An Adaptive Genetic Algorithm with Dynamic Population Size for Optimizing Join Queries

The problem of finding the optimal join ordering executing a query to a relational database management system is a combinatorial optimization problem, which makes deterministic exhaustive solution search unacceptable for queries with a great number of joined relations. In this work an adaptive genetic algorithm with dynamic population size is proposed for optimizing large join queries. The performance of the algorithm is compared with that of several classical non-deterministic optimization algorithms. Experiments have been performed optimizing several random queries against a randomly generated data dictionary. The proposed adaptive genetic algorithm with probabilistic selection operator outperforms in a number of test runs the canonical genetic algorithm with Elitist selection as well as two common random search strategies and proves to be a viable alternative to existing non-deterministic optimization approaches

Computational intelligence techniques for maximum energy efficiency of cogeneration processes based on internal combustion engines

153 p.El objeto de la tesis consiste en desarrollar estrategias de modelado y optimización del rendimiento energético de plantas de cogeneración basadas en motores de combustión interna (MCI), mediante el uso de las últimas tecnologías de inteligencia computacional. Con esta finalidad se cuenta con datos reales de una planta de cogeneración de energía, propiedad de la compañía EnergyWorks, situada en la localidad de Monzón (provincia de Huesca). La tesis se realiza en el marco de trabajo conjunto del Grupo de Diseño en Electrónica Digital (GDED) de la Universidad del País Vasco UPV/EHU y la empresa Optimitive S.L., empresa dedicada al software avanzado para la mejora en tiempo real de procesos industriale

Distributed classifier based on genetically engineered bacterial cell cultures

We describe a conceptual design of a distributed classifier formed by a population of genetically engineered microbial cells. The central idea is to create a complex classifier from a population of weak or simple classifiers. We create a master population of cells with randomized synthetic biosensor circuits that have a broad range of sensitivities towards chemical signals of interest that form the input vectors subject to classification. The randomized sensitivities are achieved by constructing a library of synthetic gene circuits with randomized control sequences (e.g. ribosome-binding sites) in the front element. The training procedure consists in re-shaping of the master population in such a way that it collectively responds to the "positive" patterns of input signals by producing above-threshold output (e.g. fluorescent signal), and below-threshold output in case of the "negative" patterns. The population re-shaping is achieved by presenting sequential examples and pruning the population using either graded selection/counterselection or by fluorescence-activated cell sorting (FACS). We demonstrate the feasibility of experimental implementation of such system computationally using a realistic model of the synthetic sensing gene circuits.Comment: 31 pages, 9 figure

Biological Organisms as Semiosic Systems: the importance of strong and weak anticipation

The biological realm is examined as a semiosic system that transforms basic matter into a complex and intimately networked diversity of morphological forms according to generic sets of self‐generated rules of formation. Semiosis is understood to operate as a function f(x)=y where the mediative rules of formation, f, operate within predictive or anticipatory capacities. Strong and weak anticipation are examined and the paper concludes that strong anticipation, operating as a virtual or imaginary hypothesis construction is a basic property of the biological realm. Strong anticipation enables the biological species to develop multiple hypothetical ‘network motifs’ about its future activities within the environment. The species will ‘choose’ one of these probabilities – any of which would be functional – to articulate in actual time and space. This theory rejects random mutation as the source of innovative evolution and adaptation. Weak anticipation is defined as Natural Selection and is described as a post hoc model of strong anticipation’s ‘selected solution’

