This paper proposes the use of the q-Gaussian mutation with self-adaptation of the shape of the mutation distribution in evolutionary algorithms. The shape of the q-Gaussian mutation distribution is controlled by a real parameter q. In the proposed method, the real parameter q of the q-Gaussian mutation is encoded in the chromosome of individuals and hence is allowed to evolve during the evolutionary process. In order to test the new mutation operator, evolution strategy and evolutionary programming algorithms with self-adapted q-Gaussian mutation generated from anisotropic and isotropic distributions are presented. The theoretical analysis of the q-Gaussian mutation is also provided. In the experimental study, the q-Gaussian mutation is compared to Gaussian and Cauchy mutations in the optimization of a set of test functions. Experimental results show the efficiency of the proposed method of self-adapting the mutation distribution in evolutionary algorithms.; FAPESP; CNPq in Brazil; Engineering and Physical Sciences Research Council (EPSRC) of the UK[EP/E060722/1]; Engineering and Physical Sciences Research Council (EPSRC) of the UK[EP/E060722/2]

Evolutionary biologists have long endeavored to document how many species exist on Earth, to understand the processes by which biodiversity waxes and wanes, to document and interpret spatial patterns of biodiversity, and to infer evolutionary relationships. Despite the great potential of this knowledge to improve biodiversity science, conservation, and policy, evolutionary biologists have generally devoted limited attention to these broader implications. Likewise, many workers in biodiversity science have underappreciated the fundamental relevance of evolutionary biology. The aim of this article is to summarize and illustrate some ways in which evolutionary biology is directly relevant We do so in the context of four broad areas: (1) discovering and documenting biodiversity, (2) understanding the causes of diversification, (3) evaluating evolutionary responses to human disturbances, and (4) implications for ecological communities, ecosystems, and humans We also introduce bioGENESIS, a new project within DIVERSITAS launched to explore the potential practical contributions of evolutionary biology In addition to fostering the integration of evolutionary thinking into biodiversity science, bioGENESIS provides practical recommendations to policy makers for incorporating evolutionary perspectives into biodiversity agendas and conservation. We solicit your involvement in developing innovative ways of using evolutionary biology to better comprehend and stem the loss of biodiversity.; Yale University; Yale University; Kyushu University; Kyushu University; EDIT; EDIT; Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP); FAPESP; Universidade de Sao Paulo (USP); Universidade de São Paulo (USP); CNPq; Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq); DIVERSITAS; DIVERSITAS

Este trabalho verifica a possibilidade de se aplicar Algoritmos Evolutivos no aprendizado on-line de jogos. Alguns autores concordam que Algoritmos Evolutivos não são aplicáveis na prática para se atingir o objetivo em questão. É com a intenção de contestar a veracidade desta afirmação que foi desenvolvido o presente trabalho. Para atingir o objetivo proposto, foi desenvolvido um jogo de computador, no qual o algoritmo de aprendizado gera estratégias inteligentes e adaptativas para os caracteres não controlados pelo jogador através de um algoritmo evolutivo. Desta forma, a função do algoritmo evolutivo é fazer com que a estratégia utilizada pelo computador se adapte à estratégia utilizada pelo usuário a cada vez que joga. É apresentada uma revisão bibliográfica a respeito de Computação Evolutiva e as técnicas utilizadas para implementar comportamentos inteligentes para os caracteres controlados por computador nos jogos atuais, esclarecendo suas vantagens, desvantagens e algumas possíveis aplicações. São também explicados o jogo e os algoritmos implementados, assim como os experimentos realizados e seus resultados. Por fim, é feita uma comparação do algoritmo evolutivo final com uma outra técnica de adaptação...

