Optimization of Genetic Operators for Scheduling Problems

2007 ◽  
Vol 11 (9) ◽  
pp. 1092-1098 ◽  
Author(s):  
António Ferrolho ◽  
◽  
Manuel Crisóstomo ◽  
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Job Scheduling ◽  
Software Tool ◽  
Scheduling Problems ◽  
Genetic Operators ◽  
Mutation Operators ◽  
Crossover And Mutation

Genetic algorithms (GA) can provide good solutions for scheduling problems. But, when a GA is applied to scheduling problems various crossovers and mutations operators can be applicable. This paper presents and examines a new concept of genetic operators for scheduling problems. A software tool called hybrid and flexible genetic algorithm (HybFlexGA) was developed to examine the performance of various crossover and mutation operators by computing simulations of job scheduling problems.

Scheduling Flexible Production Lines with No Intermediate Buffers by Hybrid Algorithms

2012 ◽  
Vol 557-559 ◽  
pp. 2229-2233
Author(s):  
Bing Gang Wang
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Algorithm ◽  
Initial Population ◽  
Flexible Production ◽  
Scheduling Problems ◽  
Production Lines ◽  
Solution Quality ◽  
Mutation Operators ◽  
Crossover And Mutation ◽  
Better Than

This paper is concerned about the scheduling problems in flexible production lines with no intermediate buffers. The optimization objective is to minimizing the makespan. The mathematical models are presented. Since the problem is NP-hard, a hybrid algorithm, based on genetic algorithm and tabu search, is put forward for solving the models. In this algorithm, the method of generating the initial population is proposed and the crossover and mutation operators, tabu list, and aspiration rule are newly designed. The performance of the hybrid algorithm is compared with that of the traditional genetic algorithm. The computational results show that satisfactory solutions can be obtained by the hybrid algorithm and it performs better than the genetic algorithm in terms of solution quality.

scholarly journals Software Systems Clustering Using Estimation of Distribution Approach

2016 ◽  
Vol 8 (2) ◽  
pp. 99-113 ◽  
Author(s):  
Mahjoubeh Tajgardan ◽  
Habib Izadkhah ◽  
Shahriar Lotfi
Keyword(s):  
Genetic Algorithms ◽  
Probabilistic Models ◽  
Evolutionary Process ◽  
Solution Space ◽  
Building Blocks ◽  
Genetic Operators ◽  
Software Clustering ◽  
Mutation Operators ◽  
Estimation Of Distribution ◽  
Crossover And Mutation

AbstractSoftware clustering is usually used for program understanding. Since the software clustering is a NP-complete problem, a number of Genetic Algorithms (GAs) are proposed for solving this problem. In literature, there are two wellknown GAs for software clustering, namely, Bunch and DAGC, that use the genetic operators such as crossover and mutation to better search the solution space and generating better solutions during genetic algorithm evolutionary process. The major drawbacks of these operators are (1) the difficulty of defining operators, (2) the difficulty of determining the probability rate of these operators, and (3) do not guarantee to maintain building blocks. Estimation of Distribution (EDA) based approaches, by removing crossover and mutation operators and maintaining building blocks, can be used to solve the problems of genetic algorithms. This approach creates the probabilistic models from individuals to generate new population during evolutionary process, aiming to achieve more success in solving the problems. The aim of this paper is to recast EDA for software clustering problems, which can overcome the existing genetic operators’ limitations. For achieving this aim, we propose a new distribution probability function and a new EDA based algorithm for software clustering. To the best knowledge of the authors, EDA has not been investigated to solve the software clustering problem. The proposed EDA has been compared with two well-known genetic algorithms on twelve benchmarks. Experimental results show that the proposed approach provides more accurate results, improves the speed of convergence and provides better stability when compared against existing genetic algorithms such as Bunch and DAGC.

scholarly journals Tuning Genetic Algorithm Parameters to Improve Convergence Time

