Numerical and Evolutionary Optimization 2020

This book was established after the 8th International Workshop on Numerical and Evolutionary Optimization (NEO), representing a collection of papers on the intersection of the two research areas covered at this workshop: numerical optimization and evolutionary search techniques. While focusing on the design of fast and reliable methods lying across these two paradigms, the resulting techniques are strongly applicable to a broad class of real-world problems, such as pattern recognition, routing, energy, lines of production, prediction, and modeling, among others. This volume is intended to serve as a useful reference for mathematicians, engineers, and computer scientists to explore current issues and solutions emerging from these mathematical and computational methods and their applications

Computational Tradespace Exploration, Analysis, and Decision-Making: A Proposed Framework for Organizational Self-Assessment

The ability to assess technical feasibility, project risk, technical readiness, and realistic performance expectations in early-phase conceptual design is a challenging mission-critical task for large procurement projects. At present, there is not a well-defined framework for evaluating current practices of organizations performing computational trade studies. One such organization is the US Army Ground Vehicle Systems Center (GVSC). When defining requirements and priorities for the next-generation autonomy-enabled ground vehicle system, GVSC is faced with the challenge of an increasingly complex programmatic tradespace due to emerging complexities of ground vehicle systems. This thesis aims to document and evaluate tradespace processes, methods, and tools within GVSC. A systematic review of the literature was conducted to investigate existing gaps, limitations, and potential growth opportunities related to tradespace activities reflecting the greater body of knowledge observed in the literature. Following this review, an interview-based study was developed through which a series of interviews with GVSC personnel was conducted and subsequently benchmarked against the baseline established in the literature. In addition to characterizing the current practices of tradespace exploration and analysis within GVSC, the analysis of the collected interview data revealed current capability gaps, areas of excellence, and potential avenues for improvement within GVSC. Through this thesis, other organizations can perform similar self-assessments to improve internal capabilities with respect to tradespace studies

Machine Learning and Portfolio Optimization: an application to Italian FTSE-MIB Stocks

A model that combines econometric ARMA model with new machine learning techniques will be developed to build an efficient portfolio, composed of Italian FTSE-MIB stocks. The goal of this portfolio is to over-perform a benchmark portfolio obtained throw traditional Markowitz optimisation.A model that combines econometric ARMA model with new machine learning techniques will be developed to build an efficient portfolio, composed of Italian FTSE-MIB stocks. The goal of this portfolio is to over-perform a benchmark portfolio obtained throw traditional Markowitz optimisation

