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Molecular Genetic Programming

Molecular Genetic Programming. Soft Computing 5(2):106-113, 2001 P. Wasiewicz, J.J. Mulawka Summarized by Shin, Soo-Yong 2001.5.18. Abstract. A new implementation of genetic programming by using molecular approach. Based on dataflow techniques Handle graph encoding molecules.

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Molecular Genetic Programming

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  1. Molecular Genetic Programming Soft Computing 5(2):106-113, 2001 P. Wasiewicz, J.J. Mulawka Summarized by Shin, Soo-Yong 2001.5.18

  2. Abstract • A new implementation of genetic programming by using molecular approach. • Based on dataflow techniques • Handle graph encoding molecules (C) 2001, SNU Biointelligence Lab, http://bi.snu.ac.kr/

  3. Data flow computer • Have fully parallel architectures • Data availability rather than a program counter is used to drive the execution of instructions. (C) 2001, SNU Biointelligence Lab, http://bi.snu.ac.kr/

  4. Representing graphs (logical function graph) • The construction of the graph starts with creating nodes, which are related to function arguments plus one node – root. • The function’s result is TRUE when at least one leaf of the tree can be reached from the root through the successive nodes. (C) 2001, SNU Biointelligence Lab, http://bi.snu.ac.kr/

  5. Representing graphs • V : set of function argument nodes • N : number of graph nodes • M : number of function arguments • E : set of ordered node (arcs) 5’ 5’ 3’ 3’ nodes complement 3’ 5’ arc Restriction site (C) 2001, SNU Biointelligence Lab, http://bi.snu.ac.kr/

  6. Negation operator xi zi yi complement xk yk (C) 2001, SNU Biointelligence Lab, http://bi.snu.ac.kr/

  7. Negation operator (C) 2001, SNU Biointelligence Lab, http://bi.snu.ac.kr/

  8. Proposed genetic algorithm • Initiation • Put all strings into test tube • Cutting arc strings by enzyme • Concatenation of arc parts. New arcs are created. • Evaluation • Ligation & PCR (C) 2001, SNU Biointelligence Lab, http://bi.snu.ac.kr/

  9. crossover before (C) 2001, SNU Biointelligence Lab, http://bi.snu.ac.kr/

  10. crossover cut (C) 2001, SNU Biointelligence Lab, http://bi.snu.ac.kr/

  11. crossover After (new) (C) 2001, SNU Biointelligence Lab, http://bi.snu.ac.kr/

  12. Evaluation • Using existing arcs • Put TRUE sequences (a, b, c, or d) • Making paths by arcs & TRUE sequences • Check the length (correct path) • 의문점? • Graph가 true가 되었다고 해서 function이 true가 될 수 있는가? (C) 2001, SNU Biointelligence Lab, http://bi.snu.ac.kr/

  13. Evaluation (C) 2001, SNU Biointelligence Lab, http://bi.snu.ac.kr/

  14. Evaluation (C) 2001, SNU Biointelligence Lab, http://bi.snu.ac.kr/

  15. Conclusion • No mutation, only crossover • Making edges (by crossover) • Not practical implications (C) 2001, SNU Biointelligence Lab, http://bi.snu.ac.kr/

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