1. Genetic Regulatory Networks Applied to Neural Networks
Bryan Adams
MIT Computer Science and Artificial Intelligence Laboratory

2. Research Qualifying Exam
Outline
Motivation and Related Work
System Overview and Results
Conclusions
Motivation and Related Work
System Overview and Results
Conclusions
October 15, 2004
Research Qualifying Exam

3. Research Qualifying Exam
Motivation: June, 2004
October 15, 2004
Research Qualifying Exam

4. Research Qualifying Exam
Motivation
Robot controllers …
Robust
Adaptive
Complex behaviors
Borrow from biology
Evolutionary Artificial Neural Networks (ANNs)
Genetic Regulatory Networks (GRNs)
October 15, 2004
Research Qualifying Exam

5. Research Qualifying Exam
Motivation
Two similar robots (or cars) …
Slightly different morphologies
October 15, 2004
Research Qualifying Exam

6. Related Work: Evolutionary ANNs
Stanley, Miikkulainen
NEAT
Husbands
GasNets
Zhou, Shen
Bugs
October 15, 2004
Research Qualifying Exam

7. Research Qualifying Exam
Related Work: GRNs
Kumar
GRN controller
Peter Eggenberger
Neural Retina
Josh Bongard
Artificial Ontogeny
October 15, 2004
Research Qualifying Exam

8. Research Qualifying Exam
Outline
Motivation and Related Work
System Overview and Results
Conclusions
Motivation and Related Work
System Overview and Results
Conclusions
October 15, 2004
Research Qualifying Exam

9. Research Qualifying Exam
System Overview: NEAT
Direct, complete genetic encoding
“Innovation numbers”
Very clever genetic operators
Speciation during evolution
Theoretically minimal networks
October 15, 2004
Research Qualifying Exam

10. Research Qualifying Exam
System Overview: GRN
Repressive control
Constitutively active
Repressor shuts off
Pcnt
Prod 
Prod R
 = Pcnt – (R Famt) ;  >= 0
October 15, 2004
Research Qualifying Exam

11. Research Qualifying Exam
System Overview: GRN
Activator control
Constitutively silent
Activator causes expression
Prod
Prod A 
 = A Famt ;  <= Pcnt
October 15, 2004
Research Qualifying Exam

12. System Overview: Signals
Decay according to first-order kinetics
t=1 = k t=0
For n signals, half-lives are evenly spaced
October 15, 2004
Research Qualifying Exam

13. System Overview: NEAT-GRN
Environment +
October 15, 2004
Research Qualifying Exam

14. System Overview: 36 NEAT Parameters
int n_links_avoid_chaining = 15;
int num_tries_insert_hid = 30;
float max_new_weight = 2.50f;
float max_big_weight = 10.0f;
float max_w_change = 2.50f;
bool allow_recurrent_links = false;
int num_tries_insert_link = 30;
float prob_reenable_during_xover = 0.25f;
float max_weight = 12.00f;
float min_weight =-12.00f;
float p_mutate_weights = 0.90f;
int min_size_age_prot = 10;
float old_links_frac = 0.20f;
float old_links_mul = 1.20f;
float p_severe_mut = 0.50f;
float p_severe_change = 0.70f;
float p_severe_new = 0.20f;
float p_normal_change = 0.50f;
float p_normal_new = 0.10f;
int min_size_for_elite = 5;
int max_elderly_amnesty = 15;
float failure_to_improve_penalty = 0.01f;
float good_parent_frac = 0.20f;
float p_mutate_only = 0.25f;
float p_inters_xover = 0.001f;
float upper_spec_frac = 0.22f;
float lower_spec_frac = 0.18f;
float dyn_spec_increment = 0.30f;
float c1 = 1.0f;
float c2 = 1.0f;
float c3 = 0.4f;
float delta_t = 3.0f;
float p_add_node = 0.03f;
float p_add_link = 0.30f;
float p_add_node = 0.001f;
float p_add_link = 0.05f;
October 15, 2004
Research Qualifying Exam

