The Youngest, the Heaviest and/or the Darkest? Selection Potentialities and Determinants of Leadership in Canarian Dromedary Camels

Several idiosyncratic and genetically correlated traits are known to extensively influence leadership in both domestic and wild species. For minor livestock such as camels, however, this type of behavior remains loosely defined and approached only for sex-mixed herds. The interest in knowing those animal-dependent variables that make an individual more likely to emerge as a leader in a single-sex camel herd has its basis in the sex-separated breeding of Canarian dromedary camels for utilitarian purposes. By means of an ordinal logistic regression, it was found that younger, gelded animals may perform better when eliciting the joining of mates, assuming that they were castrated just before reaching sexual maturity and once they were initiated in the pertinent domestication protocol for their lifetime functionality. The higher the body weight, the significantly (p < 0.05) higher the score in the hierarchical rank when leading group movements, although this relationship appeared to be inverse for the other considered zoometric indexes. Camels with darker and substantially depigmented coats were also significantly (p < 0.05) found to be the main initiators. Routine intraherd management and leisure tourism will be thus improved in efficiency and security through the identification and selection of the best leader camels

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

Machine learning for improving heuristic optimisation

Heuristics, metaheuristics and hyper-heuristics are search methodologies which have been preferred by many researchers and practitioners for solving computationally hard combinatorial optimisation problems, whenever the exact methods fail to produce high quality solutions in a reasonable amount of time. In this thesis, we introduce an advanced machine learning technique, namely, tensor analysis, into the field of heuristic optimisation. We show how the relevant data should be collected in tensorial form, analysed and used during the search process. Four case studies are presented to illustrate the capability of single and multi-episode tensor analysis processing data with high and low abstraction levels for improving heuristic optimisation. A single episode tensor analysis using data at a high abstraction level is employed to improve an iterated multi-stage hyper-heuristic for cross-domain heuristic search. The empirical results across six different problem domains from a hyper-heuristic benchmark show that significant overall performance improvement is possible. A similar approach embedding a multi-episode tensor analysis is applied to the nurse rostering problem and evaluated on a benchmark of a diverse collection of instances, obtained from different hospitals across the world. The empirical results indicate the success of the tensor-based hyper-heuristic, improving upon the best-known solutions for four particular instances. Genetic algorithm is a nature inspired metaheuristic which uses a population of multiple interacting solutions during the search. Mutation is the key variation operator in a genetic algorithm and adjusts the diversity in a population throughout the evolutionary process. Often, a fixed mutation probability is used to perturb the value at each locus, representing a unique component of a given solution. A single episode tensor analysis using data with a low abstraction level is applied to an online bin packing problem, generating locus dependent mutation probabilities. The tensor approach improves the performance of a standard genetic algorithm on almost all instances, significantly. A multi-episode tensor analysis using data with a low abstraction level is embedded into multi-agent cooperative search approach. The empirical results once again show the success of the proposed approach on a benchmark of flow shop problem instances as compared to the approach which does not make use of tensor analysis. The tensor analysis can handle the data with different levels of abstraction leading to a learning approach which can be used within different types of heuristic optimisation methods based on different underlying design philosophies, indeed improving their overall performance

Machine learning for improving heuristic optimisation

Heuristics, metaheuristics and hyper-heuristics are search methodologies which have been preferred by many researchers and practitioners for solving computationally hard combinatorial optimisation problems, whenever the exact methods fail to produce high quality solutions in a reasonable amount of time. In this thesis, we introduce an advanced machine learning technique, namely, tensor analysis, into the field of heuristic optimisation. We show how the relevant data should be collected in tensorial form, analysed and used during the search process. Four case studies are presented to illustrate the capability of single and multi-episode tensor analysis processing data with high and low abstraction levels for improving heuristic optimisation. A single episode tensor analysis using data at a high abstraction level is employed to improve an iterated multi-stage hyper-heuristic for cross-domain heuristic search. The empirical results across six different problem domains from a hyper-heuristic benchmark show that significant overall performance improvement is possible. A similar approach embedding a multi-episode tensor analysis is applied to the nurse rostering problem and evaluated on a benchmark of a diverse collection of instances, obtained from different hospitals across the world. The empirical results indicate the success of the tensor-based hyper-heuristic, improving upon the best-known solutions for four particular instances. Genetic algorithm is a nature inspired metaheuristic which uses a population of multiple interacting solutions during the search. Mutation is the key variation operator in a genetic algorithm and adjusts the diversity in a population throughout the evolutionary process. Often, a fixed mutation probability is used to perturb the value at each locus, representing a unique component of a given solution. A single episode tensor analysis using data with a low abstraction level is applied to an online bin packing problem, generating locus dependent mutation probabilities. The tensor approach improves the performance of a standard genetic algorithm on almost all instances, significantly. A multi-episode tensor analysis using data with a low abstraction level is embedded into multi-agent cooperative search approach. The empirical results once again show the success of the proposed approach on a benchmark of flow shop problem instances as compared to the approach which does not make use of tensor analysis. The tensor analysis can handle the data with different levels of abstraction leading to a learning approach which can be used within different types of heuristic optimisation methods based on different underlying design philosophies, indeed improving their overall performance

