tsp with genetic algorithm
TRANSCRIPT
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082236 박수진082275 민지아082303 장이현061645 송창호
Algorithm Term ProjectTraveling Salesman’s Problemwith Genetic Algorithm
1 TSP2 Genetic Algorithm3 Application Case4 GUI & Demonstration5 Q & A
Traveling Salesman’s Problem
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01 Traveling Salesman’s Problem
NP-hard
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02 Traveling Salesman’s Problem Solution
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02 Traveling Salesman’s Problem Solution
Dynamic ProgramingBrute Force AlgorithmGreedy AlgorithmGenetic AlgorithmSimulated Annealing AlgorithmAnt Colony Optimization Algo-rithmRiver Formation DynamicsThe Cross Entropy MethodTabu Search……
Solution List
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문서의 제목 7
Genetic Algorithm
8
01 Genetic Algorithm
John Holland ( 1929 ~ )
• Professor of Electrical Engineering and Computer Science at the University of Michi-gan
• Known as the father of genetic algorithms
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Genetic Algorithm01
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Genetic Algorithm Pseudo Code02
Procedure GAInitialize Population;Evaluate Population;While not (terminal condition satisfied)
doSelect chromosomes for next popula-
tionCrossover and Mutation;Evaluate Population;End whileEnd Procedure
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Genetic Algorithm – Initialize Popula-tion
03
A B C D E F G
C E F A G D B
Ran-dom
Number Population
0 A B D F G E C
1 B C G F E D A
2
3
4
5
….
Population Group
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Genetic Algorithm – Evaluate Popula-tion
04
Number Population Fitness
0 A B D F G E C 120
1 B C G F E D A 240
2 C E F A G D B 490
3 G A E C B D F 100
4 C B A F G D E 320
5 A D G E F B C 190
…. … …
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Genetic Algorithm – Select05
Roulette Se-lection
Population_1 > Population_2 > Population_3 > …
Population_2
Population_3
Population_1
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Genetic Algorithm – Crossover06
0 1 0 1 1 0 1
1 0 0 1 0 1 1
0 1 0 1 0 1 1
1 0 0 1 1 0 1
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Genetic Algorithm – Crossover06
B C G F E D A
C B E A D F G
B C G F D F G
C B E A E D A
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Genetic Algorithm – Crossover06
A B C D E F G
A C D E F G
A D E F G
A D E F
A D E
A D
A
B C G F E D A
2 C G F E D A
2 2 G F E D A2 2 5 F E D A
2 2 5 4 E D A
2 2 5 4 3 D A
2 2 5 4 3 2 A
2 2 5 4 3 2 1
2 2 5 4 3 2 1
B 2 5 4 3 2 1
B C 5 4 3 2 1B C G 4 3 2 1
B C G F 3 2 1
B C G F E 2 1
B C G F E D 1
B C G F E D A
A B C D E F G
A C D E F G
A D E F G
A D E F
A D E
A D
A
Encoding Decoding
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Genetic Algorithm – Crossover06
2 2 5 4 3 2 1
3 2 3 1 1 1 1
2 2 5 4 1 1 1
3 2 3 1 3 2 1
B C G F A D E
C B E A G F D
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Genetic Algorithm – Mutation07
B C G F E D A
B E G F C D A
문서의 제목 19
Application Case
Everland Shortest tour
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01 Application Case – Everland Shortest tour
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Visual Studio 2010Edit Plus 3.31
Naver Map API.Net Framework
4.0
C#, Javascript
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02 ISSUE
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?
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02 ISSUE
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02 ISSUE
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가내 수공업
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02 ISSUE
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Drawing Map Naver Map
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03 GUI & Demonstration
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Q & A
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감사합니다