Scalable parallel evolutionary optimisation based on high performance computing

Evolutionary algorithms (EAs) have been successfully applied to solve various challenging optimisation problems. Due to their stochastic nature, EAs typically require considerable time to find desirable solutions; especially for increasingly complex and large-scale problems. As a result, many works studied implementing EAs on parallel computing facilities to accelerate the time-consuming processes. Recently, the rapid development of modern parallel computing facilities such as the high performance computing (HPC) bring not only unprecedented computational capabilities but also challenges on designing parallel algorithms. This thesis mainly focuses on designing scalable parallel evolutionary optimisation (SPEO) frameworks which run efficiently on the HPC. Motivated by the interesting phenomenon that many EAs begin to employ increasingly large population sizes, this thesis firstly studies the effect of a large population size through comprehensive experiments. Numerical results indicate that a large population benefits to the solving of complex problems but requires a large number of maximal fitness evaluations (FEs). However, since sequential EAs usually requires a considerable computing time to achieve extensive FEs, we propose a scalable parallel evolutionary optimisation framework that can efficiently deploy parallel EAs over many CPU cores at CPU-only HPC. On the other hand, since EAs using a large number of FEs can produce massive useful information in the course of evolution, we design a surrogate-based approach to learn from this historical information and to better solve complex problems. Then this approach is implemented in parallel based on the proposed scalable parallel framework to achieve remarkable speedups. Since demanding a great computing power on CPU-only HPC is usually very expensive, we design a framework based on GPU-enabled HPC to improve the cost-effectiveness of parallel EAs. The proposed framework can efficiently accelerate parallel EAs using many GPUs and can achieve superior cost-effectiveness. However, since it is very challenging to correctly implement parallel EAs on the GPU, we propose a set of guidelines to verify the correctness of GPU-based EAs. In order to examine these guidelines, they are employed to verify a GPU-based brain storm optimisation that is also proposed in this thesis. In conclusion, the comprehensively experimental study is firstly conducted to investigate the impacts of a large population. After that, a SPEO framework based on CPU-only HPC is proposed and is employed to accelerate a time-consuming implementation of EA. Finally, the correctness verification of implementing EAs based on a single GPU is discussed and the SPEO framework is then extended to be deployed based on GPU-enabled HPC

Bio-inspired computation: where we stand and what's next

In recent years, the research community has witnessed an explosion of literature dealing with the adaptation of behavioral patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques

Bio-inspired computation: where we stand and what's next

In recent years, the research community has witnessed an explosion of literature dealing with the adaptation of behavioral patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques

