Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
Advances in genetic programming
Advances in genetic programming
A new algorithm for floorplan design
DAC '86 Proceedings of the 23rd ACM/IEEE Design Automation Conference
Genetic Optimization Using A Penalty Function
Proceedings of the 5th International Conference on Genetic Algorithms
A Study of Genetic Algorithm Hybrids for Facility Layout Problems
Proceedings of the 6th International Conference on Genetic Algorithms
DAC '82 Proceedings of the 19th Design Automation Conference
Evolutionary approaches to the design and organization of manufacturing systems
Computers and Industrial Engineering
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
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This research applies techniques and tools from Genetic Programming (GP) to the facility layout problem. The facility layout problem (FLP) is an NP-complete combinatorial optimization problem that has applications to efficient facility design for manufacturing and service industries. A facility layout is represented as a collection of rectangular blocks using a slicing tree structure (STS). We use a multiple purpose genetic programming kernel to generate slicing trees that are converted into candidate solutions for an FLP. The utility of our techniques is established using eight previously published benchmark problems. Our genetic programming techniques that evolve STSs are more natural and more flexible than all of the previously published genetic algorithm and simulated annealing techniques. Previous genetic algorithm techniques use a two-phase optimization strategy. The first phase uses clustering techniques to determine a near optimal fixed tree structure that is represented as a chromosome in a genetic algorithm. Within the constraints implied by the fixed tree structure, genetic algorithm techniques are applied during the second phase to optimize the placement of facilities in relation to each other. Our genetic programming technique is a single phase global optimization strategy using an unconstrained tree structure. This yields superior results.