'logParetoFront': 'yes', 'logBestIndividual': 'yes', 'logParetoFrontKeepAllGenerations': 'yes', 'logParetoFrontPeriod': 5, 'logParetoSize': 'yes',
'backup': 'no', 'trackAncestry': 'no',
'randomSeed': 0}
arrowbotsDefaults = {'segments': segments, 'sensorAttachmentType': 'variable',
'simulationTime': 10., 'timeStep': 0.1,
'integrateError': 'false', 'writeTrajectories': 'false'}
arrowbotInitialConditions = [[0]*segments]*segments # segmentsXsegments null matrix
arrowbotTargetOrientations = [ [1 if i==j else 0 for i in range(segments)] for j in range(segments) ] # segmentsXsegments identity matrix
# Optional definitions for pbsGridWalker that are constant
involvedGitRepositories = mmr.involvedGitRepositories
# dryRun = False
### Required pbsGridWalker definitions
computationName = 'rateSizeSwipe_N' + str(segments)
nonRSGrid = gr.LinGrid('probabilityOfMutatingClass0', 0.0, 0.2, 0, 5) * \
gr.Grid1d('initialPopulationType', ['sparse', 'random']) * \
gr.Grid1d('populationSize', [15, 25, 40, 60])
parametricGrid = nonRSGrid*numTrials + gr.Grid1dFromFile('randomSeed', mmr.randSeedFile, size=len(nonRSGrid)*numTrials)
for par in parametricGrid.paramNames():
evsDefaults.pop(par)
def prepareEnvironment(experiment):
gccommons.prepareEnvironment(experiment)
def runComputationAtPoint(worker, params):
return gccommons.runComputationAtPoint(worker, params,
evsDefaults, arrowbotsDefaults,
arrowbotInitialConditions,
arrowbotTargetOrientations)
'integrateError': 'false',
'writeTrajectories': 'false'
}
arrowbotInitialConditions = [[0] * segments
] * segments # segmentsXsegments null matrix
arrowbotTargetOrientations = [[1 if i == j else 0 for i in range(segments)]
for j in range(segments)
] # segmentsXsegments identity matrix
# Optional definitions for pbsGridWalker that are constant
involvedGitRepositories = mmr.involvedGitRepositories
# dryRun = False
### Required pbsGridWalker definitions
computationName = 'ccSwipe_N' + str(segments)
nonRSGrid = gr.LinGrid('secondObjectiveProbability', 0.0, 0.05, 0, 20) * \
gr.Grid1d('initialPopulationType', ['sparse', 'random'])
parametricGrid = nonRSGrid * numTrials + gr.Grid1dFromFile(
'randomSeed', mmr.randSeedFile, size=len(nonRSGrid) * numTrials)
for par in parametricGrid.paramNames():
evsDefaults.pop(par)
def prepareEnvironment(experiment):
gccommons.prepareEnvironment(experiment)
def runComputationAtPoint(worker, params):
return gccommons.runComputationAtPoint(worker, params, evsDefaults,
arrowbotsDefaults,
'governingNumHiddenNeurons': 12, 'governingMutModifyNeuron': 0.3, 'governingMutModifyConnection': 0.4, 'governingMutAddRemRatio': 1.,
'behavioralNumHiddenNeurons': 6, 'behavioralMutModifyNeuron': 0.3, 'behavioralMutModifyConnection': 0.4, 'behavioralMutAddRemRatio': 1.,
'weightScale': 1.,
'populationSize': 100,
'genStopAfter': 5000,
'initialPopulationType': 'sparse', 'secondObjectiveProbability': 1.0,
'backup': 'yes', 'backupPeriod': 100, 'trackAncestry': 'no',
'logBestIndividual': 'yes', 'logPopulation': 'no',
'printGeneration': 'yes', 'printPopulation': 'no', 'printParetoFront': 'yes',
'logParetoFront': 'yes', 'logParetoFrontKeepAllGenerations': 'yes', 'logParetoFrontPeriod': 10,
'randomSeed': 42}
### Required pbsGridWalker definitions
computationName = 'afpo_with_mutGoverning'
nonRSGrid = gr.LinGrid('mutGoverning', 0.1, 0.2, 0, 4)
parametricGrid = nonRSGrid * numTrials + gr.Grid1dFromFile(
'randomSeed', cr.randSeedFile, size=len(nonRSGrid) * numTrials)
for par in parametricGrid.paramNames():
evsDefaults.pop(par)
def prepareEnvironment(experiment):
ce.prepareEnvironment(experiment)
def runComputationAtPoint(worker, params):
return ce.runComputationAtPoint(worker,
params,
evsDefaults,