Treasure hunt : a framework for cooperative, distributed parallel optimization

Orientador: Prof. Dr. Daniel WeingaertnerCoorientadora: Profa. Dra. Myriam Regattieri DelgadoTese (doutorado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa : Curitiba, 27/05/2019Inclui referências: p. 18-20Área de concentração: Ciência da ComputaçãoResumo: Este trabalho propõe um framework multinível chamado Treasure Hunt, que é capaz de distribuir algoritmos de busca independentes para um grande número de nós de processamento. Com o objetivo de obter uma convergência conjunta entre os nós, este framework propõe um mecanismo de direcionamento que controla suavemente a cooperação entre múltiplas instâncias independentes do Treasure Hunt. A topologia em árvore proposta pelo Treasure Hunt garante a rápida propagação da informação pelos nós, ao mesmo tempo em que provê simutaneamente explorações (pelos nós-pai) e intensificações (pelos nós-filho), em vários níveis de granularidade, independentemente do número de nós na árvore. O Treasure Hunt tem boa tolerância à falhas e está parcialmente preparado para uma total tolerância à falhas. Como parte dos métodos desenvolvidos durante este trabalho, um método automatizado de Particionamento Iterativo foi proposto para controlar o balanceamento entre explorações e intensificações ao longo da busca. Uma Modelagem de Estabilização de Convergência para operar em modo Online também foi proposto, com o objetivo de encontrar pontos de parada com bom custo/benefício para os algoritmos de otimização que executam dentro das instâncias do Treasure Hunt. Experimentos em benchmarks clássicos, aleatórios e de competição, de vários tamanhos e complexidades, usando os algoritmos de busca PSO, DE e CCPSO2, mostram que o Treasure Hunt melhora as características inerentes destes algoritmos de busca. O Treasure Hunt faz com que os algoritmos de baixa performance se tornem comparáveis aos de boa performance, e os algoritmos de boa performance possam estender seus limites até problemas maiores. Experimentos distribuindo instâncias do Treasure Hunt, em uma rede cooperativa de até 160 processos, demonstram a escalabilidade robusta do framework, apresentando melhoras nos resultados mesmo quando o tempo de processamento é fixado (wall-clock) para todas as instâncias distribuídas do Treasure Hunt. Resultados demonstram que o mecanismo de amostragem fornecido pelo Treasure Hunt, aliado à maior cooperação entre as múltiplas populações em evolução, reduzem a necessidade de grandes populações e de algoritmos de busca complexos. Isto é especialmente importante em problemas de mundo real que possuem funções de fitness muito custosas. Palavras-chave: Inteligência artificial. Métodos de otimização. Algoritmos distribuídos. Modelagem de convergência. Alta dimensionalidade.Abstract: This work proposes a multilevel framework called Treasure Hunt, which is capable of distributing independent search algorithms to a large number of processing nodes. Aiming to obtain joint convergences between working nodes, Treasure Hunt proposes a driving mechanism that smoothly controls the cooperation between the multiple independent Treasure Hunt instances. The tree topology proposed by Treasure Hunt ensures quick propagation of information, while providing simultaneous explorations (by parents) and exploitations (by children), on several levels of granularity, regardless the number of nodes in the tree. Treasure Hunt has good fault tolerance and is partially prepared to full fault tolerance. As part of the methods developed during this work, an automated Iterative Partitioning method is proposed to control the balance between exploration and exploitation as the search progress. A Convergence Stabilization Modeling to operate in Online mode is also proposed, aiming to find good cost/benefit stopping points for the optimization algorithms running within the Treasure Hunt instances. Experiments on classic, random and competition benchmarks of various sizes and complexities, using the search algorithms PSO, DE and CCPSO2, show that Treasure Hunt boosts the inherent characteristics of these search algorithms. Treasure Hunt makes algorithms with poor performances to become comparable to good ones, and algorithms with good performances to be capable of extending their limits to larger problems. Experiments distributing Treasure Hunt instances in a cooperative network up to 160 processes show the robust scaling of the framework, presenting improved results even when fixing a wall-clock time for the instances. Results show that the sampling mechanism provided by Treasure Hunt, allied to the increased cooperation between multiple evolving populations, reduce the need for large population sizes and complex search algorithms. This is specially important on real-world problems with time-consuming fitness functions. Keywords: Artificial intelligence. Optimization methods. Distributed algorithms. Convergence modeling. High dimensionality

Técnicas de optimización paralelas : esquema híbrido basado en hiperheurísticas y computación evolutiva

