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Product Evolution : Computer-aided Recombinant Design by Customer-driven Natural Selection

This meeting discusses the concept of evolving new products through computer-aided recombinant design and customer-driven natural selection. The IDEA (Interactive Design by Evolutionary Algorithms) approach allows for the featurization of designs into genes, which are then exposed to natural selection through customer interactions on the web. Through multiple generations, preferences are extracted, resulting in optimal designs and innovative insights.

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Product Evolution : Computer-aided Recombinant Design by Customer-driven Natural Selection

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  1. MIT Media Lab / The Center For Bits and Atoms Meeting on Emergent Engineering October 16, 2002 Product Evolution: Computer-aided Recombinant Design by Customer-driven Natural Selection Kamal Malek Noubar Afeyan

  2. Evolving New Products “Featurize “/ code the product as a set of genes Step 2 Create Recombinant Designs and Expose them to “Natural Selection” Allow customers to interact and choose over the web Step 3 Evolve the “Fittest” Customers/ developers select, refine to produce optimal designs Step 1 Sequence the Product Genome

  3. So What’s the IDEA? • IDEA: Interactive Design by Evolutionary Algorithms (patents pending) • Featurize design into genes and define alleles • Consumer votes through web on relative appeal of recombinant designs • Preferences extracted through multiple generations • Collective action – segmentation – preference extraction • Results: Designs, Insights, Affinity, Innovation

  4. IDEA: Interactive Design by Evolutionary Algorithms Recombinant Design A set of design direction candidates and a description of the underlying design intent Featurize candidates extracting significant design features and attributes, establishing an allowable range of variation, and identifying design constraints This “featurization” is encoded into the design genotype in a way that enables new design candidates to be generated automatically within the vast design universe defined around the stated design intent

  5. Evolutionary Algorithms Evolve the “Fittest” Designs Evolutionary Algorithm Initial Design Population Fitness Assessment Fitness Weighted Breeding Mutations More Fit Population

  6. Discovery of preferred product designs and market segments 10 Features of 10 Options Potential Design Population: 10 Billion Designs Consumer Population 5,000 Heterogeneous Users

  7. PRODUCTS PACKAGING PROMOTIONAL DESIGN CONCEPT TESTING PRINT MEDIA Examples

  8. Logo Background Model Perfume Bottle Ad Layout

  9. Crossover

  10. Mutation

  11. 1,000+ Participants Explored A Design Space Rendered In Real-Time Potential Design Universe: Over 340,000+ Possible Advertisements!

  12. 100% 3 4 1 5 2 1,000+ Participants Converged on 5 Major Design Themes from a Design Universe of 340,000+

  13. Summary • First steps toward evolving products through computation, customer selection and web-based collectivism • Insights into segmentation • Enabling to designers, market researchers as well as product management

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