Evolutionary and adaptive synthesis methods
Formal engineering design synthesis
Constrained two dimensional bin packing using a genetic algorithm
Recent advances in intelligent paradigms and applications
Computer Networks: The International Journal of Computer and Telecommunications Networking
Incorporating fuzzy knowledge into fitness: multiobjective evolutionary 3D design of process plants
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Self-regulatory hierarchical coevolution
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Integrated Computer-Aided Engineering
Dynamic programming decision path encoding of genetic algorithms for production allocation problems
Computers and Industrial Engineering
Assigning cells to switches in cellular mobile networks: a comparative study
Computer Communications
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When solving real-world problems, often the main task is to find a proper representation for the candidate solutions. Strings of elementary data types with standard genetic operators may tend to create infeasible individuals during the search because of the discrete and often constrained search space. This article introduces a generally applicable representation for 2D combinatorial placement and packing problems. Empirical results are presented for two constrained placement problems, the facility layout problem and the generation of VLSI macro-cell layouts. For multiobjective optimization problems, common approaches often deal with the different objectives in different phases and thus are unable to efficiently solve the global problem. Due to a tree structured genotype representation and hybrid, problem-specific operators, the proposed approach is able to deal with different constraints and objectives in one optimization step