O agrupamento de dados é uma técnica fundamental em aplicações de diversos campos do mercado e da ciência, como, por exemplo, no comércio, na biologia, na psiquiatria, na astronomia e na mineração da Web. Ocorre que em um subconjunto desses campos, como engenharia industrial, ciências sociais, engenharia sísmica e recuperação de documentos, as bases de dados são usualmente descritas apenas pelas proximidades entre os objetos (denominadas bases de dados relacionais). Mesmo em aplicações nas quais os dados não são naturalmente relacionais, o uso de bases relacionais permite que os dados em si sejam mantidos sob sigilo, o que pode ser de grande valia para bancos ou corretoras, por exemplo. Nesta dissertação é apresentada uma revisão de algoritmos de agrupamento de dados que lidam com bases de dados relacionais, com foco em algoritmos que produzem partições rígidas (hard ou crisp) dos dados. Particular ênfase é dada aos algoritmos evolutivos, que têm se mostrado capazes de resolver problemas de agrupamento de dados com relativa acurácia e de forma computacionalmente eficiente. Nesse contexto, propõe-se nesta dissertação um novo algoritmo evolutivo de agrupamento capaz de operar sobre dados relacionais e também capaz de estimar automaticamente o número de grupos nos dados (usualmente desconhecido em aplicações práticas). É demonstrado empiricamente que esse novo algoritmo pode superar métodos tradicionais da literatura em termos de eficiência computacional e acurácia; Data clustering is a fundamental technique for applications in several fields of science and marketing...

Redes Bayesianas (RB) são ferramentas probabilísticas amplamente aceitas para modelar e fazer inferências em domínios sob incertezas. Uma das maiores dificuldades na construção de uma RB é determinar a sua estrutura de modelo, a qual representa a estrutura de interdependências entre as variáveis modeladas. A estimativa exata da estrutura de modelo a partir de dados observados é, de forma geral, impraticável já que o número de estruturas possíveis cresce de forma super-exponencial com o número de variáveis. Métodos eficientes de aprendizagem aproximada tornam-se, portanto, essenciais para a construção de RBs verossímeis. O presente trabalho apresenta o Sistema Evolutivo Eficiente para Aprendizagem Estrutural de RBs, ou abreviadamente, EES-BN. Duas etapas de aprendizagem compõem EES-BN. A primeira etapa é encarregada de reduzir o espaço de busca mediante a aprendizagem de uma superestrutura. Para tal fim foram desenvolvidos dois métodos efetivos: Opt01SS e OptHPC, ambos baseados em testes de independência. A segunda etapa de EES-BN é um esquema de busca evolutiva que aproxima a estrutura do modelo respeitando as restrições estruturais aprendidas na superestrutura. Três blocos principais integram esta etapa: recombinação...

Existe um interesse crescente do mercado por aplicações de multimídia em streaming via rede. Particularmente, as aplicações de live streaming que utilizam a tecnologia de redes P2P para a disseminação de conteúdo têm sido alvo de grande atenção. Aplicações como PPLive e PPStream provam que as aplicações de live streaming em redes P2P são uma realidade com relação à tecnologia atual. Os sistemas de live streaming fornecem um serviço de multicast no nível de aplicação para transmissões ao vivo na Internet. Essas aplicações de live streaming, quando executadas em redes P2P, têm potencial para serem altamente robustas, escaláveis e adaptativas devido à redundância e não dependência de recursos particulares dentre os nodos participantes. Porém, para fazer uso de todas as vantagens disponíveis, a aplicação deve contornar alguns desafios: i) manter a qualidade de playback mesmo com a inerente dinamicidade das redes P2P; ii) impedir que nodos incorretos escondam ações maliciosas atrás do anonimato que existe em P2P; iii) manter a taxa de upload dos nodos participantes da aplicação em um nível aceitável. A taxa de upload dos nodos é muito importante porque a aplicação de live streaming em P2P é uma aplicação cooperativa. Desta forma...

Evolutionary biology is an essential basic science for medicine, but few doctors and medical researchers are familiar with its most relevant principles. Most medical schools have geneticists who understand evolution, but few have even one evolutionary biologist to suggest other possible applications. The canyon between evolutionary biology and medicine is wide. The question is whether they offer each other enough to make bridge building worthwhile. What benefits could be expected if evolution were brought fully to bear on the problems of medicine? How would studying medical problems advance evolutionary research? Do doctors need to learn evolution, or is it valuable mainly for researchers? What practical steps will promote the application of evolutionary biology in the areas of medicine where it offers the most? To address these questions, we review current and potential applications of evolutionary biology to medicine and public health. Some evolutionary technologies, such as population genetics, serial transfer production of live vaccines, and phylogenetic analysis, have been widely applied. Other areas, such as infectious disease and aging research, illustrate the dramatic recent progress made possible by evolutionary insights. In still other areas...

