Abstract: Interactive physical programming is based on physical programming，a new effective and computationally efficient approach for multidisciplinary design optimization．It takes into account the designer’s or the decision maker’s (DM’s) prefer-ences during the optimization process，and allows for design exploration at a given Pareto design．The approximate model of Pareto surface at a given Pareto design is developed using neural networks for design exploration．At the given Pareto design，a group of Pareto designs are generated randomly．They’re brought to the decision maker using a Pareto visualization tool，and are evaluated with both qualitative and quantitative analysis．A map from Pareto designs to their corresponding evaluation value is established using a neural networks model，it illustrates the decision maker’s local preference at the given Pareto design．Then genetic algorithms is used in optimization to find the Pareto design which mostly accords with the decision maker’s local preference．The obtained Pareto design is used as the aspiration point in compromise programming，and the final design solution can be obtained．

Key words: Compromise programming, Genetic algorithms, Interactive physical programming, Neural networks, Interactive design, Multiobjective optimization, Physical programming