This document discusses biologically inspired design and evolutionary computation. It outlines examples of biomimicry in engineering and proposes that evolutionary algorithms could be used to optimize design problems. The document then examines characteristics of biological evolution like selection, mutation, and recombination that have translated to algorithms like genetic algorithms. It concludes by considering adding more biological complexity like genetic structure and development to evolutionary design methods.

1. Biologically inspired design of design
Ben Bolker
Departments of Mathematics & Statistics and Biology, McMaster University
19 December 2010
Ben Bolker (Departments of Mathematics & Statistics and Biology, McMaster University)
Evolutionary computation 19 December 2010 1 / 20

2. 1 Introduction
2 Biologically inspired optimization
3 Avenues for exploration/conclusions
Ben Bolker (Departments of Mathematics & Statistics and Biology, McMaster University)
Evolutionary computation 19 December 2010 2 / 20

3. Outline
1 Introduction
2 Biologically inspired optimization
3 Avenues for exploration/conclusions
Ben Bolker (Departments of Mathematics & Statistics and Biology, McMaster University)
Evolutionary computation 19 December 2010 3 / 20

4. Biologically inspired design
Examples:
micro/macro fluid dynamics:
kingfisher beaks, robot fish, sharkskin
materials (Velcro, gecko toes)
structural color
Some references:
http://www.japanfs.org/en_/newsletter/200503-2.html,
http://www.treehugger.com
http://brainz.org/15-coolest-cases-biomimicry/
Ben Bolker (Departments of Mathematics & Statistics and Biology, McMaster University)
Evolutionary computation 19 December 2010 4 / 20

5. Evolutionary computation
Biologically inspired design of design:
i.e., biologically inspired algorithms
Can we learn from evolutionary biology? How?
Generative systems (Genr8, Maya, Rhino . . . )
Ben Bolker (Departments of Mathematics & Statistics and Biology, McMaster University)
Evolutionary computation 19 December 2010 5 / 20

6. Evolutionary computation
Biologically inspired design of design:
i.e., biologically inspired algorithms
Can we learn from evolutionary biology? How?
Generative systems (Genr8, Maya, Rhino . . . )
Ben Bolker (Departments of Mathematics & Statistics and Biology, McMaster University)
Evolutionary computation 19 December 2010 5 / 20

7. Problem: bridge design
http://imac.epfl.ch/Team/landolf/Rhode%20et%20al%20EG-ICE%2009.pdf
objective function: cost, performance
parameter space: area of layer and x-cables; outer diameter,
diameter-to-thickness ratio of tubular struts; self-stress of layer and
x-cables
Ben Bolker (Departments of Mathematics & Statistics and Biology, McMaster University)
Evolutionary computation 19 December 2010 6 / 20

8. Problem: patio design
Caldas (2008) doi:10.1016/j.aei.2007.08.012
objective function: (?)
parameter space: which sides have balconies
(24 possibilities, encoded as a bit string): discrete
Ben Bolker (Departments of Mathematics & Statistics and Biology, McMaster University)
Evolutionary computation 19 December 2010 7 / 20

9. Parameter space
http://www.iread.it/lz/hypercube.html
Ben Bolker (Departments of Mathematics & Statistics and Biology, McMaster University)
Evolutionary computation 19 December 2010 8 / 20

10. Outline
1 Introduction
2 Biologically inspired optimization
3 Avenues for exploration/conclusions
Ben Bolker (Departments of Mathematics & Statistics and Biology, McMaster University)
Evolutionary computation 19 December 2010 9 / 20

11. Adaptive landscapes
Wright 1931
(from Johnson 2008)
Ben Bolker (Departments of Mathematics & Statistics and Biology, McMaster University)
Evolutionary computation 19 December 2010 10 / 20

