An object-oriented environment for specification and concurrent execution of genetic algorithms

This paper proposes a genetic algorithm-based method to generate test cases. This method provides information for test case generation using state machine diagrams. Its feature is realizing automation through fewer generated test cases. In terms of automatic generation of test data based on path coverage, the goal is to build a function that can excellently assess the generated test data and guide the genetic algorithms to find the targeting parameter values.

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Object-oriented job-shop scheduling using genetic algorithms

Computers in Industry ◽

10.1016/s0166-3615(96)00081-4 ◽

1997 ◽

Vol 32 (3) ◽

pp. 281-294 ◽

Cited By ~ 11

Author(s):

F. Maturana ◽

P. Gu ◽

A. Naumann ◽

D.H. Norrie

Keyword(s):

Genetic Algorithms ◽

Job Shop ◽

Job Shop Scheduling ◽

Object Oriented ◽

Shop Scheduling

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An Object-Oriented Framework for Rapid Genetic Algorithm Development

Handbook of Research on Nature-Inspired Computing for Economics and Management ◽

10.4018/978-1-59140-984-7.ch040 ◽

2007 ◽

pp. 608-624 ◽

Cited By ~ 6

Author(s):

A. L. Medaglia

Keyword(s):

Genetic Algorithm ◽

Genetic Algorithms ◽

Evolutionary Algorithms ◽

Optimization Problems ◽

Lot Sizing ◽

Rapid Development ◽

Object Oriented ◽

Lot Sizing Problem ◽

Fast Development ◽

Complex Optimization

JGA, the acronym for Java Genetic Algorithm, is a computational object-oriented framework for rapid development of evolutionary algorithms for solving complex optimization problems. This chapter describes the JGA framework and illustrates its use on the dynamic inventory lot-sizing problem. Using this problem as benchmark, JGA is compared against three other tools, namely, GAlib, an open C++ implementation; GADS, a commercial MatlabÒ toolbox; and PROC GA, a commercial (yet experimental) SASÒ procedure. JGA has proved to be a flexible and extensible object-oriented framework for the fast development of single (and multi-objective) genetic algorithms by providing a collection of ready-to-use modules (Java classes) that comprise the nucleus of any genetic algorithm. Furthermore, JGA has also been designed to be embedded in larger applications that solve complex business problems.

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Object-oriented genetic algorithms for two-dimensional design optimization of the magnetic circuit of a mobile magnetic resonance device

International Journal of Applied Electromagnetics and Mechanics ◽

10.3233/jae-2002-453 ◽

2002 ◽

Vol 15 (1-4) ◽

pp. 219-223 ◽

Cited By ~ 2

Author(s):

Hartmut Popella ◽

Gerhard Henneberger

Keyword(s):

Genetic Algorithms ◽

Magnetic Resonance ◽

Design Optimization ◽

Magnetic Circuit ◽

Object Oriented ◽

Two Dimensional

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The BETA Programming Language

DAIMI Report Series ◽

10.7146/dpb.v16i229.7578 ◽

1987 ◽

Vol 16 (229) ◽

Cited By ~ 23

Author(s):

Bent Bruun Kristensen ◽

Ole Lehrmann Madsen ◽

Birger Møller-Pedersen ◽

Kristen Nygaard

Keyword(s):

Programming Language ◽

Functional Programming ◽

Object Oriented ◽

Unified Framework ◽

Modern Language ◽

Concurrent Execution

The BETA programming language is a modern language in the SIMULA 67 tradition. It supports the object-oriented perspective on programming and contains comprehensive facilities for procedural and functional programming. BETA replaces classes, procedures, functions and types by a single abstraction mechanism called the pattern. Patterns may be organized in a classification hierarchy by means of sub-patterns. The notion of virtual procedure is generalized to virtual pattern. Virtual patterns combined with sub-patterns make it possible to delay the specification of an attribute in a pattern. Attributes may then have different bindings in different sub-patterns. BETA also provides a unified framework for sequential, coroutine and concurrent execution.This paper is a tutorial introduction to BETA.

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