biosimrust.ini

# biosimrust.ini
# This is the default config file for BiosimRust (the Rust version of the simulator).
# The config filename is determined in simulator() in simulator.rs.
# The config file is parsed by ParamManager, see params.rs.
# Although not foolproof, the config file can be modified during a simulation
# run and the param manager will make any new params available to the simulator
# after the end of the current simulator step or after the end of the current
# generation.
#
# Parameter values can also be changed automatically based on the current generation
# number of the simulation using the syntax (see barriertype param for example): 
# <parameterName>@<generationNumber> = <newValue>
# Instances of the same parameter have to be ascending by generation number to work
# correctly, e.g. barriertype@100 must be later in the file than barriertype@50, 
# which in turn must come after barriertype. Not all parameters can be safely
# changed during a simulation. Some restrictions are noted below.

# numthreads must be 1 or greater. Best value is less than or equal to
# the number of CPU cores. Cannot be changed after a simulation starts.
numthreads = 16

# sizex, sizey define the size of the 2D world. Minimum size is 16,16.
# Maximum size is 32767, 32767. Cannot be changed after a simulation starts.
sizex = 128
sizey = 128

# population at the start of each generation. Maximum value = 32766.
# Cannot be changed after a simulation starts.
# BASELINE TEST: Reduced to 500 for faster speciation testing
population = 3000

# Number of simulation steps per generation. Range 1..INT_MAX.
stepspergeneration = 300

# The simulator will stop when the generation number == maxgenerations.
# Range 1..INT_MAX
# BASELINE TEST: Reduced to 500 generations for initial speciation testing
maxgenerations = 2000

# genomeinitiallengthmin and genomeinitiallengthmax should be set to
# the same value. (For future use, the max length might be larger to
# allow mutations that lengthen the genome.) Range 1..INT_MAX and
# must be no larger than genomemaxlength. The range of genomemaxlength
# is genomeinitiallengthmax..INT_MAX. Cannot be changed after a
# simulation starts.
genomeinitiallengthmin = 24
genomeinitiallengthmax = 24
genomemaxlength = 300

# maxnumberneurons is the maximum number of internal neurons that may
# be addressed by genes in the genome. Range 1..INT_MAX. Cannot be
# changed after a simulation starts. Cannot be changed after a
# simulation starts.
maxnumberneurons = 28

# If killenable is true and the "kill" action neuron is enabled in
# sensors-actions.h and compiled in, then agents are permitted to
# kill their neighbor in the adjacent location in the direction of
# forward movement. If false, the neighbors are safe.
killenable = true

# killkinprotection reduces the probability of killing genetically similar individuals.
# Range 0.0..1.0. Higher values = stronger protection against killing kin.
# 0.0 = no protection (identical genomes can kill each other)
# 1.0 = maximum protection (identical genomes never kill each other)
# Formula: adjusted_kill_prob = base_kill_prob * (1.0 - genetic_similarity * killkinprotection)
# Recommended: 0.5 to 0.9 for biological realism. Default: 0.8
# Higher values (0.8-0.9) provide strong kin protection, significantly reducing kills
# among related individuals as genetic diversity decreases.
killkinprotection = 0.9

# If sexualreproduction is false, newborns inherit the genes from a
# single parent. If true, newborns inherit a mixture of genes from
# two parents.
sexualreproduction = true

# If chooseparentbyfitness is false, then every agent that survives the
# selection criterion has equal chance of reproducing. If true, then
# preference is given to those parents who passed the selection criterion
# with a greater score. Fitness scores are determined in survival-criteria.cpp.
chooseparentsbyfitness = true

# pointmutationrate is the probability per gene of having a single-bit
# mutation during spawning. Range 0.0 .. 1.0. A reasonable range is
# 0.0001 to 0.01.
# Increased to 0.02 to promote genetic exploration and prevent premature convergence
pointmutationrate = 0.02

# geneinsertiondeletionrate is the probability per genome of having a gene
# inserted or deleted during reproduction. Range 0.0 .. 1.0.
# A reasonable range is 0.0001 to 0.01. Set to 0.0 to disable.
# For variable-length genome testing, 0.01 provides controlled, gradual changes.
# Increased to 0.02 to promote genetic exploration and prevent premature convergence
# deletionratio is the probability that a mutation will be a deletion vs insertion.
# Range 0.0 .. 1.0. 0.5 means equal chance of insertion or deletion.
# Note: Insertion probability automatically scales down as genome length increases.
geneinsertiondeletionrate = 0.02
deletionratio = 0.5

