Riolo Rick - Genetic programming theory and practice III
Here you can read online Riolo Rick - Genetic programming theory and practice III full text of the book (entire story) in english for free. Download pdf and epub, get meaning, cover and reviews about this ebook. City: New York;NY, year: 2006, publisher: Springer, genre: Home and family. Description of the work, (preface) as well as reviews are available. Best literature library LitArk.com created for fans of good reading and offers a wide selection of genres:
Romance novel
Science fiction
Adventure
Detective
Science
History
Home and family
Prose
Art
Politics
Computer
Non-fiction
Religion
Business
Children
Humor
Choose a favorite category and find really read worthwhile books. Enjoy immersion in the world of imagination, feel the emotions of the characters or learn something new for yourself, make an fascinating discovery.
- Book:Genetic programming theory and practice III
- Author:
- Publisher:Springer
- Genre:
- Year:2006
- City:New York;NY
- Rating:5 / 5
- Favourites:Add to favourites
- Your mark:
Genetic programming theory and practice III: summary, description and annotation
We offer to read an annotation, description, summary or preface (depends on what the author of the book "Genetic programming theory and practice III" wrote himself). If you haven't found the necessary information about the book — write in the comments, we will try to find it.
Genetic Programming: Theory and Practice -- Evolving Swarming Agents in Real Time -- Automated Design of a Previously Patented Aspherical Optical Lens System by Means of Genetic Programming -- Discrimination of Unexploded Ordnance from Clutter Using Linear Genetic Programming -- Rapid Re-Evolution of an X-Band Antenna for Nasas Space Technology 5 Mission -- Variable Selection in Industrial Datasets Using Pareto Genetic Programming -- A Higher-Order Function Approach to Evolve Recursive Programs -- Trivial Geography in Genetic Programming -- Running Genetic Programming Backwards -- An Examination of Simultaneous Evolution of Grammars and Solutions -- The Importance of Local Search -- Content Diversity in Genetic Programming and Its Correlation with Fitness -- Genetic Programming Inside a Cell -- Evolution on Neutral Networks in Genetic Programming -- The Effects of Size and Depth Limits on Tree Based Genetic Programming -- Application Issues of Genetic Programming in Industry -- Challenges in Open-Ended Problem Solving with Genetic Programming -- Domain Specificity of Genetic Programming Based Automated Synthesis: A Case Study with Synthesis of Mechanical Vibration Absorbers -- Genetic Programming in Industrial Analog CAD: Applications and Challenges.;Genetic Programming Theory and Practice III explores the emerging interaction between theory and practice in the cutting-edge, machine learning method of Genetic Programming (GP). This contributed volume was developed from the third workshop at the University of Michigans Center for the Study of Complex Systems to facilitate the exchange of ideas and information related to this rapidly advancing field. The text provides a cohesive view of the issues facing both practitioners and theoreticians and examines the synergy between GP theory and application. The foremost international researchers and practitioners in the GP arena contributed to the volume, discussing such topics as: techniques to enhance GP capabilities with real-world applications and real-world application success stories from a variety of domains, including chemical and process control, informatics, and circuit design visualization models to understand GP processing and open challenges facing the community and potential research directions Genetic Programming Theory and Practice III provides the most recent developments in GP theory, practice, and the integration of theory and practice. This text, the result of an extensive dialog between GP theoreticians and practitioners, is a unique and indispensable tool for both academics and industry professionals interested in the GP realm.
Riolo Rick: author's other books
Who wrote Genetic programming theory and practice III? Find out the surname, the name of the author of the book and a list of all author's works by series.
Riolo Rick
Jada Benn Torres
Sarah Boslaugh
Kotanchek Mark
Riccardo Poli
Kinga Laura Dobolyi
National Research Council (U.S.). Committee on Human Genome
Gerard Sierksma
Genetic programming theory and practice III — read online for free the complete book (whole text) full work
Below is the text of the book, divided by pages. System saving the place of the last page read, allows you to conveniently read the book "Genetic programming theory and practice III" online for free, without having to search again every time where you left off. Put a bookmark, and you can go to the page where you finished reading at any time.
Light
Font size:
↓
↑
Reset
Interval:
↓
↑
Bookmark:
Make
Rick Riolo , Ekaterina Vladislavleva , Marylyn D Ritchie and Jason H. Moore (eds.) Genetic and Evolutionary Computation Genetic Programming Theory and Practice X 2013 10.1007/978-1-4614-6846-2_1 Springer Science+Business Media New York 2013
1. Evolving SQL Queries from Examples with Developmental Genetic Programming
Thomas Helmuth 1
(1)
Department of Computer Science, University of Massachusetts, Amherst, MA 01003, USA
(2)
School of Cognitive Science, Hampshire College, Amherst, MA 01002, USA
Thomas Helmuth (Corresponding author)
Email:
Lee Spector
Email:
Abstract
Large databases are becoming ever more ubiquitous, as are the opportunities for discovering useful knowledge within them. Evolutionary computation methods such as genetic programming have previously been applied to several aspects of the problem of discovering knowledge in databases. The more specific task of producing human-comprehensible SQL queries has several potential applications but has thus far been explored only to a limited extent. In this chapter we show howdevelopmental genetic programming can automatically generate SQL queries from sets of positive and negative examples. We show that a developmental genetic programming system can produce queries that are reasonably accurate while excelling in human comprehensibility relative to the well-known C5.0 decision tree generation system.
