In this presentation, we talk about creativity from a psychological point of view, and how genetic algorithms can be useful to explain creativity.

1. GENETIC ALGORITHMS AS A
MODEL OF HUMAN CREATIVITY?
JOHN L. DENNIS
In collaboration with Aldo Stella,
Rocco Catrone, Megan Foster,
Megan Larigan, & Kara Shidlofsky
AISC 2011 Conference

2. WHAT IS CREATIVITY?
an object, idea, behavior
that has the joint property
of being useful as well as novel.

3. IF I WERE:
▶ A painter – colors, light, composition, etc.
▶ A biologist – cells, life forms, diseases, etc.
▶ A physicist – atoms, forces, relationships, etc.

4. IF I WERE:
▶ A painter – colors, light, composition, etc.
▶ A biologist – cells, life forms, diseases, etc.
▶ A physicist – atoms, forces, relationships, etc.
▶ BUT, I’m a psychologist with a cognitive science/evolutionary
psychology background and a philosophical training.

5. IF I WERE:
▶ A painter – colors, light, composition, etc.
▶ A biologist – cells, life forms, diseases, etc.
▶ A physicist – atoms, forces, relationships, etc.
▶ BUT, I’m a psychologist with a cognitive science/evolutionary
psychology background and a philosophical training.
▶ So, what do I use to explain creativity?

6. IF I WERE:
▶ A painter – colors, light, composition, etc.
▶ A biologist – cells, life forms, diseases, etc.
▶ A physicist – atoms, forces, relationships, etc.
▶ BUT, I’m a psychologist with a cognitive science/evolutionary
psychology background and a philosophical training.
▶ So, what do I use to explain creativity?
GENETIC ALGORITHMS

7. HIERARCHICAL ORGANIZATION
▶ Science as organized hierarchically.
PHYSICS
BIOLOGY
SOCIAL SCIENCES

8. HIERARCHICAL ORGANIZATION
▶ Science as organized hierarchically.
PHYSICS
BIOLOGY
SOCIAL SCIENCES
▶ Why care about hierarchical ordering?
Because the social sciences, and particular
psychology, is in desperate need of valid theories.

9. HOW CAN WE DO THIS?
▶ Start with a core idea.

10. HOW CAN WE DO THIS?
▶ Start with a core idea.
▶ Develop a substantial body of observations.

11. HOW CAN WE DO THIS?
▶ Start with a core idea.
▶ Develop a substantial body of observations.
▶ Rely on introspection.

12. HOW CAN WE DO THIS?
▶ Start with a core idea.
▶ Develop a substantial body of observations.
▶ Rely on introspection.
▶ Identify causal mechanisms.

13. HOW CAN WE DO THIS?
▶ Start with a core idea.
▶ Develop a substantial body of observations.
▶ Rely on introspection.
▶ Identify causal mechanisms.
▶ Experimentation.

14. HOW CAN WE DO THIS?
▶ Start with a core idea.
▶ Develop a substantial body of observations.
▶ Rely on introspection.
▶ Identify causal mechanisms.
▶ Experimentation.
▶ Link concepts/theories.

15. HOW CAN WE DO THIS?
▶ Start with a core idea.
▶ Develop a substantial body of observations.
▶ Rely on introspection.
▶ Identify causal mechanisms.
▶ Experimentation.
▶ Link concepts/theories.
▶ Integrate findings.

16. HOW CAN WE DO THIS?
▶ Start with a core idea.
▶ Develop a substantial body of observations.
▶ Rely on introspection.
▶ Identify causal mechanisms.
▶ Experimentation.
▶ Link concepts/theories.
▶ Integrate findings.
▶ Finding boundary conditions.

17. SO, WHY GENETIC ALGORITHMS?
▶ Bain, Senses and the Intellect:
"the greatest inventions are so dependent on chances that the
only hope of success is to multiply the chances.”

