Download - Genetic Regulatory Networks Applied to Neural Networks

Genetic Regulatory Networks Applied to Neural Networks

Bryan AdamsMIT Computer Science and Artificial Intelligence

Laboratory

October 15, 2004 Research Qualifying Exam 2

• Motivation and Related Work

• System Overview and Results

• Conclusions

Outline



• Conclusions


Motivation: June, 2004


Motivation

• Robot controllers …– Robust

– Adaptive

– Complex behaviors

• Borrow from biology– Evolutionary Artificial

Neural Networks (ANNs)

– Genetic Regulatory Networks (GRNs)


Motivation

Two similar robots (or cars) …

Slightly different morphologies


• Husbands

– GasNets

• Zhou, Shen

– Bugs

• Stanley, Miikkulainen

– NEAT

Related Work: Evolutionary ANNs


Related Work: GRNs

• Josh Bongard

– Artificial Ontogeny

• Peter Eggenberger

– Neural Retina

• Kumar

– GRN controller




• Conclusions

Outline



• Conclusions


System Overview: NEAT

• Direct, complete genetic encoding

• “Innovation numbers”– Very clever genetic

operators

– Speciation during evolution

• Theoretically minimal networks


System Overview: GRN

• Repressive control

– Constitutively active

– Repressor shuts off

Pcnt

Prod

= Pcnt – (R Famt) ; >= 0

Prod

R


System Overview: GRN

• Activator control

– Constitutively silent

– Activator causes expression

= A Famt ; <= Pcnt

0

Prod

Prod

A


System Overview: Signals

• Decay according to first-order kineticst=1 = k t=0

• For n signals, half-lives are evenly spaced


System Overview: NEAT-GRN

+

Environment


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;


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;


Results: NEAT and XOR / NXOR

NEAT / XOR

Mean/SD Win gen 24.760 10.653Mean/SD Nodes 2.330 1.225Mean/SD Links 11.545 5.240

Results averaged over 200 runs; 100% solution success

NEAT / NXOR



Results: NEAT-GRN XOR / NXOR

Results averaged over 200 runs; 100% solution success

NEAT-GRN / XOR


NEAT-GRN / NXOR


Mean/SD Expr. Nodes 2.580 1.270Mean/SD Expr. Links 12.630 4.748


Mean/SD Nodes 2.445 1.272Mean/SD Links 11.920 5.166



Results: NEAT-GRN Number of Signals

Results averaged over 200 runs; Same GRN parameters

0

10

20

30

40

50

60

1 2 3 4 5 6 7 8 9 10

Number Of Signals

Gen

era

tio

ns t

o W

inn

er


Results: NEAT-GRN Number of Signals

Results averaged over 200 runs; Same GRN parameters

0

0.2

0.4

0.6

0.8

1

1.2

1 2 3 4 5

Percentage Postive Control

Su

cces

s P

erce

nta

ge


Results: NEAT-GRN XOR & NXORResults averaged over 100 runs

35% solution success (max 250 gen)



NEAT-GRN XOR & NXOR


NEAT-GRN / XOR





Results: XOR & NXOR networkbias_t [000] 0.000inpt_t [001] 0.000inpt_t [002] 0.000outp_t [003] 1.000hidn_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 }link_t [006] [e] 5 3 -8.85 r{ - 3/0.081 0.010 }link_t [007] [e] 1 4 6.69 r{ - 1/0.049 0.050 }link_t [008] [e] 0 4 -1.19 r{ - 2/0.088 0.005 }link_t [008] [e] 0 4 8.59 r{ - 2/0.045 0.019 }link_t [008] [e] 0 4 -4.49 r{ - 0/0.012 0.034 }link_t [009] [e] 0 5 1.68 r{ - 3/0.051 0.051 }link_t [010] [e] 2 5 3.76 r{ - 0/0.050 0.008 }link_t [010] [e] 2 5 3.41 r{ + 0/0.071 0.026 }link_t [010] [e] 2 5 -10.39 r{ - 3/0.054 0.067 }link_t [013] [e] 1 7 1.17 r{ + 3/0.097 0.022 }link_t [014] [e] 7 5 -12.00 r{ - 3/0.004 0.042 }link_t [017] [e] 2 7 7.27 r{ + 0/0.046 0.045 }link_t [018] [e] 7 4 -1.33 r{ - 2/0.023 0.040 }link_t [021] [e] 7 3 5.15 r{ - 2/0.006 0.059 }link_t [021] [e] 7 3 2.48 r{ + 0/0.031 0.030 }link_t [022] [e] 0 7 -3.24 r{ - 1/0.026 0.064 }link_t [022] [e] 0 7 7.14 r{ + 3/0.050 0.070 }link_t [025] [e] 1 11 7.35 r{ + 3/0.028 0.021 }link_t [026] [e] 11 7 5.03 r{ + 2/0.021 0.030 }link_t [029] [e] 7 13 1.27 r{ - 1/0.040 0.050 }link_t [030] [e] 13 3 -5.13 r{ - 0/0.020 0.041 }link_t [038] [e] 11 3 6.86 r{ - 2/0.032 0.043 }link_t [042] [e] 0 11 2.43 r{ - 1/0.011 0.027 }link_t [043] [e] 11 5 -1.64 r{ + 2/0.027 0.003 }link_t [044] [e] 2 11 -0.37 r{ + 3/0.013 0.023 }link_t [046] [e] 11 4 -8.51 r{ + 0/0.024 0.036 }link_t [053] [e] 11 19 -1.32 r{ - 3/0.058 0.074 }link_t [054] [e] 19 5 -7.22 r{ - 3/0.022 0.105 }link_t [055] [e] 19 4 -8.26 r{ + 3/0.076 0.031 }link_t [060] [e] 19 3 -4.63 r{ - 1/0.155 0.021 }link_t [064] [e] 1 19 -0.33 r{ + 2/0.048 0.016 }link_t [078] [e] 4 13 9.26 r{ + 3/0.078 0.029 }link_t [079] [e] 0 13 -2.86 r{ + 3/0.021 0.009 }link_t [080] [e] 2 13 2.33 r{ + 1/0.039 0.085 }link_t [080] [e] 2 13 -3.19 r{ - 1/0.086 0.061 }link_t [082] [e] 11 13 7.32 r{ + 3/0.034 0.042 }link_t [083] [e] 5 13 -2.39 r{ - 2/0.029 0.006 }link_t [085] [e] 1 13 3.85 r{ - 0/0.039 0.038 }link_t [093] [e] 2 19 -2.90 r{ - 1/0.020 0.021 }link_t [094] [e] 7 19 4.30 r{ + 1/0.001 0.056 }link_t [095] [e] 19 13 2.90 r{ - 2/0.046 0.025 }