15. System Overview: 30 GRN Parameters
int n_signals = 4;
float max_half_life = 20;
float min_half_life = 2;
int production_steps = 50;
float signal_input_multiplier = 0.01f;
float lethal_fraction = 0.10f;
float p_take_both= 0.25f;
float p_add_copy_link = 0.15f;
float max_num_copies = 3;
float p_mutate_regl = 0.75f;
float p_add_regl = 0.00f;
float p_regl_severe_mut = 0.50f;
float p_regl_normal_chg = 0.50f;
float p_regl_normal_new = 0.10f;
float p_regl_severe_chg = 0.70f;
float p_regl_severe_new = 0.20f;
float c4 = 0.1f;
float p_no_prod = 0.50f;
float p_no_ra = 0.00f;
float p_neg_ctrl = 0.50f;
float famt_max_val = 0.30f;
float famt_max_incr = 0.02f;
float pcnt_max_val = 0.30f;
float pcnt_max_incr = 0.02f;
float p_change_rg = 0.00f;
float p_change_ra = 0.02f;
float p_change_pr = 0.04f;
float p_change_pc = 0.65f;
float p_change_fa = 1.00f;
float expression_amt = f;
October 15, 2004
Research Qualifying Exam

16. Results: NEAT and XOR / NXOR
Results averaged over 200 runs; 100% solution success
October 15, 2004
Research Qualifying Exam

17. Results: NEAT-GRN XOR / NXOR
Results averaged over 200 runs; 100% solution success
October 15, 2004
Research Qualifying Exam

18. Results: NEAT-GRN Number of Signals
Results averaged over 200 runs; Same GRN parameters
October 15, 2004
Research Qualifying Exam

19. Results: NEAT-GRN Number of Signals
Results averaged over 200 runs; Same GRN parameters
October 15, 2004
Research Qualifying Exam

20. Results: NEAT-GRN XOR & NXOR
Results averaged over 100 runs
35% solution success (max 250 gen)
October 15, 2004
Research Qualifying Exam

21. Results: XOR & NXOR network
bias_t [000] 0.000
inpt_t [001] 0.000
inpt_t [002] 0.000
outp_t [003] 1.000
hidn_t [004] r{ + 1/ }
hidn_t [005] r{ - 0/ }
hidn_t [007] r{ - 0/ }
hidn_t [011] r{ + 2/ }
hidn_t [013] r{ - 1/ }
hidn_t [019] r{ + 3/ }
link_t [000] [e] r{ - 2/ }
link_t [001] [e] r{ - 2/ }
link_t [001] [e] r{ + 0/ }
link_t [002] [e] r{ + 3/ }
link_t [003] [e] r{ - 2/ }
link_t [004] [e] r{ - 0/ }
link_t [005] [e] r{ + 1/ }
link_t [005] [e] r{ + 2/ }
link_t [005] [e] r{ + 1/ }
link_t [006] [e] r{ - 3/ }
link_t [007] [e] r{ - 1/ }
link_t [008] [e] r{ - 2/ }
link_t [008] [e] r{ - 2/ }
link_t [008] [e] r{ - 0/ }
link_t [009] [e] r{ - 3/ }
link_t [010] [e] r{ - 0/ }
link_t [010] [e] r{ + 0/ }
link_t [010] [e] r{ - 3/ }
link_t [013] [e] r{ + 3/ }
link_t [014] [e] r{ - 3/ }
link_t [017] [e] r{ + 0/ }
link_t [018] [e] r{ - 2/ }
link_t [021] [e] r{ - 2/ }
link_t [021] [e] r{ + 0/ }
link_t [022] [e] r{ - 1/ }
link_t [022] [e] r{ + 3/ }
link_t [025] [e] r{ + 3/ }
link_t [026] [e] r{ + 2/ }
link_t [029] [e] r{ - 1/ }
link_t [030] [e] r{ - 0/ }
link_t [038] [e] r{ - 2/ }
link_t [042] [e] r{ - 1/ }
link_t [043] [e] r{ + 2/ }
link_t [044] [e] r{ + 3/ }
link_t [046] [e] r{ + 0/ }
link_t [053] [e] r{ - 3/ }
link_t [054] [e] r{ - 3/ }
link_t [055] [e] r{ + 3/ }
link_t [060] [e] r{ - 1/ }
link_t [064] [e] r{ + 2/ }
link_t [078] [e] r{ + 3/ }
link_t [079] [e] r{ + 3/ }
link_t [080] [e] r{ + 1/ }
link_t [080] [e] r{ - 1/ }
link_t [082] [e] r{ + 3/ }
link_t [083] [e] r{ - 2/ }
link_t [085] [e] r{ - 0/ }
link_t [093] [e] r{ - 1/ }
link_t [094] [e] r{ + 1/ }
link_t [095] [e] r{ - 2/ }
Env0
Env1
October 15, 2004
Research Qualifying Exam

