Performance of genetic algorithms and simulated annealing in the economic optimization of a herd dynamics model

This study focuses on replicated exploratory optimizations of a large and d ifficult beef herd dynamics model, using the net present value over a 10-ye ar planning horizon as the variable of interest. Faced with a practical sea rch-space of the order of 10(100) possible management decision combinations , the thorough but slow search pattern of simulated annealing struggled, on average failing 1.2% short of the global optimum of the system. By compari son, the cross-breeding and mutating nature of the genetic algorithm search es usually produced good results, averaging 0.1% from the global optimum. A lso, these were achieved with about half the computing time used by the sim ulated annealing optimizations. Hence, for this problem, genetic algorithms proved the superior method. (C) 1999 Elsevier Science Ltd.