scholarly journals The Crossover Operator of a Genetic Algorithm as Applied to the Task of a Production Planning

2019 ◽  
Vol 150 ◽  
pp. 603-608 ◽  
Author(s):  
S.L. Podvalny ◽  
M.I. Chizhov ◽  
P.Y. Gusev ◽  
K.Y. Gusev
Keyword(s):  
Genetic Algorithm ◽  
Production Planning ◽  
Crossover Operator

scholarly journals A GENETIC APPROACH FOR LONG TERM VIRTUAL ORGANIZATION DISTRIBUTION

2011 ◽  
Vol 20 (02) ◽  
pp. 271-295 ◽  
Author(s):  
VÍCTOR SÁNCHEZ-ANGUIX ◽  
SOLEDAD VALERO ◽  
ANA GARCÍA-FORNES
Keyword(s):  
Genetic Algorithm ◽  
Virtual Organization ◽  
Genetic Material ◽  
Multi Agent System ◽  
Crossover Operator ◽  
Agent Based ◽  
Multi Agent ◽  
Global Goal ◽  
Computational Resources

An agent-based Virtual Organization is a complex entity where dynamic collections of agents agree to share resources in order to accomplish a global goal or offer a complex service. An important problem for the performance of the Virtual Organization is the distribution of the agents across the computational resources. The final distribution should provide a good load balancing for the organization. In this article, a genetic algorithm is applied to calculate a proper distribution across hosts in an agent-based Virtual Organization. Additionally, an abstract multi-agent system architecture which provides infrastructure for Virtual Organization distribution is introduced. The developed genetic solution employs an elitist crossover operator where one of the children inherits the most promising genetic material from the parents with higher probability. In order to validate the genetic proposal, the designed genetic algorithm has been successfully compared to several heuristics in different scenarios.

scholarly journals A Modified Genetic Algorithm with Local Search Strategies and Multi-Crossover Operator for Job Shop Scheduling Problem

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5440 ◽  
Author(s):  
Monique Simplicio Viana ◽  
Orides Morandin Junior ◽  
Rodrigo Colnago Contreras
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Crossover Operator ◽  
Local Optima ◽  
Shop Scheduling ◽  
Solution Methods

It is not uncommon for today’s problems to fall within the scope of the well-known class of NP-Hard problems. These problems generally do not have an analytical solution, and it is necessary to use meta-heuristics to solve them. The Job Shop Scheduling Problem (JSSP) is one of these problems, and for its solution, techniques based on Genetic Algorithm (GA) form the most common approach used in the literature. However, GAs are easily compromised by premature convergence and can be trapped in a local optima. To address these issues, researchers have been developing new methodologies based on local search schemes and improvements to standard mutation and crossover operators. In this work, we propose a new GA within this line of research. In detail, we generalize the concept of a massive local search operator; we improved the use of a local search strategy in the traditional mutation operator; and we developed a new multi-crossover operator. In this way, all operators of the proposed algorithm have local search functionality beyond their original inspirations and characteristics. Our method is evaluated in three different case studies, comprising 58 instances of literature, which prove the effectiveness of our approach compared to traditional JSSP solution methods.

Sign in / Sign up

Export Citation Format

Share Document