Antecipação na tomada de decisão com múltiplos critérios sob incerteza

Orientador: Fernando José Von ZubenTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: A presença de incerteza em resultados futuros pode levar a indecisões em processos de escolha, especialmente ao elicitar as importâncias relativas de múltiplos critérios de decisão e de desempenhos de curto vs. longo prazo. Algumas decisões, no entanto, devem ser tomadas sob informação incompleta, o que pode resultar em ações precipitadas com consequências imprevisíveis. Quando uma solução deve ser selecionada sob vários pontos de vista conflitantes para operar em ambientes ruidosos e variantes no tempo, implementar alternativas provisórias flexíveis pode ser fundamental para contornar a falta de informação completa, mantendo opções futuras em aberto. A engenharia antecipatória pode então ser considerada como a estratégia de conceber soluções flexíveis as quais permitem aos tomadores de decisão responder de forma robusta a cenários imprevisíveis. Essa estratégia pode, assim, mitigar os riscos de, sem intenção, se comprometer fortemente a alternativas incertas, ao mesmo tempo em que aumenta a adaptabilidade às mudanças futuras. Nesta tese, os papéis da antecipação e da flexibilidade na automação de processos de tomada de decisão sequencial com múltiplos critérios sob incerteza é investigado. O dilema de atribuir importâncias relativas aos critérios de decisão e a recompensas imediatas sob informação incompleta é então tratado pela antecipação autônoma de decisões flexíveis capazes de preservar ao máximo a diversidade de escolhas futuras. Uma metodologia de aprendizagem antecipatória on-line é então proposta para melhorar a variedade e qualidade dos conjuntos futuros de soluções de trade-off. Esse objetivo é alcançado por meio da previsão de conjuntos de máximo hipervolume esperado, para a qual as capacidades de antecipação de metaheurísticas multi-objetivo são incrementadas com rastreamento bayesiano em ambos os espaços de busca e dos objetivos. A metodologia foi aplicada para a obtenção de decisões de investimento, as quais levaram a melhoras significativas do hipervolume futuro de conjuntos de carteiras financeiras de trade-off avaliadas com dados de ações fora da amostra de treino, quando comparada a uma estratégia míope. Além disso, a tomada de decisões flexíveis para o rebalanceamento de carteiras foi confirmada como uma estratégia significativamente melhor do que a de escolher aleatoriamente uma decisão de investimento a partir da fronteira estocástica eficiente evoluída, em todos os mercados artificiais e reais testados. Finalmente, os resultados sugerem que a antecipação de opções flexíveis levou a composições de carteiras que se mostraram significativamente correlacionadas com as melhorias observadas no hipervolume futuro esperado, avaliado com dados fora das amostras de treinoAbstract: The presence of uncertainty in future outcomes can lead to indecision in choice processes, especially when eliciting the relative importances of multiple decision criteria and of long-term vs. near-term performance. Some decisions, however, must be taken under incomplete information, what may result in precipitated actions with unforeseen consequences. When a solution must be selected under multiple conflicting views for operating in time-varying and noisy environments, implementing flexible provisional alternatives can be critical to circumvent the lack of complete information by keeping future options open. Anticipatory engineering can be then regarded as the strategy of designing flexible solutions that enable decision makers to respond robustly to unpredictable scenarios. This strategy can thus mitigate the risks of strong unintended commitments to uncertain alternatives, while increasing adaptability to future changes. In this thesis, the roles of anticipation and of flexibility on automating sequential multiple criteria decision-making processes under uncertainty are investigated. The dilemma of assigning relative importances to decision criteria and to immediate rewards under incomplete information is then handled by autonomously anticipating flexible decisions predicted to maximally preserve diversity of future choices. An online anticipatory learning methodology is then proposed for improving the range and quality of future trade-off solution sets. This goal is achieved by predicting maximal expected hypervolume sets, for which the anticipation capabilities of multi-objective metaheuristics are augmented with Bayesian tracking in both the objective and search spaces. The methodology has been applied for obtaining investment decisions that are shown to significantly improve the future hypervolume of trade-off financial portfolios for out-of-sample stock data, when compared to a myopic strategy. Moreover, implementing flexible portfolio rebalancing decisions was confirmed as a significantly better strategy than to randomly choosing an investment decision from the evolved stochastic efficient frontier in all tested artificial and real-world markets. Finally, the results suggest that anticipating flexible choices has lead to portfolio compositions that are significantly correlated with the observed improvements in out-of-sample future expected hypervolumeDoutoradoEngenharia de ComputaçãoDoutor em Engenharia Elétric

Integrated resource planning for urban waste management

© 2015 by the authors, licensee MDPI, Basel, Switzerland. The waste hierarchy currently dominates waste management planning in Australia. It is effective in helping planners consider options from waste avoidance or "reduction" through to providing infrastructure for landfill or other "disposal". However, it is inadequate for guiding context-specific decisions regarding sustainable waste management and resource recovery, including the ability for stakeholders to compare a range of options on an equal footing whilst considering their various sustainability impacts and trade-offs. This paper outlines the potential use of Integrated Resource Planning (IRP) as a decision-making approach for the urban waste sector, illustrated using an Australian case study. IRP is well established in both the water and energy sectors in Australia and internationally. It has been used in long-term planning enabling decision-makers to consider the potential to reduce resource use through efficiency alongside options for new infrastructure. Its use in the waste sector could address a number of the current limitations experienced by providing a broader context-sensitive, adaptive, and stakeholder focused approach to planning not present in the waste hierarchy and commonly used cost benefit analysis. For both efficiency and new infrastructure options IRP could be useful in assisting governments to make decisions that are consistent with agreed objectives while addressing costs of alternative options and uncertainty regarding their environmental and social impacts. This paper highlights various international waste planning approaches, differences between the sectors where IRP has been used and gives a worked example of how IRP could be applied in the Australian urban waste sector

Developing Advanced Academic Degree Educational Profiles for Career Fields

This research develops a career field\u27s educational profile through use of the decision analysis approach of value-focused thinking (VFT). VFT is used to solicit the capabilities that the career field manager (CFM) desires its officers obtain from an advanced academic program. This process generates a value hierarchy and a focused set of alternatives (degree programs). The academic programs\u27 content are evaluated against the hierarchy to determine how well it meets the values of the functional area. A rankordered list of degrees is produced and a portfolio of degrees is selected through the use of CFM-approved goal-setting criteria. The specific breakdown of the portfolio into percentages of the force that should attain each degree is then determined through the CFM\u27s specified relative value increments of the goal-setting criteria. The resulting effort creates a guide for CFMs to communicate to their corps on the types of degrees to earn and provides justification for fully-funded degree slots