# fitnesslengthnormalization normalizes fitness scores by genome length to prevent
# selection pressure favoring longer genomes. Formula: normalized_score = score / (1 + beta * length)
# Range 0.0 .. 1.0. Higher values = stronger penalty for longer genomes.
# Reduced to 0.001 to minimize artificial constraints - metabolic costs (Phase 2) will provide natural regularization
# Default: 0.001 (minimal penalty)
fitnesslengthnormalization = 0.001

# allowduplicateconnections enables copy number effects by allowing duplicate connections
# in the neural network phenotype. When true, multiple genes encoding the same connection
# (source-sink pair) will create multiple parallel synapses, naturally summing their
# contributions during feed-forward. This models biological gene dosage effects.
# When false, duplicates are merged by summing weights (legacy behavior).
# Default: true (enabled for biological realism)
allowduplicateconnections = true

# Phase 2: Metabolic Regularization Parameters
# These parameters control energy-based fitness and natural genome length constraints

# metaboliccostpersynapse is the static maintenance cost per synapse per simulation step.
# Larger networks (more synapses) incur higher maintenance costs.
# Range: 0.0 .. 1.0. Recommended: 0.0001 to 0.001
# MODERATE TUNING: Increased 10x from default to create meaningful trade-off
# Default: 0.0001 (very small cost per synapse)
metaboliccostpersynapse = 0.001

# metaboliccostperspike is the dynamic signaling cost per neuron activation (spike).
# More active networks consume more energy.
# Range: 0.0 .. 1.0. Recommended: 0.00001 to 0.0001
# MODERATE TUNING: Increased 10x from default to create meaningful trade-off
# Default: 0.00001 (very small cost per activation)
metaboliccostperspike = 0.0001

# energyrewardpersuccess is the energy reward for surviving a generation.
# This helps successful individuals maintain energy levels.
# Range: >= 0.0. Recommended: 0.05 to 0.2
# MODERATE TUNING: Set to 0.0 to increase cost pressure (no reward offsets)
# Default: 0.1 (modest reward for survival)
energyrewardpersuccess = 0.0

# Phase 3: Homeostatic Plasticity Parameters
# Homeostatic plasticity stabilizes neural networks after duplication events
# by adjusting connection weights to maintain target firing rates.

# homeostasisenabled enables/disables homeostatic plasticity mechanisms.
# When enabled, neurons adjust their incoming connection weights to maintain
# a target firing rate, preventing network instability after gene duplications.
# Range: true or false. Default: true
homeostasisenabled = true

# homeostasisalpha is the learning rate for homeostatic weight adjustments.
# Higher values cause faster adaptation but may be less stable.
# Range: 0.0 to 1.0. Recommended: 0.001 to 0.1. Default: 0.01
homeostasisalpha = 0.01

# targetfiringrate is the target firing rate for neurons (as fraction of steps).
# Neurons will adjust their incoming weights to maintain this firing rate.
# Range: 0.0 to 1.0. Recommended: 0.05 to 0.2. Default: 0.1 (10% of steps)
targetfiringrate = 0.1

# firingratewindow is the window size for calculating moving average firing rates.
# Larger windows provide more stable estimates but respond slower to changes.
# Range: 1 to 10000. Recommended: 50 to 500. Default: 100
firingratewindow = 100

# nsga2enabled enables NSGA-II (Non-dominated Sorting Genetic Algorithm II)
# multi-objective selection. When enabled, parents are selected based on
# multiple objectives: survival score, metabolic efficiency, and genetic diversity.
# This helps maintain population diversity while selecting for performance.
# Phase 5: Multi-objective optimization for diversity preservation.
# Range: true or false. Default: false
nsga2enabled = true

# Phase 4.3: Dendritic Integration
# Enable dendritic computation for biological realism
# Dendritic integration applies non-linear computation to groups of synapses (dendrites)
# This models how real neurons integrate inputs from different dendritic branches
# Range: true or false. Default: false
dendritickenabled = true
# Integration exponent for excitatory inputs (1.0=linear, 2.0=quadratic, 0.5=sublinear)
# Quadratic (2.0) creates cooperative effects: multiple inputs amplify each other
# Sublinear (0.5) creates saturating effects: diminishing returns with more inputs
# Range: 0.1 to 10.0. Default: 2.0
dendriticexponent = 2.0
# Use different exponents for excitatory vs inhibitory inputs
# When true, excitatory uses dendriticexponent, inhibitory uses dendriticinhibitoryexponent
# Range: true or false. Default: true
dendriticseparateexcitatoryinhibitory = true
# Integration exponent for inhibitory inputs (typically sublinear for saturation)
# Range: 0.1 to 10.0. Default: 0.5
dendriticinhibitoryexponent = 0.5

