product evolution: computer-aided recombinant design by customer-driven natural selection kamal...

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

Kamal Malek Noubar Afeyan

MIT Media Lab / The Center For Bits and Atoms

Meeting on Emergent Engineering

October 16, 2002

Confidential – Do not Duplicate

Evolving New Products

Step 1Sequence the

Product Genome

“Featurize “/ code the product as a set of genes

Step 2Create

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


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


IDEA: Interactive Design by Evolutionary Algorithms

A set of design A set of design direction direction candidates and a candidates and a description of the description of the underlying design underlying design intentintent

Featurize Featurize candidates candidates extracting significant extracting significant design features and design features and attributes, attributes, establishing an establishing an allowable range of allowable range of variationvariation, and , and identifying identifying design design constraintsconstraints

This “featurization” is This “featurization” is encoded into the encoded into the design design genotypegenotype in a way that in a way that enables new enables new design design candidatescandidates to be to be generated automatically generated automatically within the vast design within the vast design universe defined around universe defined around the stated design intentthe stated design intent

Recombinant Design


Evolutionary Algorithms Evolve the “Fittest” Designs

Initial Design PopulationInitial Design Population

Evolutionary AlgorithmEvolutionary Algorithm

Fitness AssessmentFitness Assessment

Fitness WeightedFitness WeightedBreedingBreeding

MutationsMutations

More Fit PopulationMore Fit Population


10 Features of 10 Options Potential Design Population: 10 Billion Designs

Discovery of preferred product designs and market segments

Consumer Population5,000 Heterogeneous Users


Examples

PACKAGING

CONCEPTTESTING

PROMOTIONALDESIGN

PRINT MEDIA

PRODUCTS


Model

Logo

Background

Perfume Bottle

Ad Layout


Crossover


Mutation


1,000+ Participants Explored A Design Space Rendered In Real-Time

Potential Design Universe:Over 340,000+

Possible Advertisements!


1

2

5

4

3

1,000+ Participants Converged on 5 Major Design Themes from a Design Universe of 340,000+

100%


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

product evolution: computer-aided recombinant design by customer-driven natural selection kamal...

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