Hybrid Genetic Relational Search for Inductive Learning

An important characteristic of all natural systems is the ability to acquire knowledge through experience and to adapt to new situations. Learning is the single unifying theme of all natural systems. One of the basic ways of gaining knowledge is through examples of some concepts.For instance, we may learn how to distinguish a dog from other creatures after that we have seen a number of creatures, and after that someone (a teacher, or supervisor) told us which creatures are dogs and which are not. This way of learning is called supervised learning. Inductive Concept Learning (ICL) constitutes a central topic in machine learning. The problem can be formulated in the following manner: given a description language used to express possible hypotheses, a background knowledge, a set of positive examples, and a set of negative examples, one has to find a hypothesis which covers all positive examples and none of the negative ones. This is a supervised way of learning, since a supervisor has already classified the examples of the concept into positive and negative examples. The so learned concept can be used to classify previously unseen examples. In general deriving general conclusions from specific observation is called induction. Thus in ICL, concepts are induced because obtained from the observation of a limited set of training examples. The process can be seen as a search process. Starting from an initial hypothesis, what is done is searching the space of the possible hypotheses for one that fits the given set of examples. A representation language has to be chosen in order to represent concepts, examples and the background knowledge. This is an important choice, because this may limit the kind of concept we can learn. With a representation language that has a low expressive power we may not be able to represent some problem domain, because too complex for the language adopted. On the other side, a too expressive language may give us the possibility to represent all problem domains. However this solution may also give us too much freedom, in the sense that we can build concepts in too many different ways, and this could lead to the impossibility of finding the right concept. We are interested in learning concepts expressed in a fragment of first--order logic (FOL). This subject is known as Inductive Logic Programming (ILP), where the knowledge to be learn is expressed by Horn clauses, which are used in programming languages based on logic programming like Prolog. Learning systems that use a representation based on first--order logic have been successfully applied to relevant real life problems, e.g., learning a specific property related to carcinogenicity. Learning first--order hypotheses is a hard task, due to the huge search space one has to deal with. The approach used by the majority of ILP systems tries to overcome this problem by using specific search strategies, like the top-down and the inverse resolution mechanism. However, the greedy selection strategies adopted for reducing the computational effort, render techniques based on this approach often incapable of escaping from local optima. An alternative approach is offered by genetic algorithms (GAs). GAs have proved to be successful in solving comparatively hard optimization problems, as well as problems like ICL. GAs represents a good approach when the problems to solve are characterized by a high number of variables, when there is interaction among variables, when there are mixed types of variables, e.g., numerical and nominal, and when the search space presents many local optima. Moreover it is easy to hybridize GAs with other techniques that are known to be good for solving some classes of problems. Another appealing feature of GAs is represented by their intrinsic parallelism, and their use of exploration operators, which give them the possibility of escaping from local optima. However this latter characteristic of GAs is also responsible for their rather poor performance on learning tasks which are easy to tackle by algorithms that use specific search strategies. These observations suggest that the two approaches above described, i.e., standard ILP strategies and GAs, are applicable to partly complementary classes of learning problems. More important, they indicate that a system incorporating features from both approaches could profit from the different benefits of the approaches. This motivates the aim of this thesis, which is to develop a system based on GAs for ILP that incorporates search strategies used in successful ILP systems. Our approach is inspired by memetic algorithms, a population based search method for combinatorial optimization problems. In evolutionary computation memetic algorithms are GAs in which individuals can be refined during their lifetime.Eiben, A.E. [Promotor]Marchiori, E. [Copromotor

Carbon-profit-aware job scheduling and load balancing in geographically distributed cloud for HPC and web applications

