Buyng-Ro Moon
- 1.J. Bagley. The Behavior of Adaptive Systems Which Employ Genetic and Correlation Algorithms. PhD thesis. University of Micitigan, Ann Arbor. MI. 1967. Google Scholar
Digital Library
- 2.T. N. But and B. R. Moon. Hyperplane synthesis for genetic algorithms. In Fifth International Conference on Genetic Algorithms. pages 102-109, July 1993. Google ScholarDigital Library
- 3.T. N. But and B. R. Moon. Analyzing hyperplallosynthesis ill genetic algorithms using clustered sch,:l!lata. In International Conference on Evolutionary Computation. Oct. 1994. Lecture NoteJ in Computer Science, 866:108- 118, Springer-Verlag. Google ScholarDigital Library
- 4.T. N. Bud and B. Ft. Moon. A fast and stable hybrid genetic algorithm for the ratio-cut partitioning problem on hypergraphs. In 31 st A CM/IEEE Design Automation Conference, pages 664-669, June 1994. Google ScholarDigital Library
- 5.T. N. But and B. R. Moon. Graph partitioning and genetic algorithms. IEEE Trans. on Computers, 45(7):841- 855, 1996. Google ScholarDigital Library
- 6.P. Chan, P. Mazumder, and K. Shahookax. Macro-cell and module placement by genetic adaptive search with bitmap-represented chromosome. Integration, 12(1):49- 77, 1991. Google ScholarDigital Library
- 7.L. Davis. Handbook of Genetic Algorithms. Van Nostrand Reinhold, NY, 1991.Google Scholar
- 8.W. Donath and A. Hoffman. Algorithms for partitioning of graphs and computer logic based on eigenvectors of connected matrices. IBM Technical disclosure Bulletin, 15:938-944, 1972.Google Scholar
- 9.C. Fiduccia and Ft. Mattheyses. A linear time heuristicfor improving network partitions. In 19th A CM/IEEE Design Automation Conference, pages 175-181, 1982. Google ScholarDigital Library
- 10.L. Hagen and A. B. Kahng. New spectral methods for ratio cut partitioning and clustering. IEEE Trans. on Computer. Aided Design, 11(9):297-314, 1992.Google ScholarDigital Library
- 11.J. Holland. Adaptation in Natural and Artificial Systems, ~nd ed. MIT Press, 1992. Google ScholarDigital Library
- 12.A. B. Kahng and B. R. Moon. Toward more powerful recombinations. In Sixth International Conference on genetic Algorithms. pages 96-103, 1995. Google ScholarDigital Library
- 13.N. Linial. E. London, and Y. Rabinovich. The geometry of graphs and some of its algorithmic applications. In Foundations of Computer Science, pages 577-591, 1994.Google Scholar
- 14.13. R. Moon. A hybrid genetic search for standard-cell circuit placement problem, ongoing.Google Scholar
- 15.N. Radcliffe. Equivalence class analysis of genetic algorithms. Complex Systems, 5:183-205, 1991.Google Scholar
- 16.x,V. Sun and C. Sechen. Efficient and effective placement for very large circuits. {EEE Trans. on Computer-Aided Design. 14(3):349-359, 1995. Google ScholarDigital Library
- 17.G. $yswerda. Uniform crossover in genetic algorithms. In Third International Conference on Genetic Algorithms, pages 2-9, 1989. Google ScholarDigital Library
- 18.Y. \Vet and C.K. Cheng. Ratio cut partitioning for hierarchical designs. ~EEE Trans. on Computer'.Aided Design, 10(7):911-921. 1991.Google Scholar
- 19.D. XVt~itley and J. Kauth. Gertitor: A different genetic algorithm. In Rocky Mountain Conference on Artificial Intelligence. pages 118-130. 1988.Google Scholar
Index Terms
- Genetic VLSI circuit partitioning with two-dimensional geographic crossover and zigzag mapping
Recommendations
Crossover Consideration in Genetic Algorithm
ICMLSC '24: Proceedings of the 2024 8th International Conference on Machine Learning and Soft ComputingCrossover is an important process in genetic algorithms. This process will swap genes between the chromosomes of the parents. The results from the crossover process may not be better than those of the parents, which affect the result of the genetic ...
A hybrid genetic algorithm for a variant of two-dimensional packing problem
GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computationA variant of two-dimensional packing problem was given in the GECCO'2008 competition. This paper describes the genetic algorithm that produced the best result and thus won the No. 1 prize. As the problem is naturally represented by a two-dimensional ...
A new crossover mechanism for genetic algorithms with variable-length chromosomes for path optimization problems
A new crossover mechanism containing two operators is proposed.The new mechanism produces more cross point pairs than same point crossover.Our novel genetic algorithm has a desirable performance. Genetic Algorithm (GA) has found wide application in path ...
Comments