For almost a century, evolutionary economics has been based to a significant extent on analogies derived from biology. At the same time the discipline suffered from lack of analytical rigor. Recently, advances in thermodynamics and information theory have provided a new foundation for evolutionary studies in biology and economics alike. As a result, the body of studies in evolutionary economics that imports concepts from thermodynamics and information theory to develop new analogies is growing. This paper surveys recent trends in evolutionary economics at the crossroads of biology and physics, and argues to supplant analogies derived from either of the two disciplines. Albeit powerful means to crystallize thought about evolutionary processes in economic systems, analogies from biology have tended to plaster over the many differences between biological and economic processes that are essential to economic systems. Similarly, thermodynamics and information theory cannot provide a non-anthropocentric evaluation of economic processes. Yet, the concepts and measures available from physics can be used to improve our understanding of economic evolution if properly placed into the context of socioeconomic processes. The paper delineates the realm for non-analogy based applications of concepts from physics for the assessment of economic processes in light of discontinuities and emergent complexities.; no

This thesis examines verification of system-on-a-chip (SoC) designs using a software applications test methodology that is enhanced by genetic evolutionary test generations and functional coverage. The verification methodology facilitates application based testing using behavioural simulations before the chip is fabricated. The goal of the methodology is to verify commonly used real-life functionalities of the SoC earlier in the design process, so as to uncover design bugs that are considered most critical to actual SoC usages when the SoC is employed in its intended end-product. The verification methodology is based on a test building blocks approach, whereby many different components of various SoC application use-cases are extracted into building blocks, and then recomposed with other components to construct greater variety and range of test cases for verifying the SoC. An important facet of the methodology is to address automated creation of these software application test cases in an effective and efficient manner. The goal is to maximise test coverage and hence bug detection likelihood using minimal verification resources and effort. To this end, test generations techniques employing single and multi objective genetic algorithms and evolutionary strategies are devised in this thesis. Using coverage and test size to drive test generations...

Provides information on the applications of evolutionary computation in computer security and cryptography. Main applications of evolutionary computations in cryptology; Achievements of several researchers in the field of artificial intelligence applications to computer security and cryptology; Examples of successful research.

Archiving procedures are a key parameter for Multi-objective evolutionary algorithms, since they guarantee the algorithm convergence and the good spread of the obtained solutions in the final Pareto front. For many practical applications, the cost of the algorithm is clearly dominated by the computational cost of the underlying fitness functions, allowing complex processes to be incorporated into the archiving procedure. This work presents a study of the archiving technique for evolutionary polygonal approximation (the division of a given curve into a set of n segments represented by a linear model) based on the epsilon-glitch concept, highlighting the cost of the technique compared to the fitness computation, and proposing a novel alternative archiving procedure, which yields statistically significant better results compared to available approaches.; This work was supported in part by Projects MINECO TEC2012-37832-C02-01, CICYT TEC2011-28626-C02-02, CAM CONTEXTS (S2009/TIC-1485); Proceedings of: Fifth International Conference on Future Computational Technologies and Applications (FUTURE COMPUTING 2013), Valencia, Spain, May 27 - June 1, 2013

All aspects of biological diversification ultimately trace to evolutionary modifications at the cellular level. This central role of cells frames the basic questions as to how cells work and how cells come to be the way they are. Although these two lines of inquiry lie respectively within the traditional provenance of cell biology and evolutionary biology, a comprehensive synthesis of evolutionary and cell-biological thinking is lacking. We define evolutionary cell biology as the fusion of these two eponymous fields with the theoretical and quantitative branches of biochemistry, biophysics, and population genetics. The key goals are to develop a mechanistic understanding of general evolutionary processes, while specifically infusing cell biology with an evolutionary perspective. The full development of this interdisciplinary field has the potential to solve numerous problems in diverse areas of biology, including the degree to which selection, effectively neutral processes, historical contingencies, and/or constraints at the chemical and biophysical levels dictate patterns of variation for intracellular features. These problems can now be examined at both the within- and among-species levels, with single-cell methodologies even allowing quantification of variation within genotypes. Some results from this emerging field have already had a substantial impact on cell biology...