12. “No free lunch” theorem
Across all all possible optimization problems,
all optimization algorithms perform equally:
none is universally best
. . . a “good” optimization algorithm is only good for some particular
problems
http://en.wikipedia.org/wiki/No_free_lunch_in_search_and_optimization
Ho (2002) http://resolver.scholarsportal.info/resolve/00223239/v115i0003/549_seotntaii.xml
Ben Bolker (Departments of Mathematics & Statistics and Biology, McMaster University)
Evolutionary computation 19 December 2010 11 / 20

13. Consequences of NFL for biologically inspired design
Question
Does biological evolution use good optimization techniques?
Ben Bolker (Departments of Mathematics & Statistics and Biology, McMaster University)
Evolutionary computation 19 December 2010 12 / 20

14. Consequences of NFL for biologically inspired design
Question
/ ///// / / / / / / / / / / / / / / /// good/////////////////// techniques?
Does/biological/evolution/use//////// optimization///////// / / / /
/ ////// ////// / ////
Do evolving systems face the same kinds of problems we do?
Ben Bolker (Departments of Mathematics & Statistics and Biology, McMaster University)
Evolutionary computation 19 December 2010 12 / 20

15. characteristics of objective functions/landscapes
discrete vs
continuous
single vs multiple
peaks
smooth vs jagged
Ben Bolker (Departments of Mathematics & Statistics and Biology, McMaster University)
Evolutionary computation 19 December 2010 13 / 20

16. Selection
Evolution occurs in
populations
Offspring have
different
characteristics
Best ones survive,
the population
“climbs the hill”
Ben Bolker (Departments of Mathematics & Statistics and Biology, McMaster University)
Evolutionary computation 19 December 2010 14 / 20

17. mutation
In order to move (and get out
of local minima), need to
maintain variation: mutation
too little mutation: slow
movement
too much: constantly
losing fitness
Selection+mutation = “asexual
reproduction”
http://en.wikipedia.org/wiki/TMNT
Ben Bolker (Departments of Mathematics & Statistics and Biology, McMaster University)
Evolutionary computation 19 December 2010 15 / 20

18. crossover/recombination
let individuals “mate”
randomly select some
characteristics from each
parent
combines features of two
different solutions:
building blocks
hypothesis
tradeoff: can also break
up good combinations
http://www.flickr.com/photos/ajc1/
modularity is important
1103490291/sizes/o/
Ben Bolker (Departments of Mathematics & Statistics and Biology, McMaster University)
Evolutionary computation 19 December 2010 16 / 20

19. specific algorithms
1 10
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separate “genes”
different mutation operation
— uses direction information
Ben Bolker (Departments of Mathematics & Statistics and Biology, McMaster University)
Evolutionary computation 19 December 2010 17 / 20

20. Outline
1 Introduction
2 Biologically inspired optimization
3 Avenues for exploration/conclusions
Ben Bolker (Departments of Mathematics & Statistics and Biology, McMaster University)
Evolutionary computation 19 December 2010 18 / 20

21. genetic complications (opportunities?)
genetic structure:
chromosomes, gene clusters
non-point mutations:
deletion, duplication
mating types (♂, ♀)
modifiers: dominance, canalization
genotype-phenotype map:
integrating developmental biology
(back to generative systems)
Which of these are important for
optimization, and which are accidents?
(How and why did they evolve?)
Ben Bolker (Departments of Mathematics & Statistics and Biology, McMaster University)
Evolutionary computation 19 December 2010 19 / 20

22. evolving complexity
closing the loop: development + evolution
can we allow for evolution of complexity (evolving grammars)?
evolution of modularity
(adaptive recombination, gene rearrangement)
It’s cool, but is it worth it?
Ben Bolker (Departments of Mathematics & Statistics and Biology, McMaster University)
Evolutionary computation 19 December 2010 20 / 20

23. tradeoffs
general vs. problem-specific solutions (NFL)
performance vs robustness (both in optimization algorithms and
solutions)
programming vs computation time
computation vs “meta-computation”
Ben Bolker (Departments of Mathematics & Statistics and Biology, McMaster University)
Evolutionary computation 19 December 2010 21 / 20