Genotypic differences and migration policies in an island model

ABSTRACT In this paper we compare different policies to select individuals to migrate in an island model. Our thesis is that choosing individuals in a way that exploits genotypic differences between populations can enhance diversity, and improve the system performance. This has lead us to propose a family of policies that we call multikulti, in which nodes exchange individuals different "enough" among them. In this paper we present a policy according to which the receiver node chooses the most different individual among the sample received from the sending node. This sample is randomly built but only using individuals with a fitness above a threshold. This threshold is previously established by the receiving node. We have tested our system in two problems previously used in the evaluation of parallel systems, presenting different degree of difficulty. The multikulti policy presented herein has been proved to be more robust than other usual migration policies, such as sending the best or a random individual

Revitalizing missions on the cusp of change : complex systems science mazeways for mission theory amid twenty-first century realities

https://place.asburyseminary.edu/ecommonsatsdissertations/1909/thumbnail.jp

In situ Distributed Genetic Programming: An Online Learning Framework for Resource Constrained Networked Devices

This research presents In situ Distributed Genetic Programming (IDGP) as a framework for distributively evolving logic while attempting to maintain acceptable average performance on highly resource-constrained embedded networked devices. The framework is motivated by the proliferation of devices employing microcontrollers with communications capability and the absence of online learning approaches that can evolve programs for them. Swarm robotics, Internet of Things (IoT) devices including smart phones, and arguably the most constrained of the embedded systems, Wireless Sensor Networks (WSN) motes, all possess the capabilities necessary for the distributed evolution of logic - specifically the abilities of sensing, computing, actuation and communications. Genetic programming (GP) is a mechanism that can evolve logic for these devices using their “native” logic representation (i.e. programs) and so technically GP could evolve any behaviour that can be coded on the device. IDGP is designed, implemented, demonstrated and analysed as a framework for evolving logic via genetic programming on highly resource-constrained networked devices in real-world environments while achieving acceptable average performance. Designed with highly resource-constrained devices in mind, IDGP provides a guide for those wishing to implement genetic programming on such systems. Furthermore, an implementation on mote class devices is demonstrated to evolve logic for a time-varying sense-compute-act problem and another problem requiring the evolution of primitive communications. Distributed evolution of logic is also achieved by employing the Island Model architecture, and a comparison of individual and distributed evolution (with the same and slightly different goals) presented. This demonstrates the advantage of leveraging the fact that such devices often reside within networks of devices experiencing similar conditions. Since GP is a population-based metaheuristic which relies on the diversity of the population to achieve learning, many, if not most, programs within the population exhibit poor performance. As such, the average observed performance (pool fitness) of the population using the standard GP learning mechanism is unlikely to be acceptable for online learning scenarios. This is suspected to be the reason why no previous attempts have been made to deploy standard GP as an online learning approach. Nonetheless, the benefits of GP for evolving logic on such devices are compelling and motivated the design of a novel satisficing heuristic called Fitness Importance (FI). FI is population-based heuristic used to bias the evaluation of candidate solutions such that an “acceptable” average fitness (AAF) is achieved while also achieving ongoing, though diminished, learning capacity. This trade off motivated further investigation into whether dynamically adjusting the average performance in response to AAF would be superior to a constant, balanced, performing-learning approach. Dynamic and constant strategies were compared on a simple problem where the AAF target was changed during evolution, revealing that dynamically tracking the AAF target can yield a higher success rate in meeting the AAF. The combination of IDGP and FI offers a novel approach for achieving online learning with GP on highly resource-constrained embedded systems. Furthermore, it simultaneously considers the acceptable average performance of the system which may change during the operational lifetime. This approach could be applied to swarm and cooperative robot systems, WSN motes or IoT devices allowing them to cooperatively learn and adapt their logic locally to meet dynamic performance requirements

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

2019 GREAT Day Program

SUNY Geneseo’s Thirteenth Annual GREAT Day.https://knightscholar.geneseo.edu/program-2007/1013/thumbnail.jp

Co-evolutionary and Reinforcement Learning Techniques Applied to Computer Go players