Segmentation is a tool presented for representation and approximation of data, according to a set of appropriate models. These procedures have applications to many different domains, such as time series analysis, polygonal approximation, Air Traffic Control,... Different heuristic and metaheuristic proposals have been introduced to deal with this issue. This thesis provides a novel multiobjective evolutionary method, analyzing the required general tools for the application evolutionary algorithms to real problems and the specific modifications required over the different steps of general proposals to adapt them to the segmentation domain. An introduction to the domain is presented by means of the design of a specific heuristic for segmentation of Air Traffic Control (ATC) data. This domain has a series of characteristics which make it difficult to be faced with traditional techniques: noisy data and a large number of measurements. The proposal works on two phases, using a pre-segmentation which introduces available domain information and applying a standard technique over this initial technique's results. Its results according to the presented domain, tested with a set of eight different representative trajectories, show competitive advantages compared to general approaches...

Evolutionary Computation is useful in a broad range of practical applications, however currently generalized algorithms tend to be focused upon solving problems in a theoretical domain. We aim to develop a range of generalised algorithms more suited than current algorithms to practical applications. We contextualize our algorithms using the elite sport of Team Pursuit Track Cycling, which features as part of the Summer Olympics. The sport is fiercely competitive and fractions of a second often separate the world’s leading teams. We set about using Evolutionary Computation to optimise strategies for elite teams of cyclists through changes in the transition timings and the riders power outputs. We trial our range of Evolutionary Computation methods, comparing various algorithms and running them within a time frame suitable for use in a real world environment. We find significantly better results are able to be obtained through our methods than current strategies being developed at an elite level and find the use of the developed algorithms favourable for use in a practical environment.; Claire Diora Jordan and Trent Kroeger

Questa tesi raccoglie il lavoro di tre anni di ricerche e studi nel settore dei nanomateriali, nanostrutture ed in generale dei compositi avanzati effettuati presso la Scuola di Ingegneria Aerospaziale della “Sapienza” Università di Roma. In particolare lo scopo è stato quello di approfondire l’interazione tra campi elettromagnetici ed alcune tipologie di compositi avanzati basati essenzialmente su strutture in carbonio e nanomateriali. Questo tentativo ha richiesto un approccio multidisciplinare tra diversi settori scientifici che comprendono quello dei materiali, delle strutture, dei processi di fabbricazione, delle nanotecnologie e dell’elettromagnetismo, i cui concetti di base sono, in questo contesto, dati per acquisiti e per il cui approfondimento si rimanda a testi specifici. L’obiettivo principale è stato quello di utilizzare queste conoscenze trasversali per progettare e costruire nuovi materiali/strutture in grado di assorbire efficacemente i campi elettromagnetici in un ampio intervallo di frequenze ed angoli d’incidenza con molteplici applicazioni anche se l’ambito su cui si è lavorato è quello aerospaziale. Per ottimizzare questi materiali/strutture si è fatto ricorso all’utilizzazione di algoritmi evoluzionistici che sono entrati a pieno titolo nello studio multidisciplinare con uno stretto collegamento tra la teoria sviluppata e le prove di laboratorio atte a validare sperimentalmente i modelli matematici proposti.; This Thesis is focused on scientific research on composite materials electromagnetic characterization and electric conductive polymers applications. Mainly two different composite materials types are taken into account...

This paper presents a survey of evolutionary algorithms that are designed for decision-tree induction. In this context, most of the paper focuses on approaches that evolve decision trees as an alternate heuristics to the traditional top-down divide-and-conquer approach. Additionally, we present some alternative methods that make use of evolutionary algorithms to improve particular components of decision-tree classifiers. The paper's original contributions are the following. First, it provides an up-to-date overview that is fully focused on evolutionary algorithms and decision trees and does not concentrate on any specific evolutionary approach. Second, it provides a taxonomy, which addresses works that evolve decision trees and works that design decision-tree components by the use of evolutionary algorithms. Finally, a number of references are provided that describe applications of evolutionary algorithms for decision-tree induction in different domains. At the end of this paper, we address some important issues and open questions that can be the subject of future research.; Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES); Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq); Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)

