Genetic algorithms solution to generator maintenance scheduling with modified genetic operators

2003 ◽  
Vol 150 (1) ◽  
pp. 56 ◽  
Author(s):  
S. Baskar ◽  
P. Subbaraj ◽  
M.V.C. Rao ◽  
S. Tamilselvi
Keyword(s):  
Genetic Algorithms ◽  
Maintenance Scheduling ◽  
Genetic Operators

Minimization of total trim loss occurring in pipe cutting in shipbuilding with genetic algorithm

2021 ◽  
pp. 147509022199169
Author(s):  
Abdullah Türk ◽  
Dursun Saral ◽  
Murat Özkök ◽  
Ercan Köse
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Search Space ◽  
Cutting Process ◽  
Genetic Operators ◽  
Critical Stage ◽  
Different Types ◽  
Trim Loss ◽  
Pipe Systems

Outfitting is a critical stage in the shipbuilding process. Within the outfitting, the construction of pipe systems is a phase that has a significant effect on time and cost. While cutting the pipes required for the pipe systems in shipyards, the cutting process is usually performed randomly. This can result in large amounts of trim losses. In this paper, we present an approach to minimize these losses. With the proposed method it is aimed to base the pipe cutting process on a specific systematic. To solve this problem, Genetic Algorithms (GA), which gives successful results in solving many problems in the literature, have been used. Different types of genetic operators have been used to investigate the search space of the problem well. The results obtained have proven the effectiveness of the proposed approach.

Assembly Planning: A Genetic Approach

1998 ◽  
Author(s):  
Shiang-Fong Chen
Keyword(s):  
Genetic Algorithms ◽  
Fitness Function ◽  
Assembly Planning ◽  
Genetic Approach ◽  
Genetic Operators ◽  
Complex Problems ◽  
All Solutions ◽  
Assembly Problem ◽  
Optimal Assembly

Abstract The difficulty of an assembly problem is the inherent complexity of possible solutions. If the most suitable plan is selected after all solutions are found, it will be very time consuming and unrealistic. Motivated by the success of genetic algorithms (GAs) in solving combinatorial and complex problems by examining a small number of possible candidate solutions, GAs are employed to find a near-optimal assembly plan for a general environment. Five genetic operators are used: tree crossover, tree mutation, cut-and-paste, break-and-joint, and reproduction. The fitness function can adapt to different criteria easily. This assembly planner can help an inexperienced technician to find a good solution efficiently. The algorithm has been fully implemented. One example product is given to show the applications and results.

scholarly journals A GENETIC BASED TECHNIQUE FOR THE DETERMINATION OF POWER SYSTEM TOPOLOGICAL OBSERVABILITY

2014 ◽  
pp. 16-21
Author(s):  
S. Vazquez-Rodriguez ◽  
R. J. Duro
Keyword(s):  
Genetic Algorithms ◽  
Power Systems ◽  
Power System ◽  
Spanning Tree ◽  
Point Of View ◽  
Genetic Operators ◽  
Building Process ◽  
Phenotype Transformation

In this paper we have addressed the problem of observability of power systems from the point of view of topological observability and using genetic algorithms for its determination. The objective is to find a way to determine if a system is observable by establishing if a spanning tree of the system that verifies certain properties with regards to the use of available measurements can be obtained. To this end we have developed a genotype-phenotype transformation scheme for genetic algorithms that permits using very simple genetic operators over integer based chromosomes which after a building process can become very complex trees. The procedure was successfully applied to standard benchmark systems and we present some results for one of them.

scholarly journals Computing strong metric dimension of some special classes of graphs by genetic algorithms

2008 ◽  
Vol 18 (2) ◽  
pp. 143-151 ◽  
Author(s):  
Jozef Kratica ◽  
Vera Kovacevic-Vujcic ◽  
Mirjana Cangalovic
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Graph Coloring ◽  
Crew Scheduling ◽  
Metric Dimension ◽  
Hard Problem ◽  
Genetic Operators ◽  
Strong Metric Dimension ◽  
Np Hard Problem ◽  
Special Classes

In this paper we consider the NP-hard problem of determining the strong metric dimension of graphs. The problem is solved by a genetic algorithm that uses binary encoding and standard genetic operators adapted to the problem. This represents the first attempt to solve this problem heuristically. We report experimental results for the two special classes of ORLIB test instances: crew scheduling and graph coloring.

An Empirical Study of the Effect of Parameter Combination on the Performance of Genetic Algorithms

2013 ◽  
Vol 1 (2) ◽  
pp. 43-55
Author(s):  
Pi-Sheng Deng
Keyword(s):  
Genetic Algorithms ◽  
Empirical Study ◽  
Population Size ◽  
Mutation Rate ◽  
Design Guidelines ◽  
Selection System ◽  
Genetic Operators ◽  
Research Results ◽  
Parameter Combination ◽  
Determinant Factor

Performance of genetic algorithms is affected not only by each genetic operator, but also by the interaction among genetic operators. Research on this issue still fails to converge to any conclusion. In this paper, the author focuses mainly on investigating, through a series of systematic experiments, the effects of different combinations of parameter settings for genetic operators on the performance of the author’s GA-based batch selection system, and compare the research results with the claims made by previous research. One of the major findings of the author’s research is that the crossover rate is not as a determinant factor as the population size or the mutation rate in affecting a GA’s performance. This paper intends to serve as an inquiry into the research of useful design guidelines for parameterizing GA-based systems.

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