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

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43 repeated to change the population until the first chromosome of the population obtains a good enough fitness value f(x). However, if repeated too many times, these operations will create a large number of chromosomes that is above the preset population size. To solve this problem, once the number of chromosomes increases up to a critical value n, we will kill off half of the population. 3.6.1 Representations This section defines the genetic representations of the chromosomes, the genes, and the population. 3.6.1.1 Chromosomes A chromosome is a solution, in our case a timetable of the university. The timetable contains a number of sub-timetables of classrooms. Each classroom has its own sub-timetable. Classroom i Hour Mon Tue Wed Thu Fri 08:00-09:00 Course 1 Course 2 09:00-10:00 Course 1 Course 2 10:00-11:00 Course 1 Course 2 11:00-12:00 13:00-14:00 Course 3 14:00-15:00 Course 3 15:00-16:00 Course 4 16:00-17:00 Course 4 FIGURE 3-7 Sub-timetable of a classroom We use a classroom as a ‘storage space’. Courses are scheduled to the time-slots for each classroom. This direct representation creates a visual view. Here courseis are courses that are divided into sections. These sections are assigned to be taught by particular lecturer and studied by a class of students. A look at the data relations in the database, we have course → lecturer, course→ class. This is a good foundation for checking the hard and soft constraint conflicts. The Figure 3-8 illustrates an entire chromosome.
44 Chromosome x i Fitness = f(x i ) Classroom n Mon Tue Wed Thu Fri Classroom 2 Class1 Class2 Mon Class1 Tue Wed Class2 Thu Fri Classroom Class1 1 Class1 Class2 Class2 Mon Class1 Tue Wed Class2 Thu Fri Course 1 Class1 Class3 Course 2 Class2 Course 1 Class3 Course 2 Course 1 Class3 Course 2 Class4 Class3 Class4 Course 3 Course 3 Class4 Class4 Course 4 Course 4 A gene=A time-slot FIGURE 3-8 Chromosome Each chromosome x i has a fitness value f(x i ). We will use this value to look for a good chromosome (a good solution). 3.6.1.2 Genes A gene is a time-slot in a chromosome, so there are many genes in a chromosome. Each gene contains a 0 if no course is held at that position. On the contrary, the gene contains a course. If changing value of the genes, we will create a new chromosome. 3.6.1.3 Population A population is a set of n chromosomes, or n solutions. The population is always sorted decreasingly in the order of the chromosome’s fitness value. As a result, the first chromosome has the highest fitness value, thus a candidate for the best solution, as illustrated in Figure 3-9. Chromosome x n Fitness = f(x n ) A population Chromosome x 2 Fitness = f(x 2 ) Chromosome x 1 Fitness = f(x 1 ) FIGURE 3-9 Population
Page 1 and 2: COURSE SCHEDULING IN MULTIPLE FACUL
Page 3 and 4: ชื่อ : นายฮูเ
Page 5 and 6: TABLE OF CONTENTS Page Abstract (in
Page 7 and 8: LIST OF TABLES Table Page 2-1 Cours
Page 9 and 10: LIST OF FIGURES (CONTINUED) Figure
Page 11 and 12: 2 The classrooms are sometime share
Page 13 and 14: 4 Classroom i Time-slot Hour Mon Tu
Page 15 and 16: CHAPTER 2 LITERATURE REVIEW In this
Page 17 and 18: 9 teach effectively. However, in so
Page 19 and 20: 11 The set of vertexes are consider
Page 21 and 22: 13 Gueret et al. have implemented a
Page 23 and 24: 15 When the atoms are at a high tem
Page 25 and 26: 17 The outline of the basic genetic
Page 27 and 28: 19 As shown in Figure 2-6, first of
Page 29 and 30: 21 2.3.2.1 Crossover After we have
Page 31 and 32: 23 these chromosomes. According to
Page 33 and 34: 25 2.4.1 Application Considerations
Page 35 and 36: 27 Southern California, the Argonne
Page 37 and 38: 29 2.4.3.2 Application Programming
Page 39 and 40: 31 2.5 Summary The course schedulin
Page 41 and 42: 34 3.1.5 Phase 5: Measurement a) To
Page 43 and 44: 36 All the above problems can be pr
Page 45 and 46: 38 Client at a faculty Client at th
Page 47 and 48: 40 The Grid system is only used by
Page 49: 42 3.6 The Proposed Genetic Algorit
Page 53 and 54: 46 A population is a list of n chro
Page 55 and 56: 48 Lecturers register their busy ti
Page 57 and 58: 50 A course is booked to the time-l
Page 59 and 60: 52 As mentioned in the previous sec
Page 61 and 62: 54 GIIS from the client machine m1.
Page 63 and 64: 56 The complete source code for man
Page 65 and 66: 58 3.8.2.2 Stage 2 After the centra
Page 67 and 68: 60 3.8.3.3 The Gatekeeper The gatek
Page 69 and 70: 62 TABLE 4-1 (CONTINUED) Class Seme
Page 71 and 72: 64 26 lecturers are assigned to tea
Page 73 and 74: 66 TABLE 4-2 (CONTINUED) Course Sec
Page 75 and 76: 68 This result shows that the GA ra
Page 77 and 78: 70 should revise the chromosome rep
Page 79 and 80: 72 - Population size : 10 - Crossov
Page 81 and 82: 74 for network communication is muc
Page 83 and 84: 76 TABLE 4-5 Timetable created by t
Page 85 and 86: CHAPTER 5 CONCLUSION 5.1 Conclusion
Page 87 and 88: REFERENCES 1. Alkan, A. and Ozcan,
Page 89 and 90: 83 20. Burke, E. K. and Newall, J.
Page 91 and 92: 85 38. Globus Toolkit [Online]. Ava
Page 93 and 94: 88 This section presents the struct
Page 95 and 96: 90 A.5 Building TABLE A-5 Building
Page 97 and 98: 92 A.10 Program TABLE A-10 Program
Page 99 and 100: 94 A.14 Timetable TABLE A-14 Timeta
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96 This section presents in detail
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98 TABLE B-2 Group, user ID and pas
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100 broadcastdelay 0.008 authentica
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102 mycapw [enter] During the above
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104 service gsigatekeeper { socket_
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106 B.3.3 Starting the MDS on All o
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108 B.4 Checking the Installation T
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110 This section introduces the ste
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112 C.3.2 Configuration This sectio
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114 [root@m1 root]#grid-proxy-info
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116 The system will automatically p
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118 C.6 Installing JDBC Driver on L
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120 This section presents how to co
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122 All the following files are com
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124 } } } try { ctx.close(); } catc
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126 }//for }else if(attribute.equal
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128 public static void main(String
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130 System.out.println(CentralizedS
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132 gassJob[jobCount]=new GassJob(t
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134 /** * Init job out listeners fo
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136 // Start GASS server if (! star
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138 // waits for DONE or FAILED sta
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140 //get RSL of Job having index i
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142 class Job { private String jobn
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145 BIOGRAPHY Name : Mr. Nguyen Con
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