22. Research Qualifying Exam
Outline
Motivation and Related Work
System Overview and Results
Conclusions
Motivation and Related Work
System Overview and Results
Conclusions
October 15, 2004
Research Qualifying Exam

23. Conclusions: Contributions
A GRN model that features a variably-decoding phenotype
Robust
A genome that can choose between different expressions
Adaptive
A controller where the env. Feeds back to the GRN
Complex behaviors
A genome that codes for multiple behaviors
October 15, 2004
Research Qualifying Exam

24. Research Qualifying Exam
Conclusions: Cars
bias_t [000] 0.000
inpt_t [001] 0.000
inpt_t [002] 0.000
outp_t [003] 1.000
hidn_t [004] r{ + 1/ }
hidn_t [005] r{ - 0/ }
hidn_t [007] r{ - 0/ }
hidn_t [011] r{ + 2/ }
hidn_t [013] r{ - 1/ }
hidn_t [019] r{ + 3/ }
link_t [000] [e] r{ - 2/ }
link_t [001] [e] r{ - 2/ }
link_t [001] [e] r{ + 0/ }
link_t [002] [e] r{ + 3/ }
link_t [003] [e] r{ - 2/ }
link_t [004] [e] r{ - 0/ }
link_t [005] [e] r{ + 1/ }
link_t [005] [e] r{ + 2/ }
link_t [005] [e] r{ + 1/ }
link_t [006] [e] r{ - 3/ }
link_t [007] [e] r{ - 1/ }
link_t [008] [e] r{ - 2/ }
link_t [008] [e] r{ - 2/ }
link_t [008] [e] r{ - 0/ }
link_t [009] [e] r{ - 3/ }
link_t [010] [e] r{ - 0/ }
link_t [010] [e] r{ + 0/ }
link_t [010] [e] r{ - 3/ }
link_t [013] [e] r{ + 3/ }
link_t [014] [e] r{ - 3/ }
link_t [017] [e] r{ + 0/ }
link_t [018] [e] r{ - 2/ }
link_t [021] [e] r{ - 2/ }
link_t [021] [e] r{ + 0/ }
link_t [022] [e] r{ - 1/ }
link_t [022] [e] r{ + 3/ }
link_t [025] [e] r{ + 3/ }
link_t [026] [e] r{ + 2/ }
link_t [029] [e] r{ - 1/ }
link_t [030] [e] r{ - 0/ }
link_t [038] [e] r{ - 2/ }
link_t [042] [e] r{ - 1/ }
link_t [043] [e] r{ + 2/ }
link_t [044] [e] r{ + 3/ }
link_t [046] [e] r{ + 0/ }
link_t [053] [e] r{ - 3/ }
link_t [054] [e] r{ - 3/ }
link_t [055] [e] r{ + 3/ }
link_t [060] [e] r{ - 1/ }
link_t [064] [e] r{ + 2/ }
link_t [078] [e] r{ + 3/ }
link_t [079] [e] r{ + 3/ }
link_t [080] [e] r{ + 1/ }
link_t [080] [e] r{ - 1/ }
link_t [082] [e] r{ + 3/ }
link_t [083] [e] r{ - 2/ }
link_t [085] [e] r{ - 0/ }
link_t [093] [e] r{ - 1/ }
link_t [094] [e] r{ + 1/ }
link_t [095] [e] r{ - 2/ }
Env0
Env1
October 15, 2004
Research Qualifying Exam

25. Conclusions: Next Robots
October 15, 2004
Research Qualifying Exam

26. Long-term Objectives: Project Overview
An outline of the work to be done between now and October ‘05
III. Software
a. Artificial brain modules:
i. NEATer with GRN
ii. NEATer with development
iii. NEATer with topology
iv. Synthetic Brains (integrated)
b. Simulation and evolution:
i. Simulated arm and motors ii. Simulated sensors
iii. Evolutionary algorithm
I. Academic
a. Literature search / reading
b. Qualifying examination
c. Thesis proposal
d. Doctoral dissertation
II. Robotic platform
a. Design and fabrication
b. Robot chassis and motor system
c. Sensors and cameras
d. Firmware and drivers
October 15, 2004
Research Qualifying Exam