Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Numerical Optimization of Computer Models
Numerical Optimization of Computer Models
Applied Intelligence
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Evolutionary algorithms in multiply-specified engineering. The MOEAs and WCES strategies
Advanced Engineering Informatics
Advanced Engineering Informatics
IEEE Transactions on Evolutionary Computation - Special issue on computational finance and economics
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This paper describes the application of evolution strategies to the design of interacting multiple model (IMM) tracking filters in order to fulfill a large table of performance specifications. These specifications define the desired filter performance in a thorough set of selected test scenarios, for different figures of merit and input conditions, imposing hundreds of performance goals. The design problem is stated as a numeric search in the filter parameters space to attain all specifications or at least minimize, in a compromise, the excess over some specifications as much as possible, applying global optimization techniques coming from evolutionary computation field. Besides, a new methodology is proposed to integrate specifications in a fitness function able to effectively guide the search to suitable solutions. The method has been applied to the design of an IMM tracker for a real-world civil air traffic control application: the accomplishment of specifications defined for the future European ARTAS system.