Genetic Algorithm and Its Application in Layout Problems
GA plus slicing tree is also widely developed in VLSI. Nallasamy mani, et al.1997 describes a combined genetic algorithm and slicing approach for floorplan area optimization during the early stage of integrated circuit design. It applies a partition procedure to reduce the complexity routing problem.
Genetic Algorithm (GA) is wide applied in almost any field, including solving FLP. Tam 1992 introduced the coding of layouts as a string of characters of finite length and used a fixed slicing tree structure defined by a clustering algorithm to represent a layout as a chromosome of string of characters. Its improvement is presented in Tam 1998 with a parallel GA approach in terms of schema coding and solution method. It relaxes the assumption of a fixed slicing tree structure by coding the structure, internal and external nodes of a tree as substrings in the schema. Based on the application limitation of the classical crossover and mutation operators of Tam 1998, L. Al-Hakim2000 introduced a preserving operation, referred to as transplanting, to produce feasible offspring. It also discusses the improvement of each of the GA development procedures with comparison with Tam1998.
Though the use of GA has gained popularity with the application of slicing tree structure for layout problems, most implementations require repairing procedures to ensure the legality of the chromosome representations of the layout after application of genetic operators. To overcome this limitation, E. SHAYAN 2004 reported the design and development results of a new GA named GA.FLP.STS producing legal chromosomes without any need for repairing procedures. It introduced a penalty system to facilitate generating facilities with acceptable dimensions.
With the popularity and maturity of GA with slicing tree structure, more researches focus into the different aspects of FLP solutions. Kyu-Yeul Lee2002 and 2005 proposed a hybrid GA to derive solutions for facility layouts with inner walls and
Genetic Algorithm (GA) is wide applied in almost any field, including solving FLP. Tam 1992 introduced the coding of layouts as a string of characters of finite length and used a fixed slicing tree structure defined by a clustering algorithm to represent a layout as a chromosome of string of characters. Its improvement is presented in Tam 1998 with a parallel GA approach in terms of schema coding and solution method. It relaxes the assumption of a fixed slicing tree structure by coding the structure, internal and external nodes of a tree as substrings in the schema. Based on the application limitation of the classical crossover and mutation operators of Tam 1998, L. Al-Hakim2000 introduced a preserving operation, referred to as transplanting, to produce feasible offspring. It also discusses the improvement of each of the GA development procedures with comparison with Tam1998.
Though the use of GA has gained popularity with the application of slicing tree structure for layout problems, most implementations require repairing procedures to ensure the legality of the chromosome representations of the layout after application of genetic operators. To overcome this limitation, E. SHAYAN 2004 reported the design and development results of a new GA named GA.FLP.STS producing legal chromosomes without any need for repairing procedures. It introduced a penalty system to facilitate generating facilities with acceptable dimensions.
With the popularity and maturity of GA with slicing tree structure, more researches focus into the different aspects of FLP solutions. Kyu-Yeul Lee2002 and 2005 proposed a hybrid GA to derive solutions for facility layouts with inner walls and