This thesis introduces two carbon-profit-aware control mechanisms that can be used to improve performance of job scheduling and load balancing in an interconnected system of geographically distributed data centers for HPC and web applications. These control mechanisms consist of three primary components that perform: 1) measurement and modeling, 2) job planning, and 3) plan execution. The measurement and modeling component provide information on energy consumption and carbon footprint as well as utilization, weather, and pricing information. The job planning component uses this information to suggest the best arrangement of applications as a possible configuration to the plan execution component to perform it on the system. For reporting and decision making purposes, some metrics need to be modeled based on directly measured inputs. There are two challenges in accurately modeling of these necessary metrics: 1) feature selection and 2) curve fitting (regression). First, to improve the accuracy of power consumption models of the underutilized servers, advanced fitting methodologies were used on the selected server features. The resulting model is then evaluated on real servers and is used as part of load balancing mechanism for web applications. We also provide an inclusive model for cooling system in data centers to optimize the power consumption of cooling system, which in turn is used by the planning component. Furthermore, we introduce another model to calculate the profit of the system based on the price of electricity, carbon tax, operational costs, sales tax, and corporation taxes. This model is used for optimized scheduling of HPC jobs. For position allocation of web applications, a new heuristic algorithm is introduced for load balancing of virtual machines in a geographically distributed system in order to improve its carbon awareness. This new heuristic algorithm is based on genetic algorithm and is specifically tailored for optimization problems of interconnected system of distributed data centers. A simple version of this heuristic algorithm has been implemented in the GSN project, as a carbon-aware controller. Similarly, for scheduling of HPC jobs on servers, two new metrics are introduced: 1) profitper-core-hour-GHz and 2) virtual carbon tax. In the HPC job scheduler, these new metrics are used to maximize profit and minimize the carbon footprint of the system, respectively. Once the application execution plan is determined, plan execution component will attempt to implement it on the system. Plan execution component immediately uses the hypervisors on physical servers to create, remove, and migrate virtual machines. It also executes and controls the HPC jobs or web applications on the virtual machines. For validating systems designed using the proposed modeling and planning components, a simulation platform using real system data was developed, and new methodologies were compared with the state-of-the-art methods considering various scenarios. The experimental results show improvement in power modeling of servers, significant carbon reduction in load balancing of web applications, and significant profit-carbon improvement in HPC job scheduling

USING COEVOLUTION IN COMPLEX DOMAINS

Genetic Algorithms is a computational model inspired by Darwin's theory of evolution. It has a broad range of applications from function optimization to solving robotic control problems. Coevolution is an extension of Genetic Algorithms in which more than one population is evolved at the same time. Coevolution can be done in two ways: cooperatively, in which populations jointly try to solve an evolutionary problem, or competitively. Coevolution has been shown to be useful in solving many problems, yet its application in complex domains still needs to be demonstrated.Robotic soccer is a complex domain that has a dynamic and noisy environment. Many Reinforcement Learning techniques have been applied to the robotic soccer domain, since it is a great test bed for many machine learning methods. However, the success of Reinforcement Learning methods has been limited due to the huge state space of the domain. Evolutionary Algorithms have also been used to tackle this domain; nevertheless, their application has been limited to a small subset of the domain, and no attempt has been shown to be successful in acting on solving the whole problem.This thesis will try to answer the question of whether coevolution can be applied successfully to complex domains. Three techniques are introduced to tackle the robotic soccer problem. First, an incremental learning algorithm is used to achieve a desirable performance of some soccer tasks. Second, a hierarchical coevolution paradigm is introduced to allow coevolution to scale up in solving the problem. Third, an orchestration mechanism is utilized to manage the learning processes

Medical data mining using evolutionary computation.

