View source for Genetic programming example in lua

The following code is capable of converging on a solution to Brad's parabola problem, which can be found on the [[NARG]] page

<pre>
master_stack = {}
function_table = {}
type_array = {}
type_stack = {}

stack_size = 32

num_types = 0

num_x_samples = 5
constant = {0,1,2,3,4}
expected_result = {0, 4.75, 12.75, 23.98, 38.45, 56.15}
function sleep(n)
	os.execute("sleep " .. tonumber(n))
end

function deepcopy(object)
    local lookup_table = {}
	local function _copy(object)
	if type(object) ~= "table" then
	    return object
	elseif lookup_table[object] then
		return lookup_table[object]
    end
    local new_table = {}
    lookup_table[object] = new_table
    for index, value in pairs(object) do
        new_table[_copy(index)] = _copy(value)
	end
	return setmetatable(new_table, getmetatable(object))
    end
    return _copy(object)
end


function print_table(theTable, indent)
	
	local iString = ""
	for index = 1, indent do
		iString = iString .. "-"
	end

	-- walk all the topmost values in the table
	for k,v in pairs(theTable) do
		print(iString ,k ,v)
		if type(v) == "table" then
			print_table(v, indent + 1)
		end
	end

end

function print_program(theProgram)
	print("Program Stack Bottom")
	for counter = 1, table.getn(theProgram) do
		if (theProgram[counter].name == "real") then
			print(theProgram[counter].value)
		else
			print(theProgram[counter].name)
		end
	end
	print("Program Stack Top")
end

-- add the type to the type table
function insert_function(functionName, functionCall, functionType, nArguments)
	-- ensure we haven't already inserted this function
	if function_table[functionName] ~= nil then
		print("function already defined: " .. functionName)
		return false
	end

	-- add this function to the function_table
	tempFunction = {}
	tempFunction.fName = functionCall
	tempFunction.fType = functionType
	tempFunction.nArgs = nArguments

	function_table[functionName] = tempFunction
	return true
end

function insert_type(typeName, fPointer, weight)
	-- walk the list of types and make sure this one hasn't already been defined
	for index = 1, table.getn(type_array) do
		if (type_array[index].name == typeName) then
			print("type already defined: " .. typeName)
			return false
		end
	end

	num_types = num_types + 1
	type_array[num_types] = {}
	type_array[num_types].name = typeName
	type_array[num_types].fPointer = fPointer
	type_array[num_types].weight = weight

	if fPointer == nil then
		-- establish the stack for this type
		type_stack[typeName] = {}
	end

	return true
end

function local_add(arguments)
	-- print("adding " .. arguments[1] .. " and " .. arguments[2])
	return arguments[1] + arguments[2]
end

function local_subtract(arguments)
	-- print("subtracting " .. arguments[1] .. " and " .. arguments[2])
	return arguments[1] + arguments[2]
end

function local_multiply(arguments)
	-- print("multiplying " .. arguments[1] .. " and " .. arguments[2])
	return arguments[1] * arguments[2]
end

function local_divide(arguments)
	-- print("dividing " .. arguments[1] .. " and " .. arguments[2])
	-- dragon
	if (arguments[2] == 0) then
			arguments[2] = 0.00001
	end
	return arguments[1] / arguments[2]
end

function some_constant()
	-- print("pushing constant onto stack:" .. constant[currentConstant])
	return constant[currentConstant]
end

function establish_types()
	-- add each of our types to the type_table
	insert_type("real", nil, 10)
	insert_type("+", local_add, 1)
	insert_type("*", local_multiply, 1)
	--insert_type("-", local_subtract, 1)
	--insert_type("/", local_divide, 1)
	insert_type("X", some_constant, 5)
end

function establish_functions()
	insert_function("+", local_add, "real", 2)
	insert_function("-", local_subtract, "real", 2)
	insert_function("*", local_multiply, "real",  2)
	insert_function("/", local_divide, "real",  2)
	insert_function("X", some_constant, "real", 0)
end

function generate_program(programSize )
	return_stack = {}
	
	for counter = 1, programSize do
		currentNode = {}
		ranVal = math.random(1,table.getn(type_array))
		currentNode.name = type_array[ranVal].name
		-- beware hardcoded stuffs
		if currentNode.name == "real" then
			currentNode.value = math.random()
		end

		table.insert(return_stack, currentNode)
	end

	return return_stack
end

function process_master()
	while table.getn(master_stack) ~= 0 do
	--	print("frame begin-------------------------------")
	--	print("current table:")
		-- print_table(type_stack["real"], 0)
		
		currentNode = table.remove(master_stack)
	--	print("curret node name: " .. currentNode.name )

		-- treat functions and values differently
		if currentNode.name == "real" then
		--	print("current node value: " .. currentNode.value)
			-- add this value to the 'real' stack
			table.insert(type_stack["real"], currentNode.value)
		else
			-- grab the num of params needed for this function
			nRequired = function_table[currentNode.name].nArgs
			theType = function_table[currentNode.name].fType

			-- make sure there are enough objects on the param stack to call this function
			-- print("name = " .. currentNode.name)
			-- print(function_table[currentNode.name].fType)
			-- print(type_stack["real"])
			if (table.getn(type_stack[function_table[currentNode.name].fType]) < nRequired) then
				-- not enough params available, NOOP
			--	print("not enough params, NOOP")
			else
				theArguments = {}
				-- build an array for passing the params to the function
				for counter = 1, nRequired do
					theArguments[counter] = table.remove(type_stack[theType])
				end

				-- call the function
				returnVal = function_table[currentNode.name].fName(theArguments)

