose a través de la grilla (rejilla). La implementación de esta nueva propuesta saca partido especialmente de las características específicas de las GPUs. Un extenso análisis experimental que considera varios problemas de benchmark clásicos y dos problemas del mundo real del área de Ingeniería de Software, muestra que el nuevo algoritmo propuesto es muy efectivo, encontrando soluciones óptimas o casi óptimas en tiempos de ejecución cortos. Además, los resultados numéricos obtenidos por SGS son competitivos con los resultados del estado del arte para los dos problemas del mundo real en cuestión. Por otro lado, la implementación paralela en GPU de SGS ha logrado un alto rendimiento, obteniendo grandes reducciones de tiempo de ejecución con respecto a la implementación secuencial y mostrando que escala adecuadamente cuando se consideran instancias de tamaño creciente. También se ha realizado un análisis teórico de las capacidades de búsqueda de SGS para comprender cómo algunos aspectos del diseño del algoritmo afectan a sus resultados numéricos. Este análisis arroja luz sobre algunos aspectos del funcionamiento de SGS que pueden utilizarse para mejorar el diseño del algoritmo en futuras variantes

Engineering Education and Research Using MATLAB

MATLAB is a software package used primarily in the field of engineering for signal processing, numerical data analysis, modeling, programming, simulation, and computer graphic visualization. In the last few years, it has become widely accepted as an efficient tool, and, therefore, its use has significantly increased in scientific communities and academic institutions. This book consists of 20 chapters presenting research works using MATLAB tools. Chapters include techniques for programming and developing Graphical User Interfaces (GUIs), dynamic systems, electric machines, signal and image processing, power electronics, mixed signal circuits, genetic programming, digital watermarking, control systems, time-series regression modeling, and artificial neural networks

Advances in Evolutionary Algorithms

With the recent trends towards massive data sets and significant computational power, combined with evolutionary algorithmic advances evolutionary computation is becoming much more relevant to practice. Aim of the book is to present recent improvements, innovative ideas and concepts in a part of a huge EA field