How to solve it: modern heuristics
How to solve it: modern heuristics
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Multiobjective Satisfaction within an Interactive Evolutionary Design Environment
Evolutionary Computation
Real-coded genetic algorithm for parametric modelling of a TRMS
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
An interactive genetic algorithm based on improved sharing mechanism for automobile modeling design
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part I
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While interactive evolutionary computation (IEC) is starting to penetrate a larger scientific community, only few researchers have applied IEC to the design of complicated artifacts like machines or transportation systems. The present paper introduces a specific approach to interactive evolutionary computation that breaches the two historical categories of user-defined fitness and selection in each generation (narrow) and occasional user-intervention of an automated evolutionary process to correct the fitness function used for (multi-objective) optimization (broad). To highlight the approach, a real world aircraft design problem is employed that demonstrates the relevance and importance of both features for an effective design process.