by Ngan Po Shun.Thesis (M.Phil.)--Chinese University of Hong Kong, 1998.Includes bibliographical references (leaves 109-115).Abstract also in Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Data Mining --- p.1Chapter 1.2 --- Motivation --- p.4Chapter 1.3 --- Contributions of the research --- p.5Chapter 1.4 --- Organization of the thesis --- p.6Chapter 2 --- Related Work in Data Mining --- p.9Chapter 2.1 --- Decision Tree Approach --- p.9Chapter 2.1.1 --- ID3 --- p.10Chapter 2.1.2 --- C4.5 --- p.11Chapter 2.2 --- Classification Rule Learning --- p.13Chapter 2.2.1 --- AQ algorithm --- p.13Chapter 2.2.2 --- CN2 --- p.14Chapter 2.2.3 --- C4.5RULES --- p.16Chapter 2.3 --- Association Rule Mining --- p.16Chapter 2.3.1 --- Apriori --- p.17Chapter 2.3.2 --- Quantitative Association Rule Mining --- p.18Chapter 2.4 --- Statistical Approach --- p.19Chapter 2.4.1 --- Chi Square Test and Bayesian Classifier --- p.19Chapter 2.4.2 --- FORTY-NINER --- p.21Chapter 2.4.3 --- EXPLORA --- p.22Chapter 2.5 --- Bayesian Network Learning --- p.23Chapter 2.5.1 --- Learning Bayesian Networks using the Minimum Descrip- tion Length (MDL) Principle --- p.24Chapter 2.5.2 --- Discretizating Continuous Attributes while Learning Bayesian Networks --- p.26Chapter 3 --- Overview of Evolutionary Computation --- p.29Chapter 3.1 --- Evolutionary Computation --- p.29Chapter 3.1.1 --- Genetic Algorithm --- p.30Chapter 3.1.2 --- Genetic Programming --- p.32Chapter 3.1.3 --- Evolutionary Programming --- p.34Chapter 3.1.4 --- Evolution Strategy --- p.37Chapter 3.1.5 --- Selection Methods --- p.38Chapter 3.2 --- Generic Genetic Programming --- p.39Chapter 3.3 --- Data mining using Evolutionary Computation --- p.43Chapter 4 --- Applying Generic Genetic Programming for Rule Learning --- p.45Chapter 4.1 --- Grammar --- p.46Chapter 4.2 --- Population Creation --- p.49Chapter 4.3 --- Genetic Operators --- p.50Chapter 4.4 --- Evaluation of Rules --- p.52Chapter 5 --- Learning Multiple Rules from Data --- p.56Chapter 5.1 --- Previous approaches --- p.57Chapter 5.1.1 --- Preselection --- p.57Chapter 5.1.2 --- Crowding --- p.57Chapter 5.1.3 --- Deterministic Crowding --- p.58Chapter 5.1.4 --- Fitness sharing --- p.58Chapter 5.2 --- Token Competition --- p.59Chapter 5.3 --- The Complete Rule Learning Approach --- p.61Chapter 5.4 --- Experiments with Machine Learning Databases --- p.64Chapter 5.4.1 --- Experimental results on the Iris Plant Database --- p.65Chapter 5.4.2 --- Experimental results on the Monk Database --- p.67Chapter 6 --- Bayesian Network Learning --- p.72Chapter 6.1 --- The MDLEP Learning Approach --- p.73Chapter 6.2 --- Learning of Discretization Policy by Genetic Algorithm --- p.74Chapter 6.2.1 --- Individual Representation --- p.76Chapter 6.2.2 --- Genetic Operators --- p.78Chapter 6.3 --- Experimental Results --- p.79Chapter 6.3.1 --- Experiment 1 --- p.80Chapter 6.3.2 --- Experiment 2 --- p.82Chapter 6.3.3 --- Experiment 3 --- p.83Chapter 6.3.4 --- Comparison between the GA approach and the greedy ap- proach --- p.91Chapter 7 --- Medical Data Mining System --- p.93Chapter 7.1 --- A Case Study on the Fracture Database --- p.95Chapter 7.1.1 --- Results of Causality and Structure Analysis --- p.95Chapter 7.1.2 --- Results of Rule Learning --- p.97Chapter 7.2 --- A Case Study on the Scoliosis Database --- p.100Chapter 7.2.1 --- Results of Causality and Structure Analysis --- p.100Chapter 7.2.2 --- Results of Rule Learning --- p.102Chapter 8 --- Conclusion and Future Work --- p.106Bibliography --- p.109Chapter A --- The Rule Sets Discovered --- p.116Chapter A.1 --- The Best Rule Set Learned from the Iris Database --- p.116Chapter A.2 --- The Best Rule Set Learned from the Monk Database --- p.116Chapter A.2.1 --- Monkl --- p.116Chapter A.2.2 --- Monk2 --- p.117Chapter A.2.3 --- Monk3 --- p.119Chapter A.3 --- The Best Rule Set Learned from the Fracture Database --- p.120Chapter A.3.1 --- Type I Rules: About Diagnosis --- p.120Chapter A.3.2 --- Type II Rules : About Operation/Surgeon --- p.120Chapter A.3.3 --- Type III Rules : About Stay --- p.122Chapter A.4 --- The Best Rule Set Learned from the Scoliosis Database --- p.123Chapter A.4.1 --- Rules for Classification --- p.123Chapter A.4.2 --- Rules for Treatment --- p.126Chapter B --- The Grammar used for the fracture and Scoliosis databases --- p.128Chapter B.1 --- The grammar for the fracture database --- p.128Chapter B.2 --- The grammar for the Scoliosis database --- p.12

