Genetic Operators Impact on Genetic Algorithms Based Variable Selection

2020 ◽  
pp. 211-221
Author(s):  
Marco Vannucci ◽  
Valentina Colla ◽  
Silvia Cateni
Keyword(s):  
Genetic Algorithms ◽  
Variable Selection ◽  
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.

Variable selection in STAR models with neighbourhood effects using genetic algorithms

2010 ◽  
Vol 29 (8) ◽  
pp. 728-750 ◽  
Author(s):  
Isolina Alberto ◽  
Asunción Beamonte ◽  
Pilar Gargallo ◽  
Pedro M. Mateo ◽  
Manuel Salvador
Keyword(s):  
Genetic Algorithms ◽  
Variable Selection ◽  
Neighbourhood Effects ◽  
Star Models

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.

Several Approaches to Variable Selection by Means of Genetic Algorithms

2011 ◽  
pp. 274-292
Author(s):  
Marcos Gestal Pose ◽  
Alberto Cancela Carollo ◽  
José Manuel Andrade Garda ◽  
Mari Paz Gomez-Carracedo
Keyword(s):  
Analytical Chemistry ◽  
Genetic Algorithms ◽  
Variable Selection ◽  
Evolutionary Computation ◽  
Fitness Function ◽  
Classification Model ◽  
Classification Task ◽  
Practical Case ◽  
General Method

This chapter shows several approaches to determine how the most relevant subset of variables can perform a classification task. It will permit the improvement and efficiency of the classification model. A particular technique of evolutionary computation, the genetic algorithms, is applied which aim to obtain a general method of variable selection where only the fitness function will be dependent on the particular problem. The solution proposed is applied and tested on a practical case in the field of analytical chemistry to classify apple beverages.

Chemically driven variable selection by focused multimodal genetic algorithms in mid-IR spectra

2007 ◽  
Vol 389 (7-8) ◽  
pp. 2331-2342 ◽  
Author(s):  
M. P. Gómez-Carracedo ◽  
M. Gestal ◽  
J. Dorado ◽  
J. M. Andrade
Keyword(s):  
Genetic Algorithms ◽  
Variable Selection ◽  
Ir Spectra

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.

Sign in / Sign up

Export Citation Format

Share Document