Design of vehicle routing problem domains for a hyper-heuristic framework

The branch of algorithms that uses adaptive methods to select or tune heuristics, known as hyper-heuristics, is one that has seen a large amount of interest and development in recent years. With an aim to develop techniques that can deliver results on multiple problem domains and multiple instances, this work is getting ever closer to mirroring the complex situations that arise in the corporate world. However, the capability of a hyper-heuristic is closely tied to the representation of the problem it is trying to solve and the tools that are available to do so. This thesis considers the design of such problem domains for hyper-heuristics. In particular, this work proposes that through the provision of high-quality data and tools to a hyper-heuristic, improved results can be achieved. A definition is given which describes the components of a problem domain for hyper-heuristics. Building on this definition, a domain for the Vehicle Routing Problem with Time Windows is presented. Through this domain, examples are given of how a hyper- heuristic can be provided extra information with which to make intelligent search decisions. One of these pieces of information is a measure of distance between solution which, when used to aid selection of mutation heuristics, is shown to improve results of an Iterative Local Search hyper-heuristic. A further example of the advantages of providing extra information is given in the form of the provision of a set of tools for the Vehicle Routing Problem domain to promote and measure ’fairness’ between routes. By offering these extra features at a domain level, it is shown how a hyper-heuristic can drive toward a fairer solution while maintaining a high level of performance

The Invisibles in IB: How State agents’ export promotion support, finance, and time influence firms’ internationalization