The objective of this thesis is model some processes from the nature as evolution and co-evolution, and proposing some techniques that can ensure that these learning process really happens and useful to solve some complex problems as Go game. The Go game is ancient and very complex game with simple rules which still is a challenge for the Artificial Intelligence. This dissertation cover some approaches that were applied to solve this problem, proposing solve this problem using competitive and cooperative co-evolutionary learning methods and other techniques proposed by the author. To study, implement and prove these methods were used some neural networks structures, a framework free available and coded many programs. The techniques proposed were coded by the author, performed many experiments to find the best configuration to ensure that co-evolution is progressing and discussed the results. Using co-evolutionary learning processes can be observed some pathologies which could impact co-evolution progress. In this dissertation is introduced some techniques to solve pathologies as loss of gradients, cycling dynamics and forgetting. According to some authors, one solution to solve these co-evolution pathologies is introduce more diversity in populations that are evolving. In this thesis is proposed some techniques to introduce more diversity and some diversity measurements for neural networks structures to monitor diversity during co-evolution. The genotype diversity evolved were analyzed in terms of its impact to global fitness of the strategies evolved and their generalization. Additionally, it was introduced a memory mechanism in the network neural structures to reinforce some strategies in the genes of the neurons evolved with the intention that some good strategies learned are not forgotten. In this dissertation is presented some works from other authors in which cooperative and competitive co-evolution has been applied. The Go board size used in this thesis was 9x9, but can be easily escalated to more bigger boards.The author believe that programs coded and techniques introduced in this dissertation can be used for other domains

Genetic portrayal of two colobine monkeys inhabiting a continuous forest in Gola Rainforest National Park

Tese de mestrado, Biologia da Conservação, 2022, Universidade de Lisboa, Faculdade de CiênciasWith most of the human population growth occurring in areas of high biodiversity, it is urgent and crucial to understand and assess the impacts of anthropogenic activity on wildlife. This includes the case of West Africa, a region characterized by a highly anthropogenic landscape, yet home to many threatened non-human primates. In this study, the focus is directed to scanning for possible connections between human presence/activity and patterns of dispersal, genetic diversity, demographic history, and genetic and geographic population structure of two sympatric species of colobus monkeys. Fieldwork was conducted in 2018, using a non-invasive sampling method to obtain fecal matter of the arboreal primates, extant in the Gola Rainforest National Park (GRNP), Sierra Leone. A total of 14 microsatellites for 146 samples of Piliocolobus badius badius (Bay colobus) and 15 microsatellites for 25 samples of Colobus polykomos (King colobus) were analyzed. Both colobines presented genetically diverse populations, with overall expectable patterns of sex-biased dispersal. The populations were historically large, having seemingly suffered demographic collapses at different phases of the Holocene epoch, possibly due to bioclimatic changes. Neither species appeared to have a strong genetic substructure, although C. polykomos presented some substructure at the landscape level. Thus, the results of this study suggest that both species seem to be resilient to fairly recent anthropogenic pressures in this protected area. Since these arboreal primates are highly dependent on the forests for habitat, their genetic status in the GRNP reflects the high level of integrity of the protected area. The findings in this study illustrate the importance of maintaining continuous forest habitat to conserve these primates. The former may be used to inform conservation planning of the international Gola landscape, with the involvement of stakeholders

Perspectives and Progress in Contemporary Cross-Cultural Psychology

Selected Papers from the XVIIth International Congress of the International Association for Cross-Cultural Psychology, 2004, Xi’an, Sha’anxi Province, Chinahttps://scholarworks.gvsu.edu/iaccp_proceedings/1005/thumbnail.jp

Invasion Genetics of Botryllid Colonial Ascidians in North America

This thesis analyzes genetic patterns across botryllid tunicate invasions in North America - encompassing the violet tunicate Botrylloides violaceus and the golden-star tunicate Botryllus schlosseri. I investigate these species entry and spread on the continent by using the mitochondrial cytochrome c oxidase subunit I (COI) gene, and 13 (B. violaceus) and 12 (B. schlosseri) nuclear polymorphic microsatellite loci. Considerable genetic differentiation was detected both within and among East and West coast locales. Also, there was substantial variation in the degree of genetic diversity maintained in introduced populations, which showed, in general, signatures of long-distance dispersal. Taken together, these results indicate the invasions were founded from multiple source regions. Also, post-introduction spread along the coasts appears to occur predominantly through human-mediated dispersal of sexually-produced propagules. I relate these findings to knowledge of the life-history attributes of B. schlosseri and B. violaceus, and to available records of their introductions to North America