Simulation Intelligence: Towards a New Generation of Scientific Methods

The original "Seven Motifs" set forth a roadmap of essential methods for the field of scientific computing, where a motif is an algorithmic method that captures a pattern of computation and data movement. We present the "Nine Motifs of Simulation Intelligence", a roadmap for the development and integration of the essential algorithms necessary for a merger of scientific computing, scientific simulation, and artificial intelligence. We call this merger simulation intelligence (SI), for short. We argue the motifs of simulation intelligence are interconnected and interdependent, much like the components within the layers of an operating system. Using this metaphor, we explore the nature of each layer of the simulation intelligence operating system stack (SI-stack) and the motifs therein: (1) Multi-physics and multi-scale modeling; (2) Surrogate modeling and emulation; (3) Simulation-based inference; (4) Causal modeling and inference; (5) Agent-based modeling; (6) Probabilistic programming; (7) Differentiable programming; (8) Open-ended optimization; (9) Machine programming. We believe coordinated efforts between motifs offers immense opportunity to accelerate scientific discovery, from solving inverse problems in synthetic biology and climate science, to directing nuclear energy experiments and predicting emergent behavior in socioeconomic settings. We elaborate on each layer of the SI-stack, detailing the state-of-art methods, presenting examples to highlight challenges and opportunities, and advocating for specific ways to advance the motifs and the synergies from their combinations. Advancing and integrating these technologies can enable a robust and efficient hypothesis-simulation-analysis type of scientific method, which we introduce with several use-cases for human-machine teaming and automated science

Evolutionary strategies in swarm robotics controllers

Nowadays, Unmanned Vehicles (UV) are widespread around the world. Most of these vehicles require a great level of human control, and mission success is reliant on this dependency. Therefore, it is important to use machine learning techniques that will train the robotic controllers to automate the control, making the process more efficient. Evolutionary strategies may be the key to having robust and adaptive learning in robotic systems. Many studies involving UV systems and evolutionary strategies have been conducted in the last years, however, there are still research gaps that need to be addressed, such as the reality gap. The reality gap occurs when controllers trained in simulated environments fail to be transferred to real robots. This work proposes an approach for solving robotic tasks using realistic simulation and using evolutionary strategies to train controllers. The chosen setup is easily scalable for multirobot systems or swarm robots. In this thesis, the simulation architecture and setup are presented, including the drone simulation model and software. The drone model chosen for the simulations is available in the real world and widely used, such as the software and flight control unit. This relevant factor makes the transition to reality smoother and easier. Controllers using behavior trees were evolved using a developed evolutionary algorithm, and several experiments were conducted. Results demonstrated that it is possible to evolve a robotic controller in realistic simulation environments, using a simulated drone model that exists in the real world, and also the same flight control unit and operating system that is generally used in real world experiments.Atualmente os Veículos Não Tripulados (VNT) encontram-se difundidos por todo o Mundo. A maioria destes veículos requerem um elevado controlo humano, e o sucesso das missões está diretamente dependente deste fator. Assim, é importante utilizar técnicas de aprendizagem automática que irão treinar os controladores dos VNT, de modo a automatizar o controlo, tornando o processo mais eficiente. As estratégias evolutivas podem ser a chave para uma aprendizagem robusta e adaptativa em sistemas robóticos. Vários estudos têm sido realizados nos últimos anos, contudo, existem lacunas que precisam de ser abordadas, tais como o reality gap. Este facto ocorre quando os controladores treinados em ambientes simulados falham ao serem transferidos para VNT reais. Este trabalho propõe uma abordagem para a resolução de missões com VNT, utilizando um simulador realista e estratégias evolutivas para treinar controladores. A arquitetura escolhida é facilmente escalável para sistemas com múltiplos VNT. Nesta tese, é apresentada a arquitetura e configuração do ambiente de simulação, incluindo o modelo e software de simulação do VNT. O modelo de VNT escolhido para as simulações é um modelo real e amplamente utilizado, assim como o software e a unidade de controlo de voo. Este fator é relevante e torna a transição para a realidade mais suave. É desenvolvido um algoritmo evolucionário para treinar um controlador, que utiliza behavior trees, e realizados diversos testes. Os resultados demonstram que é possível evoluir um controlador em ambientes de simulação realistas, utilizando um VNT simulado mas real, assim como utilizando as mesmas unidades de controlo de voo e software que são amplamente utilizados em ambiente real