LOI. This thesis joins the conversation about the internationalization of the firm: Dunning’s Eclectic Paradigm (Ownership, Location, Internalization) operates as an envelope to anchor important factors that have previously received little attention in the IB conversation. These missing elements are the role of finance in explaining internationalization, the role of State agents, i.e. ECAs, and the role of time. The reason why it is important to deal with these omissions is because, many firms rely on export credit agencies’ (ECAs’) services to conduct either their early internationalization, or they rely on ECAs to provide financing tools to enable their later international business activities. This factor has surprisingly been neglected in most IB explanations on internationalization and the success and failure of MNEs. Within the thesis, I studied these in three separate studies. Essay 1, 2 and 3, have been conducted to shed light on the role of ECAs in firms’ internationalization. The three studies brought up surprising observations that were not sufficiently considered in the explanations of internationalization and how MNE safeguard their business. I focused on novel elements and applied an abductive reasoning (Saetre & Van de Ven, 2021). The surprising results in each of the studies, let me to search for a theoretical anchoring framework. I used Dunning’s OLI as an envelope to integrate the most important factors. These enhance our understanding of internationalization. Then I propose a new direction for the three dimensions conceptual model, presented as LOI. Regarding the contributions, I found as the results that the focused factors, finance, state agents and time, increase our understanding how ECAs support firms’ capabilities and influence how firms make their internationalization decisions. And I am highlighting new factors that increase the explanatory power of the internationalization journey of firms: capabilities, willingness and the goal. The research contributes to academia beyond IB through the methodology used. The abductive reasoning serves as a concrete illustration of how a human brain conceives and constructs internationalization decision making. The dimension that connects it to natural Life sciences and biological models, is the innovation of this thesis: the concept of time internalization.---- LOI. Tämän väitöskirjan kontribuutio kohdistuu teoretisointiin näkymättömien tekijöiden roolista yritysten kansainvälisessä liiketoiminnassa. Väitöskirja tutkii, miten viennin edistäminen, rahoitus ja aika yhdessä selittävät yritysten kansainvälistymistä. Tätä kysymystä pohditaan rationaalisen abduktion (Saetre & Van de Ven, 2021) avulla. Väitöskirjassa peilataan useiden sidosryhmien näkökulmia ja työn käsitteel-linen positio rakentuu Dunningin eklektisen paradigman ympärille. Tämä malli yhdistää omistajuuden sijaintiin liittyviin etuihin ja sisäisen hallinnan eri tyyppeihin. Tämän OLI (Ownership, Location, Internalization) -viitekehyksen sisällä tässä työssä keskitytään sijainnin eli lokaation alakohtana viennin edistämiseen, omistajuuden alakohtana rahoitukseen ja sisäisen hallinnan alakohtana ajan rooliin. Nämä tekijät ovat toisiinsa kytkeytyneitä kansainvälisissä vaihtotransaktioissa ostajien ja myyjien välillä. Vaihdon juurtuneisuus kuvaa OLI-osatekijöiden näky-mättömyyttä kansainvälisessä liiketoiminnassa. Tämä väitöskirja jakautuu neljään osaan. Ensimmäisessä esitellään yhteenveto viennin edistämiseen liittyvästä tiedosta ja määritelmistä, sekä kartoitetaan olemassa olevat viennin edistämiseen liittyvät teoriat. Toisessa osassa arvioidaan rahoitusta omistajuuteen liittyvänä tekijänä ja yrityksen ikää omistajuuteen liittyvänä etuna yritysten kansainvälistymistä edistävinä tai sitä rajoittavina tekijöinä. Kolmantena esitellään kyselytutkimuksen tuloksia viennin edistämistoimenpiteiden suhteesta yritysten kansainvälistymisen ajoitukseen. Viimeisenä keskustellaan löydösten vertailevasta analyysistä ja ehdotetaan yritysten kansainvälistymisen tukemisen strategiaa sekä vienninedistäjille että rahoittajille perustuen saatuun syvempään ymmärrykseen asiakasyritysten tavoitteista halukkuuden näkökulmasta, pelkän kyvykkyyden lisäksi. Tämä tutkimus täydentää Dunningin alkuperäistä mallia paljastamalla vähem-mälle huomiolle jääneet tekijät OLI-viitekehyksen saumakohdissa. Viitekehykseen lisätään ajan dynaamisuus sisäisen hallinnan dimensiona ja toimintahalukkuuden vaikutin, jonka rooli ajan sisäistämisessä on kriittinen. Lisäksi tutkimus selittää, miksi on tärkeää erottaa toisistaan yrityksen toimintahalukkuus ja toiminta-kyvykkyys, sillä halukkuus on kytköksissä yrityksen tehokkuuteen ja strategiaan. Tämä tutkimus ehdottaa, että erilaiset implisiittiset oletukset ajasta mahdollisesti heikentävät yrityksen toimien koordinoimista paikkaan liittyen erityisesti pitkien matkojen välillä, digitaalisissa suhteissa ja valtiorajojen yli. Lopullinen käsitteel-linen viitekehys esittää yhden ratkaisun toimivan kansainvälistymisstrategian tunnistamiseen: omistajuuden ja ajan synkronoinnin sisäistämisen kautta, mukaan lukien kyvyn ja halukkuuden saavuttaa määritelty kansainvälistymistavoite

Design of vehicle routing problem domains for a hyper-heuristic framework

The branch of algorithms that uses adaptive methods to select or tune heuristics, known as hyper-heuristics, is one that has seen a large amount of interest and development in recent years. With an aim to develop techniques that can deliver results on multiple problem domains and multiple instances, this work is getting ever closer to mirroring the complex situations that arise in the corporate world. However, the capability of a hyper-heuristic is closely tied to the representation of the problem it is trying to solve and the tools that are available to do so. This thesis considers the design of such problem domains for hyper-heuristics. In particular, this work proposes that through the provision of high-quality data and tools to a hyper-heuristic, improved results can be achieved. A definition is given which describes the components of a problem domain for hyper-heuristics. Building on this definition, a domain for the Vehicle Routing Problem with Time Windows is presented. Through this domain, examples are given of how a hyper- heuristic can be provided extra information with which to make intelligent search decisions. One of these pieces of information is a measure of distance between solution which, when used to aid selection of mutation heuristics, is shown to improve results of an Iterative Local Search hyper-heuristic. A further example of the advantages of providing extra information is given in the form of the provision of a set of tools for the Vehicle Routing Problem domain to promote and measure ’fairness’ between routes. By offering these extra features at a domain level, it is shown how a hyper-heuristic can drive toward a fairer solution while maintaining a high level of performance