2011 ◽  
Vol 2011 ◽  
pp. 1-7 ◽  
Author(s):  
Maria Angelova ◽  
Tania Pencheva
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Optimization Method ◽  
Convergence Time ◽  
Generation Gap ◽  
Genetic Operators ◽  
Time Saving ◽  
Fermentation Processes ◽  
Highly Nonlinear ◽  
Crossover And Mutation

Fermentation processes by nature are complex, time-varying, and highly nonlinear. As dynamic systems their modeling and further high-quality control are a serious challenge. The conventional optimization methods cannot overcome the fermentation processes peculiarities and do not lead to a satisfying solution. As an alternative, genetic algorithms as a stochastic global optimization method can be applied. For the purpose of parameter identification of a fed-batch cultivation ofS. cerevisiaealtogether four kinds of simple and four kinds of multipopulation genetic algorithms have been considered. Each of them is characterized with a different sequence of implementation of main genetic operators, namely, selection, crossover, and mutation. The influence of the most important genetic algorithm parameters—generation gap, crossover, and mutation rates has—been investigated too. Among the considered genetic algorithm parameters, generation gap influences most significantly the algorithm convergence time, saving up to 40% of time without affecting the model accuracy.

Integrated Process Planning and Scheduling Based on Genetic Algorithms

2011 ◽  
Vol 291-294 ◽  
pp. 331-334
Author(s):  
Jin Feng Wang ◽  
Shi Jie Li ◽  
Shun Cheng Fan
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Process Planning ◽  
Manufacturing System ◽  
Simulation Studies ◽  
Integrated Process ◽  
Improved Genetic Algorithm ◽  
Planning And Scheduling ◽  
Mutation Operators ◽  
Crossover And Mutation

Process planning and scheduling are two important manufacturing activities in the manufacturing system. In this paper, an improved genetic algorithm(GA) has been developed to facilitate the integration and optimization of process planning and scheduling. To improve the optimization performance, an efficient genetic representation has been developed. Selection, crossover, and mutation operators have been described. Simulation studies have been established to evaluate the performance of the algorithm. The results show that the algorithm is a promising and effective method for the integration of process planning and scheduling(IPPS).

scholarly journals Study on Selection Methods of Parents and Crossover in Genetic Algorithm

2021 ◽  
pp. 280-285
Author(s):  
Dr. K. Santhi ◽  
Dr. V. Vinodhini
Keyword(s):  
Genetic Algorithms ◽  
Optimization Technique ◽  
Population Based ◽  
Complex Problem ◽  
Genetic Operators ◽  
Mutation Operators ◽  
The People ◽  
Population Based Search ◽  
Crossover And Mutation ◽  
Selection Of

Genetic Algorithms are the population based search and optimization technique that mimic the process of Genetic and Natural Evolution. Genetic algorithms are very effective way of finding an Optimized solution to a complex problem. Performance of genetic algorithms mainly depends on various factors such as selection of efficient parents and type of genetic operators which involve crossover and mutation operators etc. This paper will help the people to acquire the knowledge about various strategies of selecting parents and description about standard crossover operators.

scholarly journals Representation Invariant Genetic Operators

2010 ◽  
Vol 18 (4) ◽  
pp. 635-660 ◽  
Author(s):  
Jonathan E. Rowe ◽  
Michael D. Vose ◽  
Alden H. Wright
Keyword(s):  
Genetic Algorithm ◽  
Group Action ◽  
Search Space ◽  
Complete Characterization ◽  
Genetic Operators ◽  
Invariance Properties ◽  
Mutation Operators ◽  
High Level ◽  
Crossover And Mutation

A genetic algorithm is invariant with respect to a set of representations if it runs the same no matter which of the representations is used. We formalize this concept mathematically, showing that the representations generate a group that acts upon the search space. Invariant genetic operators are those that commute with this group action. We then consider the problem of characterizing crossover and mutation operators that have such invariance properties. In the case where the corresponding group action acts transitively on the search space, we provide a complete characterization, including high-level representation-independent algorithms implementing these 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.

