Genetic Regulatory Networks Applied to Neural Networks

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Genetic Regulatory Networks Applied to Neural
Networks

• Bryan Adams
• MIT Computer Science and Artificial Intelligence
  Laboratory

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Outline

• Motivation and Related Work
• System Overview and Results
• Conclusions

• Motivation and Related Work
• System Overview and Results
• Conclusions

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Motivation June, 2004
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Motivation

• Robot controllers
• Robust
• Adaptive
• Complex behaviors
• Borrow from biology
• Evolutionary Artificial Neural Networks (ANNs)
• Genetic Regulatory Networks (GRNs)

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Motivation

• Two similar robots (or cars)
• Slightly different morphologies

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Related Work Evolutionary ANNs
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Related Work GRNs
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Outline

• Motivation and Related Work
• System Overview and Results
• Conclusions

• Motivation and Related Work
• System Overview and Results
• Conclusions

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System Overview NEAT

• Direct, complete genetic encoding
• Innovation numbers
• Very clever genetic operators
• Speciation during evolution
• Theoretically minimal networks

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System Overview GRN

• Repressive control
• Constitutively active
• Repressor shuts off

? Pcnt (R Famt) ? gt 0
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System Overview GRN

• Activator control
• Constitutively silent
• Activator causes expression

? A Famt ? lt Pcnt
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System Overview Signals

• Decay according to first-order kinetics
• ?t1 k ?t0

• For n signals, half-lives are evenly spaced

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System Overview NEAT-GRN

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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
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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
0.00001f
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Results NEAT and XOR / NXOR
Results averaged over 200 runs 100 solution
success
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Results NEAT-GRN XOR / NXOR
Results averaged over 200 runs 100 solution
success
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Results NEAT-GRN Number of Signals
Results averaged over 200 runs Same GRN
parameters
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Results NEAT-GRN Number of Signals
Results averaged over 200 runs Same GRN
parameters
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Results NEAT-GRN XOR NXOR
Results averaged over 100 runs 35 solution
success (max 250 gen)
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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 0.500 r 1/0.029 0.054
• hidn_t 005 0.500 r - 0/0.022 0.043
• hidn_t 007 0.250 r - 0/0.030 0.049
• hidn_t 011 0.125 r 2/0.046 0.017
• hidn_t 013 0.625 r - 1/0.012 0.096
• hidn_t 019 0.313 r 3/0.013 0.048
• link_t 000 e 0 3 5.88 r - 2/0.028 0.039
  
• link_t 001 e 2 3 -3.90 r - 2/0.007 0.037
  
• link_t 001 e 2 3 8.10 r 0/0.021 0.005
  
• link_t 002 e 1 3 -6.04 r 3/0.007 0.008
  
• link_t 003 e 2 4 2.70 r - 2/0.026 0.036
  
• link_t 004 e 4 3 11.65 r - 0/0.035 0.065
  
• link_t 005 e 1 5 -9.92 r 1/0.007 0.040
  
• link_t 005 e 1 5 -10.55 r 2/0.102 0.024
  
• link_t 005 e 1 5 -6.01 r 1/0.021 0.014
  

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Outline

• Motivation and Related Work
• System Overview and Results
• Conclusions

• Motivation and Related Work
• System Overview and Results
• Conclusions

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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

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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 0.500 r 1/0.029 0.054
• hidn_t 005 0.500 r - 0/0.022 0.043
• hidn_t 007 0.250 r - 0/0.030 0.049
• hidn_t 011 0.125 r 2/0.046 0.017
• hidn_t 013 0.625 r - 1/0.012 0.096
• hidn_t 019 0.313 r 3/0.013 0.048
• link_t 000 e 0 3 5.88 r - 2/0.028 0.039
  
• link_t 001 e 2 3 -3.90 r - 2/0.007 0.037
  
• link_t 001 e 2 3 8.10 r 0/0.021 0.005
  
• link_t 002 e 1 3 -6.04 r 3/0.007 0.008
  
• link_t 003 e 2 4 2.70 r - 2/0.026 0.036
  
• link_t 004 e 4 3 11.65 r - 0/0.035 0.065
  
• link_t 005 e 1 5 -9.92 r 1/0.007 0.040
  
• link_t 005 e 1 5 -10.55 r 2/0.102 0.024
  
• link_t 005 e 1 5 -6.01 r 1/0.021 0.014
  

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Conclusions Next Robots
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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