A Hybrid of Ant Colony Optimization Algorithm and Simulated Annealing for Classification Rules

Ant colony optimization (ACO) is a metaheuristic approach inspired from the behaviour of natural ants and can be used to solve a variety of combinatorial optimization problems. Classification rule induction is one of the problems solved by the Ant-miner algorithm, a variant of ACO, which was initiated by Parpinelli in 2001. Previous studies have shown that ACO is a promising machine learning technique to generate classification rules. However, the Ant-miner is less class focused since the rule’s class is assigned after the rule was constructed. There is also the case where the Ant-miner cannot find any optimal solution for some data sets. Thus, this thesis proposed two variants of hybrid ACO with simulated annealing (SA) algorithm for solving problem of classification rule induction. In the first proposed algorithm, SA is used to optimize the rule's discovery activity by an ant. Benchmark data sets from various fields were used to test the proposed algorithms. Experimental results obtained from this proposed algorithm are comparable to the results of the Ant-miner and other well-known rule induction algorithms in terms of rule accuracy, but are better in terms of rule simplicity. The second proposed algorithm uses SA to optimize the terms selection while constructing a rule. The algorithm fixes the class before rule's construction. Since the algorithm fixed the class before each rule's construction, a much simpler heuristic and fitness function is proposed. Experimental results obtained from the proposed algorithm are much higher than other compared algorithms, in terms of predictive accuracy. The successful work on hybridization of ACO and SA algorithms has led to the improved learning ability of ACO for classification. Thus, a higher predictive power classification model for various fields could be generated

Machine learning for microbial ecology: predicting interactions and identifying their putative mechanisms

Microbial communities are key components of Earth’s ecosystems and they play important roles in human health and industrial processes. These communities and their functions can strongly depend on the diverse interactions between constituent species, posing the question of how such interactions can be predicted, measured and controlled. This challenge is particularly relevant for the many practical applications enabled by the rising field of synthetic microbial ecology, which includes the design of microbiome therapies for human diseases. Advances in sequencing technologies and genomic databases provide valuable datasets and tools for studying inter-microbial interactions, but the capacity to characterize the strength and mechanisms of interactions between species in large consortia is still an unsolved challenge. In this thesis, I show how machine learning methods can be used to help address these questions. The first portion of my thesis work was focused on predicting the outcome of pairwise interactions between microbial species. By integrating genomic information and observed experimental data, I used machine learning algorithms to explore the predictive relationship between single-species traits and inter-species interaction phenotypes. I found that organismal traits (e.g. annotated functions of genomic elements) are sufficient to predict the qualitative outcome of interactions between microbes. I also found that the relative fraction of possible experiments needed to build acceptable models drastically shrinks as the combinatorial space grows. In the second part of my thesis work, I developed an algorithmic method for identifying putative interaction mechanisms by scoring combinations of variables that random forest uses in order to predict interaction outcomes. I applied this method to a study of the human microbiome and identified a previously unreported combination of microbes that are strongly associated with Crohn’s disease. In the last part of my thesis, I utilized a regression approach to first identify and then quantify interactions between microbial species relevant to community function. The work I present in this dissertation provides a general framework for understanding the myriad interactions that occur in natural and synthetic microbial consortia

El mundo de las ciencias de la complejidad

La situación es verdaderamente apasionante. Mientras que en el mundo llamado real –y entonces se hace referencia a dominios como la política, la economía, los conflictos militares y sociales, por ejemplo–, la percepción natural –digamos: de los medios y la opinión pública– es que el país y el mundo se encuentran en condiciones difíciles; en algunos casos, dramática; y en muchas ocasiones trágica, en el campo del progreso del conocimiento asistimos a una magnífica vitalidad. Esta vitalidad se expresa en la ciencia de punta y, notablemente, en las ciencias de la complejidad. Mientras que la ciencia normal –para volver a la expresión de Kuhn– se encuentra literalmente a la defensiva en numerosos campos, temas y problemas –digamos, a la defensiva con respecto al decurso de los acontecimientos y a las dinámicas del mundo contemporáneo–, en el contexto del estudio de los sistemas complejos adaptativos asistimos a una vitalidad que es prácticamente desconocida para la corriente principal de académicos –independientemente de los niveles en los que trabajan–, de científicos, de administradores de educación y de ciencia y tecnología (por ejemplo rectores, vicerrectores, decanos, directores de departamentos, tomadores de decisión, políticos y gobernantes). La corriente principal del conocimiento (mainstream) desconoce una circunstancia, un proceso, una dinámica que sí es conocida por parte de quienes trabajan e investigan activamente en el campo de las ciencias de la complejidad

The 1988 Goddard Conference on Space Applications of Artificial Intelligence

This publication comprises the papers presented at the 1988 Goddard Conference on Space Applications of Artificial Intelligence held at the NASA/Goddard Space Flight Center, Greenbelt, Maryland on May 24, 1988. The purpose of this annual conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed. The papers in these proceedings fall into the following areas: mission operations support, planning and scheduling; fault isolation/diagnosis; image processing and machine vision; data management; modeling and simulation; and development tools/methodologies