				-- push the return val to the appropriate stack
				table.insert(type_stack[function_table[currentNode.name].fType], returnVal)

			end
		end
	--	print("frame end---------------------------------")
	end

end

function grab_result()
	-- print the top of the "real" stack
	if (table.getn(type_stack["real"]) == 0) then
		return 9999999
	end
	return table.remove(type_stack["real"])

end

--generate_master()

--process_master()

--print_result()


function create_population()
	-- loop through each member in the population
	for count = 1, population_size do
		current_member = {}
		current_member._error = 99999
		
		-- generate the member's program data
		current_member.program = generate_program(initial_member_size)
		
		-- add this member to the population
		table.insert(population, current_member)
	end
end

function get_best_candidate()
	local best_error = 99999
	local best_index = 0

	for tIndex = 1, table.getn(candidates) do
		if candidates[tIndex]._error < best_error then
			best_index = tIndex
			best_error = candidates[tIndex]._error
		end
	end

	--print("candidate size: " .. table.getn(candidates) .. "\nbest error from candidates: " .. best_error)

	-- remove and return the *best* candidate
	return table.remove(candidates, best_index)
end

function mutate_child(origin, n_mutations)
	dest_node = deepcopy(origin)

	for counter = 1, n_mutations do
		-- random point inside this child
		index_mutate = math.random(1, table.getn(dest_node.program))
		
		ranVal = math.random(1,table.getn(type_array))
		dest_node.program[index_mutate].name = type_array[ranVal].name
		-- beware hardcoded stuffs
		if dest_node.program[index_mutate].name == "real" then
			dest_node.program[index_mutate].value = math.random()
		end
	end

	return dest_node
end

function crossover_parents(mommy, daddy)
	index_m = math.random(1, table.getn(mommy))
	--index_d = math.random(1, table.getn(daddy))

	the_child = {}

	-- add the first index_m elements of mommy to the_child
	for xxx = 1, index_m do
		table.insert(the_child, mommy[xxx])
	end

	-- add the elements index_d to #daddy of daddy to the_child
	for xxx = index_m+1, table.getn(daddy) do
		table.insert(the_child, daddy[xxx])
	end

	return the_child
end

-- initialize the population (1 program for each member in the population)
establish_functions()
establish_types()
population = {}
population_size = 10000
initial_member_size = 24
create_population()

error_history = {}
error_threshhold = 0.016


max_num_iterations = 10000
current_iteration = 1

-- while we haven't reached our error threshhold
while current_iteration <= max_num_iterations do

	print("iteration #" .. current_iteration)

	--print_table(population, 2)

	-- get the error for each of the members of our population
	for pcount = 1, population_size do

		--print_table(population[pcount], 1)

		-- initialize the error for this program
		population[pcount]._error = 0
		--print("pcount = " .. pcount)

		if (current_iteration == 2) then
			--print_table(population[pcount], 1)
		end


			-- for each value of X
			for icount = 1, num_x_samples do
				-- establish the index to the current X/Y pair
				currentConstant = icount

			--	print("population size: " .. table.getn(population))
			--	print("master_stack size: " .. table.getn(population[pcount].program))

				-- make a copy of this guy's program
				master_stack = deepcopy(population[pcount].program)

				-- initialize the stacks for each data type
				type_stack["real"] = {}
				
				-- evaluate the program
				process_master()

			--	print("master_stack size: " .. table.getn(population[pcount].program))

			--	print("!!! - " .. table.getn(type_stack["real"]))

				the_result = grab_result()

				--print("the result = " .. the_result)

				-- add the current error to the total error for this program
				population[pcount]._error = population[pcount]._error + math.abs(the_result - expected_result[currentConstant])
			end
	end

	-- scan the population and find the lowest error
	total_error = 0
	best_error = 99999
	best_index = 0
	for pcount = 1, population_size do
		total_error = total_error + population[pcount]._error
		if (population[pcount]._error < best_error) then
			best_index = pcount
			best_error = population[pcount]._error
		end
	end

	average_error = total_error / population_size

	print("lowest error : " .. best_error)
	print("average error: " .. average_error)

	table.insert(error_history, best_error)

	-- if the error is under our threshhold, break out and report success
	if (best_error < error_threshhold) then
		break
	end


	--=======================================================================
	-- evolution FTW
	--=======================================================================

	children = {}
	child = {}
	candidates = deepcopy(population)

	-- find the top 10% of the population, keep them
	tnum = math.ceil(table.getn(population) / 100)
	for cpop = 1, tnum do
		table.insert(children, get_best_candidate())
	end


	--sleep(10)
	-- the next 25% should be mutations of the top 10%
	xnum = tnum + math.floor(tnum * 10)
	candidates = deepcopy(children) -- reset candidates
	-- print("we have " .. table.getn(candidates) .. " candidates to chose from")
	for cpop = (tnum+1), xnum do
		child = mutate_child(candidates[math.random(1, table.getn(candidates))], 5)
		table.insert(children, deepcopy(child))
		-- print("size of children: " .. table.getn(children))
	end

	-- the remainder should be crossovers of the full population
	for pcount = (xnum+1), population_size do

		-- take 7 random values from the population
		candidates = {}
		child = {}
		for ccount = 1, 7 do
			table.insert(candidates, deepcopy(population[math.random(1, table.getn(population))]))
		end

		-- print_table(candidates, 1)

		mom = get_best_candidate()
		dad = get_best_candidate()
		child.program = crossover_parents(mom.program, dad.program)

		-- write the child data to the list of children
		table.insert(children, child)
	end

	-- move the new population to their proper home
	population = deepcopy(children)

	current_iteration = current_iteration + 1
end

print("all done!")
--print_table(population[best_index].program, 1)
print_program(population[best_index].program)
</pre>

[[Category:NARG]]