Center for Evolutionary Computation and Automated Design Rich Terrile Symposium on Complex Systems Engineering Rand Corp. January 11, 2007 Rich Terrile Symposium on Complex Systems Engineering Rand Corp. January 11, 2007

Design Rules and Complexity Complexity Formalism of Design Rules Hardware Software Nature Industry Evolutionary Computation Effort Sandstone to Cities Hardware Design Rules Large Library of Codified Design Rules Based on Mathematics and Experience Design Rules Based on Knowledge of Physical Laws Physics, Chemistry, Material Science, etc. Standards and Tools Results: Cities, Aircraft and Computers Code to Operating Systems Software Design Rules Design Rules Individually or Institutionally Derived Partially-Codified Design Rules Based on Individual Experience Results: Windows, Air Traffic Control, Nasdaq ? Nature’s Design Rules Molecules to Minds Begin with an Information System Rule 1: Random Variation Rule 2: Selection Repeat Results: Life, the Human Brain and Mind Evolutionary Computation

What is it? A method that operates on a population of existing computational-based engineering models (or simulators) and competes them using biologically inspired genetic operators on large parallel cluster computers. The result is the ability to automatically find design optimizations and trades, and thereby greatly amplify the role of the system engineer. Existing Computer Models (CAD) Evolutionary Framework High-End Cluster Computers ++

Evolutionary Computation What does it do? We have demonstrated that complex engineering and science models can be automatically inverted by incorporating them into evolutionary frameworks and that these inversions have advantages over conventional searches by not requiring expert starting guesses (designs) and by running on large cluster computers with less overall computational time than conventional approaches. What have we already done? Demonstrated feasibility, applicability and advantage of evolutionary computational techniques to JPL related engineering design problems in at least 7 distinct and diverse areas. Created a team that can quickly apply this technology to new engineering and science problems.

Science & Technology Center Evolutionary Computation and Automated Design Portfolio of Human Competitive Successes Power System Design MEMS Gyro Tuning Low Thrust Trajectory Optimization Robotic Arm Path Planning Overview Scheduling & Mission PlanningAutomatic Spectral RetrievalAvionics Architecture Design

Automated Design of Spacecraft Systems Power Sub-System Results MMPAT - Multi-mission Power Analysis Tool –MER surface activity plan (90 sols on Mars surface) –Deep Impact (DI) comet flyby activity plan (8.3 month AU cruise) Initial Results using Evolutionary Framework –Started with random design parameters –20,000 evaluations of MMPAT for MER (14,650 for DI) –Complete trade study with 7 design options in less than one hour on JPL institutional cluster –MER and DI designs for same performance are within 10% of flown designs with lower cost and mass for MER (lower cost for DI) –Compares with JPL team of experienced domain experts requiring 1-2 weeks to generate a credible pre-award mission concept. –Redesign time is less than one hour for complete trade study

Modified from: H. Moravec (1999)Terrile 8/14/01 Turing HanrattyCAD/CAM Holland CAD Development Evol Comp Development

Design Single design based on expertise of human designer + Evolutionary Framework Computational Model Competes a population of variable input parameters over many generations Predicts and evaluates the outcome (design) of variable sets of input parameters Computer Aided Design (CAD) Allows rapid exploration of alternative designs by human designer + Computer Optimized Design (COD) Allows automatic exploration and optimization of designs over huge volumes of design space Traditional Design Allows only one point of design space to be examined Elements of Computer Optimized Design Evolutionary Computation

Multi-Mission Spacecraft Analysis Tools Coupled to Evolutionary Framework Amplify the ability of a system engineer to find optimum designs and optimum trades Automatic Optimization of Design Fitness (first-order trades) Cost Mass Performance Trade Study Analysis Population of solutions at various requirements levels Rapid Re-Design Optimization of Design Fitness Landscape (second-order trades) Margins Risk/Safety Failure analysis Visualization of performance fall-off