Artificial Intelligence based effective algorithm for Software Testing | A Genetic Algorithm perspective

Bright Keswani (u. a.)

ISBN 10: 6202518456 / ISBN 13: 9786202518451

Published by LAP LAMBERT Academic Publishing, 2020

About this Item

Artificial Intelligence based effective algorithm for Software Testing | A Genetic Algorithm perspective | Bright Keswani (u. a.) | Taschenbuch | 132 S. | Englisch | 2020 | LAP LAMBERT Academic Publishing | EAN 9786202518451 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.

Seller Inventory # 118282645

Bibliographic Details

• Title: Artificial Intelligence based effective algorithm for Software Testing | A Genetic Algorithm perspective
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2020
• Language: English
• ISBN 10: 6202518456
• ISBN 13: 9786202518451
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 9D millimeters 220 millimeters width by 150 millimeters height by 9 millimeters depth
• Weight: 215 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

Artificial Intelligence algorithms were great in their use in practical life. After successful application of various computation techniques, a strong need arose for development of Intelligent Systems and thus started a new era of Soft Computing. Genetic Algorithm is one such soft computing technique that mimics the nature and has all the potential of a computation technique. Genetic algorithms are computing algorithms constructed in analogy with the process of evolution. Genetic Algorithms are a class of stochastic search methods that try to find good solutions to difficult optimization problems by mimicking natural evolution. Over the past three decades, genetic algorithms have proved themselves successful in solving multitudinous problems from highly diverse domains including optimization, automatic programming, electronic-circuit design, telecommunications, networks, finance, economics, image analysis, signal processing, music and art. With these applications of genetic algorithms it is possible to use genetic algorithms in Software Engineering to automate testing process.