Human Enhancement Technologies and Our Merger with Machines

A cross-disciplinary approach is offered to consider the challenge of emerging technologies designed to enhance human bodies and minds. Perspectives from philosophy, ethics, law, and policy are applied to a wide variety of enhancements, including integration of technology within human bodies, as well as genetic, biological, and pharmacological modifications. Humans may be permanently or temporarily enhanced with artificial parts by manipulating (or reprogramming) human DNA and through other enhancement techniques (and combinations thereof). We are on the cusp of significantly modifying (and perhaps improving) the human ecosystem. This evolution necessitates a continuing effort to re-evaluate current laws and, if appropriate, to modify such laws or develop new laws that address enhancement technology. A legal, ethical, and policy response to current and future human enhancements should strive to protect the rights of all involved and to recognize the responsibilities of humans to other conscious and living beings, regardless of what they look like or what abilities they have (or lack). A potential ethical approach is outlined in which rights and responsibilities should be respected even if enhanced humans are perceived by non-enhanced (or less-enhanced) humans as “no longer human” at all

Evolutionary ethics without the error : how care ethics can vindicate moral realism

In this thesis I defend a form of moral realism against Richard Joyce's evolutionary argument for an error theory. I explain how evolutionary data can be used to explain human behaviour, ultimately endorsing a developmental systems perspective on the evolution of traits. I argue that evolutionary theories of ethics, developmentally conceived, are best demarcated from non-evolutionary ethical theories by appealing to the distinction between moral philosophy and moral psychology. I then set out Joyce's argument for an error theory, and in so doing respond to his claim that moral properties cannot be successfully naturalised. I then consider different naturalistic approaches to moral realism, assessing whether these approaches successfully meet Joyce's sceptical challenge. I look first at Philippa Foot's neo-Aristotelian approach to virtue ethics, arguing that her position fails because of her commitment to eudaimonism, and to a welfarist conception of function. I then consider Jesse Prinz's realist sentimentalism. This too, I argue, fails to constitute a convincing reply to Joyce, owing to internal inconsistencies, and to the failure of Prinz's theory to meet certain criteria intuitively constitutive of moral realism. Finally, I argue that a successful realist response to Joyce can be made by developing an evolutionary account of care ethics. I begin to develop such an account in the final chapter of the thesis, showing how the theory which I sketch meets each of the aspects of Joyce's argument for an error theory

2014 GREAT Day Program

SUNY Geneseo’s Eighth Annual GREAT Day.https://knightscholar.geneseo.edu/program-2007/1008/thumbnail.jp

Suffolk University Undergraduate Academic Catalog, College of Arts and Sciences, 2016-2017

This catalog contains information for the undergraduate programs in the College of Arts and Sciences. The catalog is a PDF version of the Suffolk website, so many pages have repeated information and links in the document will not work. The catalog is keyword searchable by clicking ctrl+f. A-Z course descriptions are also included here as a separate PDF file listing all CAS course offerings. Please contact the Archives if you need assistance navigating this catalog or finding information on degree requirements or course descriptions.https://dc.suffolk.edu/cassbs-catalogs/1172/thumbnail.jp

Non-universal suffrage selection operators favor population diversity in genetic algorithms

Contains fulltext : 84523.pdf (postprint version ) (Open Access)Genetic and Evolutionary Computation - Gecco 2003, Pt I