Key words
Data mining Classification SQL Push PushGPIntroduction
In the emerging era of big data, vast amounts of data are available in many kinds of databases. Unfortunately, many users who have access to this data are unable to use it effectively because they do not know how to extract relevant, concise and comprehensible features or summaries of the data; that is, they do not know what queries to formulate in order to discover novel and useful aspects of the data. This issue can be addressed in part by a system that takes positive and negative example tupleswhich is generally easy for users to provideand returns concise, comprehensible SQL queries that classify the provided tuples in simple and potentially interesting ways.
The creation of queries from examples can be thought of as adata miningclassification problem, which is often one task within a larger knowledge discovery in databases process (Freitas, ). In this task the objective is to create a comprehensible and interesting query that correctly classifies the given examples. In many cases we have no reason to expect there to be a simple query that perfectly classifies the examples, but we would nonetheless like to create a reasonably simple query that both does a good job at classifying the examples and is concise enough to be easily interpreted by the user.
To make the general problem more concrete, we seek a system that takes as inputs a database D and training example tuples 
where E D and 
. Here, E + is the set of positive examples, and E is the set of negative examples. The goal of the system is to discover a concise and potentially interesting query Q such that E + Q ( D ) and 
.
where E D and . Here, E + is the set of positive examples, and E is the set of negative examples. The goal of the system is to discover a concise and potentially interesting query Q such that E + Q ( D ) and .We have developed a system called Query From Examples (QFE) that takes the set of examples E and searches for a query Q that satisfies the above properties. It does this by means ofdevelopmental genetic programming. In QFE, each program P creates (or develops) a query Q P that is then evaluated on how well it correctly classifies the given example tuples E .
In contrast to other approaches to the production of database queries with GP (see below), this form of developmental GP allows QFE to use standard program representations and genetic operators, along with standard population and evolutionary control parameters. The only change required to use this approach in conjunction with most GP systems is to include new developmental functions in the systems function set. The developmental approach makes it easy to implement systems like QFE on top of existing GP systems and thereby to take advantage of advances in the general state of the art of GP. In addition, it may make it easier to evolve queries of arbitrary structure, thereby enhancing the generality of the system for a wide range of applications.
In the work described in this chapter we ran QFE on a standarddata miningclassification task and compared its results to those given by thedecision tree classifier C5.0. We find that although QFE does not produce quite as accurate a classifier as C5.0, the classifier that it produces is more concise and comprehensible than the one produced by C5.0. We therefore believe that developmental GP is competitive with, and in some ways superior to, other modern data mining systems on the creation of classifiers.
The remainder of the chapter is structured as follows. The next section describes work that others have done evolving SQL queries. Section Evolving Queries from Examples describes our QFE system and its implementation. Our experiments and results are given in sects.Experimental Designand Results. Finally, we discuss limitations of QFE, possible improvements to QFE (including generalizations that QFE makes possible but that competing approaches would not), and our general conclusions.
Related Work
A variety of research has been conducted that uses GP either for the creation of queries (da Silva and Thomas, , among many others). Because this literature is quite voluminous and varied we will comment specifically only on those systems most closely related to QFE.
Castro da Silva and Thomas () directly evolve queries as individuals with the goal of generating queries for inexperienced SQL users. In order to ensure that evolved queries are syntactically correct they implement numerous non-standard genetic operators to combine and mutate individuals. This approach requires significant re-design of any existing GP system and, we would argue, limits the systems generality. Interestingly, this system seems to be the only prior work in which queries are allowed to include joins across tables, leaving the joining attribute up to evolution.
Acar and Motro () frame their work as trying to provide an alternative equivalent query to a given query by creating the alternative using the results of the original as positive examples. Although their motivation is different from ours, the resulting system has many similarities. Their method assumes that the sets of positive and negative examples cover the entire database instead of a small subset of it. The user must provide the entire set of example tuples that are in the database, which is probably impossible without using an a priori query to fetch them. The given system evolves actual queries as individuals, but can only handle queries expressible as trees of relational algebra expressions.
Freitas describes a GP system that evolves programs that can be interpreted as SQL queries to be used in thedata mining tasks ofclassification and generalized rule induction (Freitas, ). Individuals are represented as trees that directly correspond to WHERE clauses of queries. Unlike QFE, this work allows for the evolution of non-binary classifiers via niches that correspond to classes of the goal attribute. This paper was pioneering insofar as it introduced the idea of evolving SQL queries but it presents no experiments or results, and it does not make clear how one can deal with practical issues such as the choosing of constants, the design of an appropriate fitness function, the alterations that must be made to standard genetic operators, etc. Because there are no results one cannot judge the system with respect to query accuracy, comprehensibility, conciseness, and time. Additionally, this approach is limited (unlike the developmental approach that we present below) to the production of queries over a single table with WHERE clauses that can be expressed as trees. Freitas has continued to produce a great deal of significant related work but not, so far as we are aware, additional work on the use of GP to evolve SQL queries.
Light
Font size:
↓
↑
Reset
Interval:
↓
↑
Bookmark:
Make
Similar books «Genetic programming theory and practice III»
Look at similar books to Genetic programming theory and practice III. We have selected literature similar in name and meaning in the hope of providing readers with more options to find new, interesting, not yet read works.
Jada Benn Torres
Sarah Boslaugh
Kotanchek Mark
Riccardo Poli
Kinga Laura Dobolyi
National Research Council (U.S.). Committee on Human Genome
Gerard Sierksma
Reviews about «Genetic programming theory and practice III»
Discussion, reviews of the book Genetic programming theory and practice III and just readers' own opinions. Leave your comments, write what you think about the work, its meaning or the main characters. Specify what exactly you liked and what you didn't like, and why you think so.