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Using Evolutionary Computation as a Creativity-Support Tool

Pilot. Using Evolutionary Computation as a Creativity-Support Tool. NSF CreativeIT Workshop, January 2009. Tim Chabuk University of Maryland Jason Lohn Carnegie Mellon University Derek Linden X5 Systems Jim Reggia University of Maryland. Example: Self-Replicating Machines.

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Using Evolutionary Computation as a Creativity-Support Tool

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  1. Pilot Using Evolutionary Computationas a Creativity-Support Tool NSF CreativeIT Workshop, January 2009 Tim Chabuk University of Maryland Jason Lohn Carnegie Mellon University Derek Linden X5 Systems Jim Reggia University of Maryland

  2. Example: Self-Replicating Machines • artificial structures/systems that produce copy of themselves • why study? • - insight into biological replication - understanding origins of life - understanding principles/algorithms - potential applications • • Game of Life • historically, most work done with cellular automata • discrete space of cells, discrete time • local, parallel computations A B B C Rule set: A . B C .  B etc. t t+1

  3. Replica under construction Construction arm Construction control Tape control tape Two Primary Classes of Replicators John von Neumann (1950’s) - 29 state cells - universal constructor computer - complicated (~100k cells, 100k rules) Chris Langton (1980’s) - 8 state cells - self-replicating loop - simpler, implemented

  4. Replication of Loop t = 3 t = 6 t = 80 t = 115 t = 150 search for simplest, emergence from “primordial soup”, simultaneous tasks, etc.

  5. A Half Century of Cellular Replicators • hand-crafted rule sets • restricted to these two broad classes Question: Is automated discovery of novel self-replicating structures possible? Approach: - given an arbitrary initial configuration - use genetic programming to evolve needed rules Pan Z, Reggia J, Gao D. Properties of Self-Replicating Cellular Automata Systems Discovered Using Genetic Programming, Advances in Complex Systems, 10 (Suppl. 1), 2007, 61-84.

  6. Evolved Replicators evolved rule tree initial structure GP ran quickly on standard PC but how do the resulting replicators work?

  7. Resulting Replicator Behavior • growth, separation of replicants • fastest CA replicant ever reported t = 0 t = 1 t = 2

  8. Another Initial Structure debris

  9. larger, repetitive components  whole new family of self-replicating configurations

  10. Other Structures? Evolves same rule table as that created previously by people?   Replicator Factory: - creates rules for arbitrary configurations to replicate - rule sets are parsimonious and fast - novel replication process relative to past manual approaches - used to study properties of replicators

  11. Pilot: Causally-Guided Evolutionary Creativity • causality in human creativity • Goal: guide EC in part using problem specific causal relations • Process:1. specify problem-specific causal relations • 2. integrate causal influences on evolutionary process • 3. validate approach • Progress to date: • - antenna array design as target task • - derivation of causal relations • - causal reasoning algorithm • - integrated with evolutionary process • - initial experimentation

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