Env1

Env0




• Conclusions

Outline



• Conclusions


Conclusions: Contributions

• 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

• A GRN model that features a variably-decoding phenotype


Conclusions: Carsbias_t [000] 0.000inpt_t [001] 0.000inpt_t [002] 0.000outp_t [003] 1.000hidn_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 }link_t [006] [e] 5 3 -8.85 r{ - 3/0.081 0.010 }link_t [007] [e] 1 4 6.69 r{ - 1/0.049 0.050 }link_t [008] [e] 0 4 -1.19 r{ - 2/0.088 0.005 }link_t [008] [e] 0 4 8.59 r{ - 2/0.045 0.019 }link_t [008] [e] 0 4 -4.49 r{ - 0/0.012 0.034 }link_t [009] [e] 0 5 1.68 r{ - 3/0.051 0.051 }link_t [010] [e] 2 5 3.76 r{ - 0/0.050 0.008 }link_t [010] [e] 2 5 3.41 r{ + 0/0.071 0.026 }link_t [010] [e] 2 5 -10.39 r{ - 3/0.054 0.067 }link_t [013] [e] 1 7 1.17 r{ + 3/0.097 0.022 }link_t [014] [e] 7 5 -12.00 r{ - 3/0.004 0.042 }link_t [017] [e] 2 7 7.27 r{ + 0/0.046 0.045 }link_t [018] [e] 7 4 -1.33 r{ - 2/0.023 0.040 }link_t [021] [e] 7 3 5.15 r{ - 2/0.006 0.059 }link_t [021] [e] 7 3 2.48 r{ + 0/0.031 0.030 }link_t [022] [e] 0 7 -3.24 r{ - 1/0.026 0.064 }link_t [022] [e] 0 7 7.14 r{ + 3/0.050 0.070 }link_t [025] [e] 1 11 7.35 r{ + 3/0.028 0.021 }link_t [026] [e] 11 7 5.03 r{ + 2/0.021 0.030 }link_t [029] [e] 7 13 1.27 r{ - 1/0.040 0.050 }link_t [030] [e] 13 3 -5.13 r{ - 0/0.020 0.041 }link_t [038] [e] 11 3 6.86 r{ - 2/0.032 0.043 }link_t [042] [e] 0 11 2.43 r{ - 1/0.011 0.027 }link_t [043] [e] 11 5 -1.64 r{ + 2/0.027 0.003 }link_t [044] [e] 2 11 -0.37 r{ + 3/0.013 0.023 }link_t [046] [e] 11 4 -8.51 r{ + 0/0.024 0.036 }link_t [053] [e] 11 19 -1.32 r{ - 3/0.058 0.074 }link_t [054] [e] 19 5 -7.22 r{ - 3/0.022 0.105 }link_t [055] [e] 19 4 -8.26 r{ + 3/0.076 0.031 }link_t [060] [e] 19 3 -4.63 r{ - 1/0.155 0.021 }link_t [064] [e] 1 19 -0.33 r{ + 2/0.048 0.016 }link_t [078] [e] 4 13 9.26 r{ + 3/0.078 0.029 }link_t [079] [e] 0 13 -2.86 r{ + 3/0.021 0.009 }link_t [080] [e] 2 13 2.33 r{ + 1/0.039 0.085 }link_t [080] [e] 2 13 -3.19 r{ - 1/0.086 0.061 }link_t [082] [e] 11 13 7.32 r{ + 3/0.034 0.042 }link_t [083] [e] 5 13 -2.39 r{ - 2/0.029 0.006 }link_t [085] [e] 1 13 3.85 r{ - 0/0.039 0.038 }link_t [093] [e] 2 19 -2.90 r{ - 1/0.020 0.021 }link_t [094] [e] 7 19 4.30 r{ + 1/0.001 0.056 }link_t [095] [e] 19 13 2.90 r{ - 2/0.046 0.025 }

Env1

Env0


Conclusions: Next Robots


Long-term Objectives:Project Overview

I. Academica. Literature search / readingb. Qualifying examinationc. Thesis proposald. Doctoral dissertation

II. Robotic platforma. Design and fabricationb. Robot chassis and motor systemc. Sensors and camerasd. Firmware and drivers

III. Softwarea. Artificial brain modules:

i. NEATer with GRNii. NEATer with developmentiii. NEATer with topologyiv. Synthetic Brains (integrated)

b. Simulation and evolution:i. Simulated arm and motors

ii. Simulated sensors iii. Evolutionary algorithm

An outline of the work to be done between now and October ‘05

Download - Genetic Regulatory Networks Applied to Neural Networks

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