Simulation of the Pressure-Bearing Capacity Test Model in Architectural Decoration Design

2014 ◽  
Vol 716-717 ◽  
pp. 391-394
Author(s):  
Li Mei Guo ◽  
Ai Min Xiao
Keyword(s):  
Genetic Algorithm ◽  
Bearing Capacity ◽  
Test Method ◽  
Test Model ◽  
Improved Genetic Algorithm ◽  
Good Convergence ◽  
Mutation Operators ◽  
Process Pressure ◽  
Calculation Process ◽  
Crossover And Mutation

in architectural decoration process, pressure-bearing capacity test is the foundation of design, and is very important. To this end, a pressure-bearing capacity test method in architectural decoration design is proposed based on improved genetic algorithm. The selection, crossover and mutation operators in genetic algorithm are improved respectively. Using its fast convergence characteristics eliminate the pressure movement in the calculation process. The abnormal area of pressure-bearing existed in buildings which can ensure to be tested is added, to obtain accurate distribution information of the abnormal area of pressure-bearing. Simulation results show that the improved genetic algorithm has good convergence, can accurately test the pressure-bearing capacity in architectural decoration.

scholarly journals Utilizing Model Knowledge for Design Developed Genetic Algorithm to Solving Problem

2018 ◽  
Vol 3 (2) ◽  
pp. 172
Author(s):  
Hamidreza Salmani mojaveri
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Parallel Machines ◽  
Job Shop ◽  
Optimization Problems ◽  
Premature Convergence ◽  
Local Optimum ◽  
Scheduling Problems ◽  
Algorithm Implementation

One of the discussed topics in scheduling problems is Dynamic Flexible Job Shop with Parallel Machines (FDJSPM). Surveys show that this problem because of its concave and nonlinear nature usually has several local optimums. Some of the scheduling problems researchers think that genetic algorithms (GA) are appropriate approach to solve optimization problems of this kind. But researches show that one of the disadvantages of classical genetic algorithms is premature convergence and the probability of trap into the local optimum. Considering these facts, in present research, represented a developed genetic algorithm that its controlling parameters change during algorithm implementation and optimization process. This approach decreases the probability of premature convergence and trap into the local optimum. The several experiments were done show that the priority of proposed procedure of solving in field of the quality of obtained solution and convergence speed toward other present procedure.

scholarly journals SCHEDULLING MODEL OF REPLENISHMENT AT SEA FOR STRICKING FORCE UNIT IN SEA OPERATION USING GENETIC ALGORITHM

JOURNAL ASRO ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 1
Author(s):  
Aris Tri Ika R ◽  
Benny Sukandari ◽  
Okol Sri Suharyo ◽  
Ayip Rivai Prabowo
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Time Windows ◽  
Lubricating Oil ◽  
Optimal Time ◽  
Routing Problem ◽  
Scheduling Model ◽  
Force Unit ◽  
Replenishment At Sea ◽  
Crossover And Mutation

Navy as a marine core in the defense force is responsible for providing security for realizing stability and security of the country.  At any time there was an invasion of other countries past through sea,  TNI AL must be able to break the enemy resistance line through a sea operation to obtain the sea superiority. But this time the endurance of Striking force Unit at only 7-10 days and required replenishment at sea to maximize the presence in the theater of operations to meet a demand of the logistics: HSD, Freshwater, Lubricating Oil, foodstuffs and amonisi. For the optimal replenishment at sea required scheduling model supporting unit to get the minimum time striking force unit was on node rendezvous. Replenishment at sea scheduling model for striking force unit refers to the problems Vehicle routing problem with time windows using Genetic Algorithms. These wheelbase used is roulette for reproduction, crossover, and mutation of genes. Genetic algorithms have obtained optimum results in the shortest route provisioning scenario uses one supporting unit with a total time of 6.89 days. In scenario two supporting unit with minimal time is 4.97 days. In the scenario, the changing of the node replenishment Genetic Algorithm also get optimal time is 4.97 days with two supporting units. Research continued by changing the parameters of the population, the probability of crossover and mutation that can affect the performance of the genetic algorithm to obtain the solution. Keywords: Genetic Algorithm, Model Scheduling, Striking Force unit

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