# Phase 6: Speciation (NEAT-style) - TUNED CONFIGURATION
# Enable speciation to protect variable-length innovations
# Organisms are grouped into species based on genetic compatibility distance
# This allows innovative structures (like large genomes from duplication events) to compete
# within their species rather than being outcompeted by lean, optimized ancestors
# Parameters tuned based on 2000-generation analysis to encourage more dynamic speciation
# Range: true or false. Default: false
speciationenabled = true
# Compatibility distance threshold for same species (lower = more species, higher = fewer species)
# Based on NEAT algorithm: organisms with distance <= threshold are in same species
# FIXED: Was set to 0.1 (way too low, causing 10,000+ species). Set to 2.5 (baseline value).
# Range: 0.1 to 100.0. Default: 3.0
# Recommended: 2.0-3.0 for balanced speciation
compatibilitythreshold = 1.8
# Coefficients for compatibility distance calculation (NEAT algorithm)
# Formula: δ = (c1*E/N) + (c2*D/N) + (c3*W)
# where E=excess genes, D=disjoint genes, W=weight difference, N=longer genome length
# BASELINE TEST: Using NEAT defaults for standard behavior
# Range: 0.0 to 10.0. Default: 1.0
excesscoefficient = 1.0
# Range: 0.0 to 10.0. Default: 1.0
disjointcoefficient = 1.0
# Range: 0.0 to 10.0. Default: 0.4
weightcoefficient = 0.4
# Species management parameters
# Generations without improvement before species becomes extinct
# TUNED: Reduced from 10 to 6 for more aggressive culling of stagnant species
# Range: 1 to 1000. Default: 15
speciesstagnationthreshold = 15
# Minimum number of members required to keep species alive
# BASELINE TEST: Keep at 1 to allow small innovative species to survive
# Range: 1 to 100. Default: 1
minspeciessize = 1
# Maximum number of members per species (enforced by culling)
# TUNED: Increased from 100 to 150 since one species was consistently hitting the limit
# Range: 1 to 10000. Default: 50
maxspeciessize = 100
# Fraction of species members to preserve as elite (top performers)
# BASELINE TEST: Using default 0.2 (20% elite preservation)
# Range: 0.0 to 1.0. Default: 0.2
specieselitefraction = 0.2

# responsivenesscurvekfactor is a small positive integer that determines
# the shape of the curve that determines how reactive an agent is to its
# sensory inputs. Typical values are # 1, 2, 3, or 4, but greater values
# are allowed experimentally.
responsivenesscurvekfactor = 2

# populationsensorradius is the radius in which the population sensor
# looks for neighbors. Floating point value. A value of 1.5 includes
# all the immediate eight-neighborhood. Larger values incur exponentially
# increasing processor overhead. Range 0.5 up to (float)max(sizex, sizey).
populationsensorradius = 2.5

# longprobedistance is the default distance that the long-probe sensors
# are able to see. Applies to long-probe population sensor and long-probe
# signal (pheromone) sensor. Range 1..INT_MAX.
longprobedistance = 16

# shortprobebarrierdistance is the distance that the short-probe sensor
# can see. Range 1..INT_MAX.
shortprobebarrierdistance = 4

# signalsensorradius is the radius in which the signal (pheromone) sensor
# looks for pheromones. Floating point value. A value of 1.5 includes
# all the immediate eight-neighborhood. Larger values incur exponentially
# increasing processor overhead. Range 0.5 up to (float)max(sizex, sizey).
signalsensorradius = 2.0

# signallayers defines the number of pheromone layers. Must be 1 for now.
# Values > 1 are for future use. Cannot be changed after a simulation starts.
signallayers = 1

# imagedir is the relative or absolute directory path where generation
# movies are created.
imagedir = images

# logdir is the relative or absolute directory path where text log files
# are created.
logdir = logs

# displayscale scales the generation movie. Typical values are
# 1 for actual size, or 2, 4, 8, 16, or 32 to scale up the movie.
displayscale = 8

# agentsize controls the size of the dot used to represent an agent
# in the generation movie. Typical value is displayscale / 2.
agentsize = 4

# displayenabled controls whether to show a real-time display window
# during simulation. If true, a window will open showing the simulation
# as it runs. If false, only video files will be generated.
displayenabled = false

# If displaychallengearea is true, the simulator will draw colored outlines
# showing the survival challenge areas (circles, lines, borders, etc.)
# based on the current challenge setting.
displaychallengearea = true

# displaychallengeareaopacity controls the opacity of the challenge area outlines.
# Range 0.0 (fully transparent) to 1.0 (fully opaque). Default 0.5.
displaychallengeareaopacity = 1.0

