PI SELVA'S LAB INCLUDING FORMER PHD STUDENT AND NSTRF FELLOW NOZOMI HITOMI AND CURRENT PHD STUDENTS PRACHI DUTTA AND PAU GARCIA BUZZI HAVE DONE SUBSTANTIAL WORK TO IMPROVE THE COMPUTATIONAL EFFICIENCY OF TRADE-SPACE EXPLORATION TOOLS THAT ASSIST IN THE DESIGN AND ARCHITECTING OF DISTRIBUTED SATELLITE SYSTEMS [1] [2] [11] [16] [3] [10]. ONE LINE OF WORK THAT IS RELEVANT TO THIS PROPOSAL HAS FOCUSED ON MULTIOBJECTIVE EVOLUTIONARY ALGORITHMS (MOEA) WHICH ARE TYPICALLY USED TO OPTIMIZE PROBLEMS WITH MULTIPLE INCOMMENSURABLE OBJECTIVES AND NON-LINEAR NON-CONVEX AND NON-DIFFERENTIABLE PROPERTIES. A CHALLENGE IN MOEA AND GLOBAL OPTIMIZATION IN GENERAL IS THE TRADE-OFF BETWEEN EXPLORATION AND EXPLOITATION: HOW TO OPTIMALLY ASSIGN COMPUTING RESOURCES TO THE CONFLICTING OBJECTIVES OF DISCOVERING NEW PROMISING AREAS WHERE THE GLOBAL OPTIMUM MIGHT BE (EXPLORATION) AND SEARCHING IN THOSE AREAS TO FIND THE LOCAL OPTIMA (EXPLOITATION). ANOTHER RELATED CHALLENGE IS HOW TO INCORPORATE EXPERT KNOWLEDGE INTO THE OPTIMIZATION. EXPERT MISSION DESIGNERS MAY FOR EXAMPLE KNOW THAT PACKING SEVERAL HIGH-ENERGY INSTRUMENTS INTO A SINGLE SPACECRAFT IS GENERALLY NOT A GOOD IDEA OR THAT CERTAIN ORBITS (E.G. AFTERNOON SUN-SYNCHRONOUS ORBIT) ARE BETTER FOR CERTAIN INSTRUMENTS AND APPLICATIONS (E.G. ATMOSPHERIC CHEMISTRY SPECTROMETERS) THAN OTHERS. HOWEVER USING THIS KNOWLEDGE BY ENCODING IT FOR EXAMPLE IN HARD CONSTRAINTS MAY ACTUALLY DECREASE PERFORMANCE IF KNOWLEDGE IS NOT ACCURATE OR GENERAL ENOUGH DUE TO A LACK OF EXPLORATION. OUR ALGORITHMS OUTPERFORM THE STATE-OF-THE-ART BECAUSE THEY CAN USE AVAILABLE INFORMATION INCLUDING BOTH EXPERT KNOWLEDGE AND KNOWLEDGE LEARNED DURING THE OPTIMIZATION TO IMPROVE EXPLOITATION IN A WAY THAT DOESN T REDUCE EXPLORATION AS MUCH AS OTHER APPROACHES (E.G. CONSTRAINTS) DO. SPECIFICALLY ADVANCEMENTS HAVE BEEN MADE ON THREE FRONTS: 1) HOW TO BEST INCORPORATE EXPERT KNOWLEDGE TO EFFECTIVELY GUIDE THE SEARCH TOWARDS PROMISING REGIONS OF THE TRADE-SPACE 2) HOW TO MAKE OPTIMAL EXPLORATION-EXPLOITATION DECISIONS THROUGH ADAPTIVE OPERATOR SELECTION STRATEGIES (AOS) AND 3) HOW TO EXTRACT NEW KNOWLEDGE BY APPLYING DATA MINING DURING THE OPTIMIZATION PROCESS (KNOWLEDGEDRIVEN OPTIMIZATION OR KDO). FIGURE 1 SHOWS A FLOWCHART OF THE FRAMEWORK DEVELOPED BY SELVA'S GROUP.