18. SO, WHY GENETIC ALGORITHMS?
▶ Bain, Senses and the Intellect:
"the greatest inventions are so dependent on chances that the
only hope of success is to multiply the chances.”
▶ What does this tell us?
coming up with solutions is an inductive approach that involves
chance encounters between elements

19. SO, WHY GENETIC ALGORITHMS?
▶ Bain, Senses and the Intellect:
"the greatest inventions are so dependent on chances that the
only hope of success is to multiply the chances.”
▶ What does this tell us?
coming up with solutions is an inductive approach that involves
chance encounters between elements
▶ Why should we care?
need creators who actively seek chance encounters.
need to help creators develop an environment where chance
encounters can occur.

20. SO, WHY GENETIC ALGORITHMS?
▶ Computational model produces chance
encounters.

21. SO, WHY GENETIC ALGORITHMS?
▶ Computational model produces chance
encounters.
▶ Insight with what helps make creators.

22. SO, WHY GENETIC ALGORITHMS?
▶ Computational model produces chance
encounters.
▶ Insight with what helps make creators.
▶ Possible model for human creativity.

23. GENETIC ALGORITHMS
process of arriving at a thought as a
selection among possible methods for
varying thought to generate subsequent
thought.

24. GENETIC ALGORITHMS
process of arriving at a thought as a
selection among possible methods for
varying thought to generate subsequent
thought.
envisioning thought as being “blind” or
goal-less.

25. BUT THERE IS A PROBLEM!
▶ Ideational variation is NOT thought of as blind

26. BUT THERE IS A PROBLEM!
▶ Ideational variation is NOT thought of as blind
▶ Rather thought of as be sighted, directed and
goal directed.

27. BUT THERE IS A PROBLEM!
▶ Ideational variation is NOT thought of as blind
▶ Rather thought of as be sighted, directed and
goal directed.
▶ There is a better way:
continuum between absolute sightedness and
absolute blindness.

28. WHAT DO GAS TELL US?
▶ Developmental differences

29. WHAT DO GAS TELL US?
▶ Developmental differences
▶ Bilingualism and/or multiculturalism.

30. WHAT DO GAS TELL US?
▶ Developmental differences
▶ Bilingualism and/or multiculturalism.
▶ Less formal education.

31. WHAT DO GAS TELL US?
▶ Developmental differences
▶ Bilingualism and/or multiculturalism.
▶ Less formal education.
▶ Exposure to unconventional or unstable
environments.

32. WHAT DO GAS TELL US?
▶ Developmental differences
▶ Bilingualism and/or multiculturalism.
▶ Less formal education
▶ Exposure to unconventional or unstable
environments.
▶ More heterogeneous mentors.

33. WHAT DO GAS TELL US?
▶ Individual differences

34. WHAT DO GAS TELL US?
▶ Individual differences
▶ Nonconformity.

35. WHAT DO GAS TELL US?
▶ Individual differences
▶ Nonconformity.
▶ Divergent thinking.

36. WHAT DO GAS TELL US?
▶ Individual differences
▶ Nonconformity.
▶ Divergent thinking.
▶ Openness to experience

37. WHAT DO GAS TELL US?
▶ Individual differences
▶ Nonconformity.
▶ Divergent thinking.
▶ Openness to experience
▶ Diverse interests.

38. WHAT DO GAS TELL US?
▶ Individual differences
▶ Nonconformity.
▶ Divergent thinking.
▶ Openness to experience
▶ Diverse interests.
▶ Subclinical DSM psychopathological traits.

39. WHAT DO GAS TELL US?
▶ Think in a non-schematic way.

40. WHAT DO GAS TELL US?
▶ Think in a non-schematic way.
▶ Keep possible solutions available for future
consideration.

41. WHAT DO GAS TELL US?
▶ Think in a non-schematic way.
▶ Keep possible solutions available for future
consideration.
▶ Non-filter information proceeding by trial and
error.

42. WHAT DO GAS TELL US?
▶ Think in a non-schematic way.
▶ Keep possible solutions available for future
consideration.
▶ Non-filter information proceeding by trial and
error.
▶ Store a large number of examples for later
combination.

43. GRAZIE
THANK YOU