IMPLEMENTATION OF GA TO JSSP

From the earlier said operational parameters, we took the real-value encoding in which the job numbers are encoded to form the string which will give the sequence of machining order. i.e., each chromosome represents a legal solution to the problem and is composed of a string of genes. In our application, each solution is encoded as a string of job sequence. For example, the string “235641” represents for a job sequence of job 2 is processed before job “3”, job “3” is processed before job “5” and so on. The requirement is that repeating a job in a sequence is not allowed and all jobs should appear in the sequence.

For the crossover we preferred the one point method since it is simple and easy to implement and it is robust. In case of mutation the swap mutation is used, which play the major role to avoid the convergence. Termination condition used is Fixed Generation Termination. In general the roulette wheel selection is used for the maximize problem so here we used the rank selection with the fitness function as said above.

Configuration of Operational Parameters with which application runs 

Population Size	Number of chromosomes in the population is 30
Crossover	Probability of crossover’s tossing head is 0.6
Mutation	Probability of Mutation’s tossing head 0.2
Fitness	Subjective Fitness of Makes-span
Selection	Linear Ranking selection
Termination Condition	After 100 Generations

References:

Book Reference: David Edward Goldberg (1989), “Genetic Algorithms in Search, Optimization and Machine Learning” Addison-Wesley.

Citation Needed: Scheduling Problem For A Single Flexible Manufacturing machine, Nguyen Van Hop, Industrial Engineering Program, Sirindhorn International Institute of Technology, Thammasat University

Related Posts:

• ABSTRACT
• INTRODUCTION
• Genetic Algorithm
• Mechanism
• History
• Biological Background
• Parameters

Table of Contents

© 2006 Kumaravel & Project Team

---

If references link found broken see the below e-printed version of webpage

Note: e-printed version of the webpage, are just for the reference and it was not owned by blog author. It had be created using the Open Source PDFCreator, which is environment friendly to save paper.

---

Genetic Operators

Genetic variation is a necessity for the process of evolution. Genetic operators used in genetic algorithms are analogous to those which occur in the natural world: survival of the fittest, or selection; asexual or sexual reproduction (crossover, or recombination); and mutation.
Crossover
In genetic algorithms, crossover is a genetic operator used to

Fitness

Fitness is used to determine which chromosomes will be used for the next generation. Mathematically, for chromosome S_k: fitness = f(S_k) where: k = 1,..., n; n is a population size, which function that you use to calculate the fitness is obviously dependant on how you decide to encode the string to represent your problem.

Selection

During each successive epoch, a proportion of the existing population is selected to breed a new generation. Individual solutions are selected through a fitness-based process, where fitter solutions (as measured by a fitness function) are typically more likely to be selected.

Operational Parameters of Genetic Algorithm

As said earlier the following are genetic parameters which are similar to Biological background of the nature.

• Chromosomes
  
• Fitness
  

BIOLOGICAL BACKGROUND - GA

All living organisms consist of cells. In each cell there is the same set of chromosomes. Chromosomes are strings of DNA and serves as a model for the whole organism. A chromosome's characteristic is determined by the genes. Each gene has several forms or alternatives which are called alleles, producing differences in the set of characteristics associated with that gene. The set of chromosome is called the genotype, which defines a phenotype (the individual) with certain fitness.