Fuzzy Grouping Genetic Algorithms: Advances for Real-World Grouping Problems

2016 ◽  
pp. 67-86
Author(s):  
Michael Mutingi ◽  
Charles Mbohwa
Keyword(s):  
Genetic Algorithms ◽  
Real World

Using Genetic Algorithms to Search for Key Stakeholders in Large-Scale Software Projects

2013 ◽  
pp. 118-134 ◽  
Author(s):  
Soo Ling Lim ◽  
Mark Harman ◽  
Angelo Susi
Keyword(s):  
Genetic Algorithms ◽  
Real World ◽  
Large Scale ◽  
Requirements Elicitation ◽  
Software Project ◽  
Software System ◽  
Software Projects ◽  
Key Stakeholders ◽  
Large Software ◽  
Stakeholder Needs

Large software projects have many stakeholders. In order for the resulting software system and architecture to be aligned with the enterprise and stakeholder needs, key stakeholders must be adequately consulted and involved in the project. This work proposes the use of genetic algorithms to identify key stakeholders and their actual influence in requirements elicitation, given the stakeholders’ requirements and the actual set of requirements implemented in the project. The proposed method is applied to a large real-world software project. Results show that search is able to identify key stakeholders accurately. Results also indicate that many different good solutions exist. This implies that a stakeholder has the potential to play a key role in requirements elicitation, depending on which other stakeholders are already involved. This work demonstrates the true complexity of requirements elicitation – all stakeholders should be consulted, but not all of them should be treated as key stakeholders, even if they appear to be significant based on their role in the domain.

Genetic Algorithms and Multimodal Search

2011 ◽  
pp. 231-249
Author(s):  
Marcos Gestal ◽  
José Manuel Vázquez Naya ◽  
Norberto Ezquerra
Keyword(s):  
Genetic Algorithms ◽  
Evolutionary Computation ◽  
Unique Solution ◽  
Real World ◽  
Multiple Solutions ◽  
Global Solutions ◽  
Search Space ◽  
The Real ◽  
Local Solutions ◽  
Multimodal Problems

Traditionally, the Evolutionary Computation (EC) techniques, and more specifically the Genetic Algorithms (GAs), have proved to be efficient when solving various problems; however, as a possible lack, the GAs tend to provide a unique solution for the problem on which they are applied. Some non global solutions discarded during the search of the best one could be acceptable under certain circumstances. Most of the problems at the real world involve a search space with one or more global solutions and multiple local solutions; this means that they are multimodal problems and therefore, if it is desired to obtain multiple solutions by using GAs, it would be necessary to modify their classic functioning outline for adapting them correctly to the multimodality of such problems. The present chapter tries to establish, firstly, the characterisation of the multimodal problems will be attempted. A global view of some of the several approaches proposed for adapting the classic functioning of the GAs to the search of mu ltiple solutions will be also offered. Lastly, the contributions of the authors and a brief description of several practical cases of their performance at the real world will be also showed.

Genetic algorithm synthesis of four-bar mechanisms

1996 ◽  
Vol 10 (5) ◽  
pp. 371-390 ◽  
Author(s):  
Gerald P. Roston ◽  
Robert H. Sturges
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Classical Theory ◽  
Real World ◽  
Design Problem ◽  
Numerical Technique ◽  
Active Area ◽  
Mechanism Synthesis ◽  
Classical Design ◽  
Real World Problems

AbstractThe synthesis of four-bar mechanisms is a well-understood, classical design problem. The original systematic work in this field began in the late 1800s and continues to be an active area of research. Limitations to the classical theory of four-bar synthesis potentially limit its application to certain real-world problems by virtue of the small number of precision points and unspecified order. This paper presents a numerical technique for four-bar mechanism synthesis based on genetic algorithms that removes this limitation by relaxing the accuracy of the precision points.

A Survey to Nature Inspired Soft Computing

2017 ◽  
Vol 8 (2) ◽  
pp. 112-133 ◽  
Author(s):  
Deepak Kumar ◽  
Sushil Kumar ◽  
Rohit Bansal ◽  
Parveen Singla
Keyword(s):  
Genetic Algorithms ◽  
Particle Swarm Optimization ◽  
Real World ◽  
Firefly Algorithm ◽  
Travelling Salesman Problem ◽  
Cuckoo Search ◽  
Travelling Salesman ◽  
Swarm Optimization ◽  
Physical Chemical ◽  
Time Problem

This article describes how swarm intelligence (SI) and bio-inspired techniques shape in-vogue topics in the advancements of the latest algorithms. These algorithms can work on the basis of SI, using physical, chemical and biological frameworks. The authors can name these algorithms as SI-based, inspired by biology, physics and chemistry as per the basic concept behind the particular algorithm. A couple of calculations have ended up being exceptionally effective and consequently have turned out to be the mainstream devices for taking care of real-world issues. In this article, the reason for this survey is to show a moderately complete list of the considerable number of algorithms in order to boost research in these algorithms. This article discusses Ant Colony Optimization (ACO), the Cuckoo Search, the Firefly Algorithm, Particle Swarm Optimization and Genetic Algorithms in detail. For ACO a real-time problem, known as Travelling Salesman Problem, is considered while for other algorithms a min-sphere problem is considered, which is well known for comparison of swarm techniques.

A real-world scheduling problem using genetic algorithms

1995 ◽  
Vol 29 (1-4) ◽  
pp. 177-181 ◽  
Author(s):  
John C. Gilkinson ◽  
Luis C. Rabelo ◽  
Brian O. Bush
Keyword(s):  
Genetic Algorithms ◽  
Real World ◽  
Scheduling Problem
Sign in / Sign up

Export Citation Format

Share Document