# displaychallengeareacolor sets the RGB color of the challenge area outlines.
# Format: R,G,B where each value is 0-255. Default is "255,255,0" (yellow).
# Examples: "255,0,0" (red), "0,255,0" (green), "0,0,255" (blue), "255,255,255" (white)
displaychallengeareacolor = 255,0,0

# If videosavefirstframes is 0, then only the parameter videostride controls
# how often generation movies are made. If videosavefirstframes is nonzero,
# then generation movies will also be generated for every generation from 0
# through videosavefirstframes (because the first few generations are often
# the most interesting). Range 1..INT_MAX.
videosavefirstframes = 25

# updategraphlog can be set to true to cause the simulator program to
# invoke graphlog.gp to update the simulation progress graph. If true,
# then updategraphlogstride controls how often it is invoked. If
# updategraphlog is false, then the simulator program will not invoke
# graphlog.gp.
updategraphlog = true

# If savevideo is true, the simulator program will create generation
# movies in the directory named by imagedir at the intervals set by
# videosavefirstframes and videostride.
# Note: PNG frame files are automatically saved when savevideo is true
# (required for ffmpeg video generation).
# BASELINE TEST: Disable video to speed up testing
savevideo = true

# videostride determines how often generation movies will be created.
# Also see savevideo and videosavefirstframes. Range 1..INT_MAX.
videostride = 25

# videoframerate controls the playback speed of generated videos.
# Higher values (e.g., 60) make shorter, faster videos.
# Lower values (e.g., 10) make longer, slower videos.
# Typical values: 15-30 fps. Range 1..120.
videoframerate = 25

# updategraphlogstride determines how often the simulation progress graph
# is updated by direct invocation of graphlog.gp. Ignored if updategraphlog
# is false. updategraphlogstride may be a positive integer from 1 to INT_MAX,
# or may be set to the string videostride to use the value of videostride.
updategraphlogstride = videostride

# genomeanalysisstride determines how often the simulator will print genomic
# statistics. The stats are printed to stdout when the generation number
# modulo genomeanalysisstride == 0. The value may be a positive integer from
# 1 to INT_MAX, or may be set to the string videostride to use the value of
# videostride.
# BASELINE TEST: Print every 10 generations to track speciation effects more frequently
genomeanalysisstride = 10

# When the genomic statistics are printed (see genomeanalysisstride), the
# method used to measure genome diversity in the population is determined
# by genomecomparisonmethod. May be set to 0 for Jaro-Winkler method (useful
# for variable-length genomes that can grow or shrink in size); or 1
# for a Hamming measure bit-by-bit, or 2 for a Hamming measure byte-by-byte.
# Set to 0 for variable-length genome testing (required when geneinsertiondeletionrate > 0.0).
genomecomparisonmethod = 0

# When genomic statistics are printed (see genomeanalysisstride), the number
# of genomes sampled from the population and printed to stdout is determined
# by displaysamplegenomes. Range 0 to population size.
displaysamplegenomes = 5

# challenge determines the selection criterion for reproduction. This is
# typically always under active development. See survival-criteria.rs for
# more information.
# 0 = circle
# 1 = right half
# 2 = right quarter
# 3 = neighbor count
# 4 = center weighted
# 40 = center unweighted
# 5 = corner 1/4 radius
# 6 = corner 1/4 radius weighted
# 7 = migrate distance
# 8 = center sparse
# 9 = left eighth
# 10 = radioactive walls (Left side then Right side)
# 11 = against any wall
# 12 = touch any wall any time
# 13 = east-west eighths
# 14 = near barrier
# 15 = pairs
# 16 = contact location sequence
# 17 = altruism, circle + NE corner
challenge = 5

# The simulator supports a feature called "barriers." Barriers are locations
# in the simulated 2D world where agents may not occupy. The value of
# barriertype is typically under active development. See createBarrier.rs
# for more information.
# 0 = none
# 1 = vertical bar constant location
# 2 = vertical bar random locations
# 3 = five staggered vertical bars
# 4 = horiz bar constant location north center
# 5 = floating islands
# 6 = sequence of spots
barriertype = 0

# This is an example of an automatic parameter change based on the generation.
# If uncommented, the barrier type will automatically change to the new value
# when the simulation reaches the generation specified after the @ delimiter.
# barriertype@500 = 5

# If true, then the random number generator (RNG) will be seeded by the value
# in rngseed, causing each thread to receive a deterministic sequence from
# the RNG. If false, the RNG will be randomly seeded and program output will
# be non-deterministic. Cannot be changed after a simulation starts.
deterministic = false

# If deterministic is true, the random number generator will be seeded with
# this value. If deterministic is false, this value is ignored. Legal values
# are integers 0 to 4294967295. Cannot be changed after a simulation starts.
rngseed = 12345678