Optimisation is the process of selecting the best element fr om a set of available alternatives. Solutions are termed good or bad depending on its performance for a set of objectives. Several algorithms to deal with such kind of problems have been defined in the literature. Metaheuristics are one of the most prominent techniques. They are a class of modern heuristics whose main goal is to com bine heuristics in a problem independent way with the aim of improving their per formance. Meta- heuristics have reported high-quality solutions in severa l fields. One of the reasons of the good behaviour of metaheuristics is that they are defin ed in general terms. Therefore, metaheuristic algorithms can be adapted to fit th e needs of most real-life optimisation. However, such an adaptation is a hard task, and it requires a high computational and user effort. There are two main ways of reducing the effort associated to th e usage of meta- heuristics. First, the application of hyperheuristics and parameter setting strategies facilitates the process of tackling novel optimisation pro blems and instances. A hyperheuristic can be viewed as a heuristic that iterativel y chooses between a set of given low-level metaheuristics in order to solve an optim isation problem. By using hyperheuristics, metaheuristic practitioners do no t need to manually test a large number of metaheuristics and parameterisations for d iscovering the proper algorithms to use. Instead, they can define the set of configur ations which must be tested, and the model tries to automatically detect the be st-behaved ones, in order to grant more resources to them. Second, the usage of pa rallel environments might speedup the process of automatic testing, so high qual ity solutions might be achieved in less time. This research focuses on the design of novel hyperheuristic s and defines a set of models to allow their usage in parallel environments. Differ ent hyperheuristics for controlling mono-objective and multi-objective multi-po int optimisation strategies have been defined. Moreover, a set of novel multiobjectivisa tion techniques has been proposed. In addition, with the aim of facilitating the usage of multiobjectivi- sation, the performance of models that combine the usage of m ultiobjectivisation and hyperheuristics has been studied. The proper performance of the proposed techniques has been v alidated with a set of well-known benchmark optimisation problems. In addi tion, several practical and complex optimisation problems have been addressed. Som e of the analysed problems arise in the communication field. In addition, a pac king problem proposed in a competition has been faced up. The proposals for such pro blems have not been limited to use the problem-independent schemes. Inste ad, new metaheuristics, operators and local search strategies have been defined. Suc h schemes have been integrated with the designed parallel hyperheuristics wit h the aim of accelerating the achievement of high quality solutions, and with the aim of fa cilitating their usage. In several complex optimisation problems, the current best -known solutions have been found with the methods defined in this dissertation.Los problemas de optimización son aquellos en los que hay que elegir cuál es la solución más adecuada entre un conjunto de alternativas. Actualmente existe una gran cantidad de algoritmos que permiten abordar este tipo de problemas. Entre ellos, las metaheurísticas son una de las técnicas más usadas. El uso de metaheurísticas ha posibilitado la resolución de una gran cantidad de problemas en diferentes campos. Esto se debe a que las metaheurísticas son técnicas generales, con lo que disponen de una gran cantidad de elementos o parámetros que pueden ser adaptados a la hora de afrontar diferentes problemas de optimización. Sin embargo, la elección de dichos parámetros no es sencilla, por lo que generalmente se requiere un gran esfuerzo computacional, y un gran esfuerzo por parte del usuario de estas técnicas. Existen diversas técnicas que atenúan este inconveniente. Por un lado, existen varios mecanismos que permiten seleccionar los valores de dichos parámetros de forma automática. Las técnicas más simples utilizan valores fijos durante toda la ejecución, mientras que las técnicas más avanzadas, como las hiperheurísticas, adaptan los valores usados a las necesidades de cada fase de optimización. Además, estas técnicas permiten usar varias metaheurísticas de forma simultánea. Por otro lado, el uso de técnicas paralelas permite acelerar el proceso de testeo automático, reduciendo el tiempo necesario para obtener soluciones de alta calidad. El objetivo principal de esta tesis ha sido diseñar nuevas hiperheurísticas e integrarlas en el modelo paralelo basado en islas. Estas técnicas se han usado para controlar los parámetros de varias metaheurísticas evolutivas. Se han definido diversas hiperheurísticas que han permitido abordar tanto problemas mono-objetivo como problemas multi-objetivo. Además, se han definido un conjunto de multiobjetivizaciones, que a su vez se han beneficiado de las hiperheurísticas propuestas. Las técnicas diseñadas se han validado con algunos de los problemas de test más ampliamente utilizados. Además, se han abordado un conjunto de problemas de optimización prácticos. Concretamente, se han tratado tres problemas que surgen en el ámbito de las telecomunicaciones, y un problema de empaquetado. En dichos problemas, además de usar las hiperheurísticas y multiobjetivizaciones, se han definido nuevos algoritmos, operadores, y estrategias de búsqueda local. En varios de los problemas, el uso combinado de todas estas técnicas ha posibilitado obtener las mejores soluciones encontradas hasta el momento

Estructura genética, filogeografía, variación adaptativa y especiación en árboles tropicales del género Symphonia

The genetic structure within a species is the result of the levels of the genetic diversity and its spatial distribution. Also, it depends significantly on the specific evolutionary history experienced by the species. Thus, to disentangle the overlapping evolutionary processes acting at different levels in a species or a taxon, it will be necessary to work at different spatial scales and at different taxonomic levels as complementary approaches. The study of the fine-scale spatial genetic structure in plants (the micro scale approach) will imply to work at the shortest spatial scales and to capture detailed information on the spatial distribution of genotypes at within-population scale. The analysis at this scale will help to detect mainly evolutionary and ecological processes more related to short‐term periods of time and/or smaller spatial scales such as habitat fragmentation and other disturbances, efficiency of dispersal mechanisms or gene dispersal distances. On the other side, the study of the genetic structure at wider scales (the macro scale approach), including both geographical and taxonomic (i.e., speciation) points of view, will usually imply to detect larger spatio-temporal processes and to work with deeper evolutionary timescales. In this sense, the spatial genetic structure within a species at this macro scale will be the result of different historical and contemporary influences such as connectivity across the range of the species and landscape barriers, environmental adaptation, demographic history or climatic events, among others. Finally, if we include the taxonomic perspective in the analysis of genetic structure in a group of closely related species, we will be able to analyse the processes leading to speciation, which also may involve those previously mentioned.La estructura genética que presenta una especie es el resultado de sus niveles de diversidad genética, así como de su distribución espacial. Además, depende significativamente de la historia evolutiva específica que ha sufrido la especie. Así, para desentrañar los procesos evolutivos que actúan simultáneamente a diferentes niveles en una especie o taxon, será necesario trabajar mediante enfoques complementarios, orientados a diferentes escalas espaciales y a diferentes niveles taxonómicos. El estudio de la estructura genética espacial a pequeña escala en plantas (un enfoque en escala micro) implicará trabajar en las escalas espaciales más pequeñas, así como recoger información detallada de la distribución espacial de genotipos dentro de las poblaciones. El análisis a esta escala ayudará a detectar principalmente procesos evolutivos y ecológicos relacionados principalmente con periodos de tiempo cortos y/o a escala espacial pequeña, tales como fragmentación de hábitats y otras perturbaciones, eficiencia de los mecanismos de dispersión o distancias de dispersión genética. Por otro lado, el estudio de la estructura genética a escala más amplia (un enfoque en escala macro), incluyendo los puntos de vista geográfico y taxonómico (es decir, de especiación), normalmente implicará detectar procesos espacio-temporales más amplios y trabajar con escalas evolutivas de tiempo más largas. En este sentido, la estructura genética espacial de una especie a escala macro será el resultado de diferentes influencias históricas y contemporáneas, tales como la conectividad a lo largo de la distribución de la especie, las barreras del paisaje, la adaptación al medio, la historia demográfica o los eventos climáticos, entre otros. Finalmente, si incluímos la perspectiva taxonómica en el análisis de la estructura genética de un grupo de especies muy relacionadas, podremos ser capaces de analizar los procesos que conducen a la especiación, entre los cuales se pueden encontrar también los ya previamente mencionados.Doctorado en Conservación y Uso Sostenible de Sistemas Forestale

