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Molecular Genetic Programming: Data-Driven Graph Representation and Evaluation

This study presents a novel genetic programming implementation using a molecular approach, focusing on dataflow techniques for graph encoding molecules. The fully parallel architectures rely on data availability to drive instruction execution, emphasizing logical function graph representation. The proposed genetic algorithm involves string manipulation through cutting, concatenation, ligation, and PCR steps, followed by crossover for evaluating true sequences and creating valid paths. The evaluation process raises questions on the true functionality of the generated graph. However, the study concludes that only crossover without mutation may limit practical implications.

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Molecular Genetic Programming: Data-Driven Graph Representation and Evaluation

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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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