a multi-objective bisexual reproduction genetic algorithm for ...

LIST OF FIGURES (CONTINUED)
Figure
Page
3-10 Creating constraint data 45
3-11 Algorithm for initializing a random population 45
3-12 Pseudo code for creating a random chromosome 46
3-13 Pseudo code for checking small classroom conflicts 47
3-14 Pseudo code for checking lecturer’s busy time 47
3-15 Pseudo code for detecting conflicts about preferable times 48
3-16 Pseudo code for checking conflicts about double scheduled lecturers 48
3-17 Pseudo code for checking conflicts about double scheduled classes 49
3-18 Pseudo code for checking conflicts about double scheduled courses 49
3-19 Crossover 50
3-20 Pseudo code for crossover 51
3-21 Mutation 52
3-22 Pseudo code for mutating a chromosome 52
3-23 Hardware and software for each machine 53
3-24 MDS configuration 54
3-25 Working with a broker 55
3-26 Centralized scheduling 56
3-27 Job scheduler for the grid computing environment 57
3-28 Overview of GRAM and GASS 58
4-1 The average fitness value of hard constraints vs various weights 67
4-2 The average fitness value of soft constraints vs various weights 68
4-3 The average execution time for a resultant solution vs population sizes 69
4-4 The GA with various mutation rates 71
4-5 The execution time versus various models 72
4-6 Parallel execution versus serial execution 73
C-1 Visual-grid-proxy-init 113
C-2 Service configuration 115
C-3 Result in the web browser 117
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