Genetic Programming IV: Routine Human-Competitive Machine Intelligence
Genetic Programming IV: Routine Human-Competitive Machine Intelligence
Automatic design synthesis and optimization of component-based systems by evolutionary algorithms
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Stochastic ranking for constrained evolutionary optimization
IEEE Transactions on Evolutionary Computation
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The synthesis of novel heating, ventilating, and air-conditioning (HVAC), system configurations is a mixed-integer, non-linear, highly constrained, multi-modal, optimization problem, with many of the constraints being subject to time-varying boundary conditions on the system operation. It was observed that the highly constrained nature of the problem resulted in the dominance of the search by a single topology. This paper, introduces an new evolutionary algorithm operator that prevents topology dominance by penalizing solutions that have a dominant topology.The operator results in a range of dynamic behavior for the rates of growth in topology dominance. Similarly, the application of the ageing penalty can result in the attenuation of topology dominance, or more severely, the complete removal of a topology from the search. It was also observed that following the penalization of a dominant topology, the search was dynamically re-seeded with both new and previously evaluated topologies. It is concluded that the operator prevents topology dominance and increases the exploratory power of the algorithm.The application of an evolutionary algorithm with ageing to the synthesis of HVAC system configurations resulted in a novel design solution having a 15% lower energy use than the best of conventional system designs.