Pearl Y. Wang

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We undertake an empirical study in which the performance of a good genetic algorithm is tested against some well-known bin packing heuristics on a range of two-dimensional bin packing problems. We restrict our study to problems involving guillotine patterns which are produced using a series of vertical and horizontal edge–to–edge cuts. Many applications of(More)
We present a genetic algorithm (GA) that uses a slicing tree construction process for the placement and area optimization of soft modules in very large scale integration floorplan design. We have overcome the serious representational problems usually associated with encoding slicing floorplans into GAs, and have obtained excellent (often optimal) results(More)
This report describes a recursive process for generating data sets of rigid rectangles that can be placed into rectangular regions with zero waste. The generation procedure can be modified to guarantee that the aspect and area ratios of the rectangles in the generated data sets satisfy user-specified parameters. This recursive process can thus be employed(More)
Summary form only given. This paper describes the development of a fine-grained meta-heuristic for solving large strip packing problems with guillotine layouts. An architecture-adaptive environment aCe, and the aCe C parallel programming language are used to implement a massively parallel genetic simulated annealing (GSA) algorithm. The parallel GSA(More)
This paper describes a parallel approximation algorithm that can be used to obtain solutions to the one-dimensional bin packing problem: a list L of n items with sizes in the interval (0, I] is to be packed into a minimum number of unit-size bins. The algorithm is based on a systolic model of computation and packs items into one-dimensional bins by dividing(More)