Since genetic algorithm was proposed by John Holland (Holland J. H., 1975) in the early 1970s, the study of evolutionary algorithm has emerged as a popular research field (Civicioglu & Besdok, 2013). Researchers from various scientific and engineering disciplines have been digging into this field, exploring the unique power of evolutionary algorithms (Hadka & Reed, 2013). Many applications have been successfully proposed in the past twenty years. For example, mechanical design (Lampinen & Zelinka, 1999), electromagnetic optimization (Rahmat-Samii & Michielssen, 1999), environmental protection (Bertini, Felice, Moretti, & Pizzuti, 2010), finance (Larkin & Ryan, 2010), musical orchestration (Esling, Carpentier, & Agon, 2010), pipe routing (Furuholmen, Glette, Hovin, & Torresen, 2010), and nuclear reactor core design (Sacco, Henderson, Rios-Coelho, Ali, & Pereira, 2009). In particular, its function optimization capability was highlighted (Goldberg & Richardson, 1987) because of its high adaptability to different function landscapes, to which we cannot apply traditional optimization techniques (Wong, Leung, & Wong, 2009). Here we review the applications of evolutionary algorithms in bioinformatics.

Evolutionary algorithms (EA) have been widely accepted as efficient solvers for complex real world optimization problems, including engineering optimization. However, real world optimization problems often involve uncertain environment including noisy and/or dynamic environments, which pose major challenges to EA-based optimization. The presence of noise interferes with the evaluation and the selection process of EA, and thus adversely affects its performance. In addition, as presence of noise poses challenges to the evaluation of the fitness function, it may need to be estimated instead of being evaluated. Several existing approaches attempt to address this problem, such as introduction of diversity (hyper mutation, random immigrants, special operators) or incorporation of memory of the past (diploidy, case based memory). However, these approaches fail to adequately address the problem. In this paper we propose a Distributed Population Switching Evolutionary Algorithm (DPSEA) method that addresses optimization of functions with noisy fitness using a distributed population switching architecture, to simulate a distributed self-adaptive memory of the solution space. Local regression is used in the pseudo-populations to estimate the fitness. Successful applications to benchmark test problems ascertain the proposed method's superior performance in terms of both robustness and accuracy.; Comment: In Proceedings of the The 9th IEEE Conference on Industrial Electronics and Applications (ICIEA 2014)...

Experimental multi-objective Quantum Control is an emerging topic within the broad physics and chemistry applications domain of controlling quantum phenomena. This realm offers cutting edge ultrafast laser laboratory applications, which pose multiple objectives, noise, and possibly constraints on the high-dimensional search. In this study we introduce the topic of Multi-Observable Quantum Control (MOQC), and consider specific systems to be Pareto optimized subject to uncertainty, either experimentally or by means of simulated systems. The latter include a family of mathematical test-functions with a practical link to MOQC experiments, which are introduced here for the first time. We investigate the behavior of the multi-objective version of the Covariance Matrix Adaptation Evolution Strategy (MO-CMA-ES) and assess its performance on computer simulations as well as on laboratory closed-loop experiments. Overall, we propose a comprehensive study on experimental evolutionary Pareto optimization in high-dimensional continuous domains, draw some practical conclusions concerning the impact of fitness disturbance on algorithmic behavior, and raise several theoretical issues in the broad evolutionary multi-objective context.

This paper examines the four main types of Evolutionary Design by computers: Evolutionary Design Optimisation, Evolutionary Art, Evolutionary Artificial Life Forms and Creative Evolutionary Design. Definitions for all four areas are provided. A review of current work in each of these areas is given, with examples of the types of applications that have been tackled. The different properties and requirements of each are examined. Descriptions of typical representations and evolutionary algorithms are provided and examples of designs evolved using these techniques are shown. The paper then discusses how the boundaries of these areas are beginning to merge, resulting in four new 'overlapping' types of Evolutionary Design: Integral Evolutionary Design, Artificial Life Based Evolutionary Design, Aesthetic Evolutionary AL and Aesthetic Evolutionary Design. Finally, the last part of the paper discusses some common problems faced by creators of Evolutionary Design systems, including: interdependent elements in designs, epistasis, and constraint handling.; Comment: In Proceedings of the 3rd On-line World Conference on Soft Computing in Engineering Design and Manufacturing (WSC3)