Molecular Ecology and Systematics of Blue Mussels (Genus Mytilus) (Mytilidae; Bivalvia; Mollusca) in the Southern Hemisphere

The Mytilus edulis species complex, comprised of M. edulis, M. galloprovincialis and M. trossulus, is antitropically distributed in temperate coastal regions of all oceans and main seas of the world. This genus has been heavily studied in the Northern hemisphere but Southern hemisphere populations have received much less attention. This thesis aims to place Southern hemisphere blue mussels into global evolutionary relationships among Mytilus species and investigate aspects of their molecular ecology, including, effects of non-native Northern hemisphere species introductions, biogeography across the Southern hemisphere, regional phylogeographic patterns and population genetics within New Zealand. Southern hemisphere blue mussel phylogenetic reconstruction resulted in the detection of a monophyletic M. galloprovincialis lineage. Two new molecular markers developed with specificity for this lineage and congruence among phylogenetic investigations indicates a panhemispheric distribution of this M. galloprovincialis lineage with implications for naming a new sibling species of the M. edulis complex. This proposed new species, M. meridianus, is distributed in South America, the Kerguelen Islands, New Zealand and Australia at latitudes between ~ 30°S and ~ 55°S. Non-native M. galloprovincialis introduced from the Northern hemisphere have been present in NZ, Australia and Chile for at least ten years and hybridise with native blue mussels. Introgression is observed in New Zealand and Australian but not Chilean hybrid regions. The limited number of introduced mussels in Australia induces hybrid swamping of non-native alleles but an interlineage gender bias towards non-native maternal parents may result in eventual loss of the unique genomic content of native blue mussels in NZ. Southern hemisphere blue mussels form a monophyletic sister clade to a haplogroup shared by Northern hemisphere M. edulis and M. galloprovincialis. Although single gene histories are not congruent with respect to evolutionary relationships within the Northern hemisphere due to introgressive hybridisation after speciation, it is clear that Southern hemisphere blue mussels arose from a species native to the northeast Atlantic Ocean after speciation of the three ‘M. edulis complex’ members. Within the Southern hemisphere monophyletic clade lies three reciprocally monophyletic clades restricted to the geographic regions South America/Kerguelen Islands, New Zealand and Australia. Phylogeographic analysis indicates past gene flow between South American/Kerguelen Islands and New Zealand populations that has ceased at present day and ongoing gene flow between South America and the Kerguelen Islands likely via the West Wind Drift. Within NZ, population subdivision inferred from mtDNA indicates genetic variation is distributed within an east-west phylogeographic split on the North Island. These populations experienced gene flow in the past that has ceased at present day. Microsatellite allele frequencies indicate a different population subdivision within the northwest North Island that has been narrowed down to a 15 km stretch of coastline in a sheltered bay. The abrupt discontinuity within a small geographic area does not conform to classic population subdivision in this broad-cast spawning species, therefore, further investigation into the genomic content of northwest North Island mussels with respect to introgressed non-native genes is warranted. Resolving complex phylogenetic patterns from interspecific introgression is key to understanding the evolutionary history of Southern hemisphere M. galloprovincialis. Further characterisation of hybrid introgression would increase accuracy of (1) inferences of processes contributing to hybridisation dynamics and (2) population subdivision in NZ. Probing the basis for variation of hybridisation dynamics would help to predict the outcomes of Northern hemisphere M. galloprovincialis introductions in other areas of the world

Never Too Old To Learn: On-line Evolution of Controllers in Swarm- and Modular Robotics

Eiben, A.E. [Promotor