Reticulate Evolution: Symbiogenesis, Lateral Gene Transfer, Hybridization and Infectious heredity

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Populações baseadas em multisets para algoritmos evolutivos

Os algoritmos evolutivos simulam a evolução natural de uma população de indivíduos aplicando iterativamente operadores genéticos, recombinação, mutação e seleção dos mais aptos. O processo evolutivo pode ser visto como um processo de otimização. Nesse caso, os indivíduos representam soluções do problema e as variáveis do problema são codificados no equivalente aos genes. Estes algoritmos podem ser facilmente implementados e existem variantes especializadas para resolver várias classes de problemas. Uma das maiores dificuldades apresentadas por estes algoritmos é a convergência prematura da população para soluções sub-ótimas antes do espaço de procura ser devidamente explorado. Várias estratégias foram desenvolvidas para reduzir este risco e, neste trabalho, estudamos a possibilidade de substituir a representação da população. Tradicionalmente as populações são representadas como coleções de indivíduos e nesta tese propomos a sua substituição por um multiconjunto (multiset). Esta nova forma de representação das populações, que denominamos multipopulações, permite manipular um conjunto de genomas e os seus clones, multi-indivíduos, de uma forma muito eficiente. Adaptamos o processo evolutivo para otimizar multipopulações, estudamos o seu comportamento em vários tipos de algoritmos e problemas e desenvolvemos operadores genéticos especializados para trabalhar com a nova representação. Em resultado disso obtemos uma forma inovadora de manter uma elevada diversidade genética na população. As experiências realizadas permitiram-nos compreender melhor a dinâmica que a nova representação introduz no processo evolutivo e mostrar a sua eficácia.Evolutionary algorithms simulate the natural evolution of a population of individuals by iteratively applying genetic operators, recombination, mutation and selection of the fittest. The evolutionary process can be viewed as an optimization process. In this case, individuals represent problem solutions and the problem variables are encoded in that equivalent to the gene. These algorithms can be easily implemented and there are specialized variants to solve different classes of problems. One of the biggest difficulties presented by these algorithms is the premature convergence of the population to suboptimal solutions before the search space is properly explored. Several strategies were developed to reduce this risk and, in this thesis, we studied the possibility of replacing the representation of the population. Traditionally populations are represented as collections of individuals and in this thesis we propose its replacement by a multiset. This new form of population representation, which we call multipopulations, allows manipulating a set of genomes and their clones, multi-individuals, in a very efficient way. We adapt the evolutionary process to optimize multipopulations, study their behavior on various types of algorithms and problems, and develop specialized genetic operators to work with the new representation. As a result, we get an innovative way to maintain a high genetic diversity in the population. The experiments allowed us to better understand the dynamics that the new representation introduces in the evolutionary process and show its effectiveness

Comparison between two genetic algorithms minimizing carbon footprint of energy and materials in a residential building

The emergence of building performance optimization is recognized as a way to achieve sustainable building designs. In this paper, the problem consists in minimizing simultaneously the emissions of greenhouse gases (GHG) related to building energy consumption and those related to building materials. This multi-objective optimization problem involves variables with different hierarchical levels, i.e. variables that can become obsolete depending on the value of the other variables. To solve it, NSGA-II is compared with an algorithm designed specifically to deal with hierarchical variables, namely sNSGA. Evaluation metrics such as convergence, diversity and hypervolume show that both algorithms handle hierarchical variables, but the analysis of the Pareto front confirms that in the present case, NSGA-II is better to identify optimal solutions than sNSGA. All the optimal solutions are made of buildings with wooden envelopes and relied either on heat pumps or on electrical heaters for proving heating

Analysis of Linkage-Friendly Genetic Algorithms

Evolutionary algorithms (EAs) are stochastic population-based algorithms inspired by the natural processes of selection, mutation, and recombination. EAs are often employed as optimum seeking techniques. A formal framework for EAs is proposed, in which evolutionary operators are viewed as mappings from parameter spaces to spaces of random functions. Formal definitions within this framework capture the distinguishing characteristics of the classes of recombination, mutation, and selection operators. EAs which use strictly invariant selection operators and order invariant representation schemes comprise the class of linkage-friendly genetic algorithms (lfGAs). Fast messy genetic algorithms (fmGAs) are lfGAs which use binary tournament selection (BTS) with thresholding, periodic filtering of a fixed number of randomly selected genes from each individual, and generalized single-point crossover. Probabilistic variants of thresholding and filtering are proposed. EAs using the probabilistic operators are generalized fmGAs (gfmGAs). A dynamical systems model of lfGAs is developed which permits prediction of expected effectiveness. BTS with probabilistic thresholding is modeled at various levels of abstraction as a Markov chain. Transitions at the most detailed level involve decisions between classes of individuals. The probability of correct decision making is related to appropriate maximal order statistics, the distributions of which are obtained. Existing filtering models are extended to include probabilistic individual lengths. Sensitivity of lfGA effectiveness to exogenous parameters limits practical applications. The lfGA parameter selection problem is formally posed as a constrained optimization problem in which the cost functional is related to expected effectiveness. Kuhn-Tucker conditions for the optimality of gfmGA parameters are derived

A review of estimation of distribution algorithms in bioinformatics

Evolutionary search algorithms have become an essential asset in the algorithmic toolbox for solving high-dimensional optimization problems in across a broad range of bioinformatics problems. Genetic algorithms, the most well-known and representative evolutionary search technique, have been the subject of the major part of such applications. Estimation of distribution algorithms (EDAs) offer a novel evolutionary paradigm that constitutes a natural and attractive alternative to genetic algorithms. They make use of a probabilistic model, learnt from the promising solutions, to guide the search process. In this paper, we set out a basic taxonomy of EDA techniques, underlining the nature and complexity of the probabilistic model of each EDA variant. We review a set of innovative works that make use of EDA techniques to solve challenging bioinformatics problems, emphasizing the EDA paradigm's potential for further research in this domain

Improving Population-Based Algorithms with Fitness Deterioration, Journal of Telecommunications and Information Technology, 2011, nr 4

This work presents a new hybrid approach for supporting sequential niching strategies called Cluster Supported Fitness Deterioration (CSFD). Sequential niching is one of the most promising evolutionary strategies for analyzing multimodal global optimization problems in the continuous domains embedded in the vector metric spaces. In each iteration CSFD performs the clustering of the random sample by OPTICS algorithm and then deteriorates the fitness on the area occupied by clusters. The selection pressure pushes away the next-step sample (population) from the basins of attraction of minimizers already recognized, speeding up finding the new ones. The main advantages of CSFD are low memory an computational complexity even in case of large dimensional problems and high accuracy of deterioration obtained by the flexible cluster definition delivered by OPTICS. The paper contains the broad discussion of niching strategies, detailed definition of CSFD and the series of the simple comparative tests

On the Convergence of Biogeography-Based Optimization for Binary Problems

Biogeography-based optimization (BBO) is an evolutionary algorithm inspired by biogeography, which is the study of the migration of species between habitats. A finite Markov chain model of BBO for binary problems was derived in earlier work, and some significant theoretical results were obtained. This paper analyzes the convergence properties of BBO on binary problems based on the previously derived BBO Markov chain model. Analysis reveals that BBO with only migration and mutation never converges to the global optimum. However, BBO with elitism, which maintains the best candidate in the population from one generation to the next, converges to the global optimum. In spite of previously published differences between genetic algorithms (GAs) and BBO, this paper shows that the convergence properties of BBO are similar to those of the canonical GA. In addition, the convergence rate estimate of BBO with elitism is obtained in this paper and is confirmed by simulations for some simple representative problems