Modelling Individual Behaviour in European Labour–Education Market System

Elektroniskā versija nesatur pielikumusPromocijas darba mērķis ir izstrādāt indivīda uzvedības tradicionālos ekonometriskos un aģentu modeļus Eiropas darba-izglītības tirgu sistēmā (LEMS), akcentējot pārlieku izglītošanu un sociālo tīklu ietekmi. Tiek pētīti indivīda pārliekas izglītošanas varbūtību ietekmējošie faktori. Ir identificēti un analizēti pārliekas izglītošanas varbūtību ietekmējošie konkrētie indivīda demogrāfijas, personības, imigranta statusa, nozares, darba tirgus vēstures un izglītības jomas (trešajā izglītības līmenī) efekti. Pārliekas izglītošanas laika dinamiku labi izskaidro trīs makrolīmeņa mainīgie: augstskolu absolventu daļa no strādājošajiem, strādājošo profesijās no Starptautiskās standarta profesiju klasifikācijas (ISCO) 1.-3. lielām grupām daļa no visiem strādājošajiem un bezdarba līmenis. Tiek arī pētīti svarīgi darba tirgus un izglītības rezultāti, kurus ietekmē pārlieka izglītošana. Pārlieka izglītošana negatīvi ietekmē indivīda psiholoģisko stāvokli darba tirgū – samazina apmierinātību ar darbu un palielina varbūtību aiziet no esošā darba – kā arī ietekmē motivāciju turpināt studijas doktorantūrā. Tiek izvērtēta aģentu imitācijas modelēšanas pieejas izmantošanas iespēja LEMS modelēšanā. Tiek piedāvāti un analizēti trīs aģentu modeļi. Pirmais inkorporē darba apmierinātību darba tirgus modelī, kurā apmierinātība ar darbu ir atkarīga no monetārās kompensācijas, sociālā atbalsta, darba daudzveidības un karjeras iespējām. Otrais modelis analizē, kā tiek izvēlētas izglītības jomas un ar kādām problēmām saskaras aģenti, kad tie nevar pareizi izvēlēties savu labāko izglītības jomu. Trešais modelis ir saistīts ar lēmumu sākt studijas universitātē un politikas reakciju uz pārlieku izglītošanas problēmu, kur neiejaukšanās (laissez-faire) princips ir salīdzināts ar ierobežotu iespēju sākt studijas augstskolās. Piedāvātā analīze ļauj izvērtēt esošo LEMS politiku darbību un formulēt svarīgas politikas rekomendācijas.The dissertation aims at developing conventional econometric and agent-based models of individual behaviour in European labour–education market system (LEMS) with a focus on overeducation and the influence of social networks. Factors affecting the probability of overeducation of an individual are studied. Specific effects from individual’s demographics, personality, immigrant status, industry, labour market history and field of study (at tertiary education level) on their overeducation probability are identified and discussed. The temporal dynamics of overeducation are well explained by three macro-level variables: the share of tertiary graduates, the share of occupations from the International Standard Classification of Occupations (ISCO) major groups 1 through 3 and unemployment. Important labour-market and educational outcomes influenced by overeducation are also studied. Overeducation is found to have detrimental effects on the psychological state of the individual in the labour market – specifically, on job satisfaction and the propensity to quit the current job – and affect the motivation of continuing studies at doctoral level. The possibility of using agent-based simulations for modelling LEMS is assessed. Three agent-based models are proposed and analysed. The first incorporates job satisfaction into a labour-market model, where job satisfaction is based on monetary benefits, social support, job variety and career opportunities. The second model considers how fields of study are chosen and the problems faced by agents when they are unable to correctly choose their best field. The third is concerned with the decision to enter studies at the university and policy responses to the problem of overeducation, where the laissez-faire principle is compared with restricting access to education market. The analysis presented here allows assessing the performance of current LEMS policies and formulate important policy recommendations