Ruby Array Reference Function [closed] - arrays

Question

Closed. This question needs details or clarity. It is not currently accepting answers.
Want to improve this question? Add details and clarify the problem by editing this post.
Closed 6 years ago.
Improve this question
I don't understand the Ruby reference relationship of arrays variables (best and vector) with the return of the functions (fitness or bitstring for example).
Concidere this case:
best[:fitness]
At where best is array and fitness is a function, what will be required of the variable ? and what will be referenced by the function ?
See the complete code:
def objective_function(vector)
return vector.inject(0.0) {|sum, x| sum + (x ** 2.0)}
end
def random_bitstring(num_bits)
return (0...num_bits).inject(""){|s,i| s<<((rand<0.5) ? "1" : "0")}
end
def decode(bitstring, search_space, bits_per_param)
vector = []
search_space.each_with_index do |bounds, i|
off, sum = i*bits_per_param, 0.0
param = bitstring[off...(off+bits_per_param)].reverse
param.size.times do |j|
sum += ((param[j].chr=='1') ? 1.0 : 0.0) * (2.0 ** j.to_f)
end
min, max = bounds
vector << min + ((max-min)/((2.0**bits_per_param.to_f)-1.0)) * sum
end
return vector
end
def fitness(candidate, search_space, param_bits)
candidate[:vector]=decode(candidate[:bitstring], search_space, param_bits)
candidate[:fitness] = objective_function(candidate[:vector])
end
def binary_tournament(pop)
i, j = rand(pop.size), rand(pop.size)
j = rand(pop.size) while j==i
return (pop[i][:fitness] < pop[j][:fitness]) ? pop[i] : pop[j]
end
def point_mutation(bitstring, rate=1.0/bitstring.size)
child = ""
bitstring.size.times do |i|
bit = bitstring[i].chr
child << ((rand()<rate) ? ((bit=='1') ? "0" : "1") : bit)
end
return child
end
def crossover(parent1, parent2, rate)
return ""+parent1 if rand()>=rate
child = ""
parent1.size.times do |i|
child << ((rand()<0.5) ? parent1[i].chr : parent2[i].chr)
end
return child
end
def reproduce(selected, pop_size, p_cross, p_mut)
children = []
selected.each_with_index do |p1, i|
p2 = (i.modulo(2)==0) ? selected[i+1] : selected[i-1]
p2 = selected[0] if i == selected.size-1
child = {}
child[:bitstring] = crossover(p1[:bitstring], p2[:bitstring], p_cross)
child[:bitstring] = point_mutation(child[:bitstring], p_mut)
children << child
break if children.size >= pop_size
end
return children
end
def bitclimber(child, search_space, p_mut, max_local_gens, bits_per_param)
current = child
max_local_gens.times do
candidate = {}
candidate[:bitstring] = point_mutation(current[:bitstring], p_mut)
fitness(candidate, search_space, bits_per_param)
current = candidate if candidate[:fitness] <= current[:fitness]
end
return current
end
def search(max_gens, search_space, pop_size, p_cross, p_mut, max_local_gens, p_local, bits_per_param=16)
pop = Array.new(pop_size) do |i| {:bitstring=>random_bitstring(search_space.size*bits_per_param)}
end
pop.each{|candidate| fitness(candidate, search_space, bits_per_param) }
gen, best = 0, pop.sort{|x,y| x[:fitness] <=> y[:fitness]}.first
max_gens.times do |gen|
selected = Array.new(pop_size){|i| binary_tournament(pop)}
children = reproduce(selected, pop_size, p_cross, p_mut)
children.each{|cand| fitness(cand, search_space, bits_per_param)}
pop = []
children.each do |child|
if rand() < p_local
child = bitclimber(child, search_space, p_mut, max_local_gens, bits_per_param)
end
pop << child
end
pop.sort!{|x,y| x[:fitness] <=> y[:fitness]}
best = pop.first if pop.first[:fitness] <= best[:fitness]
puts ">gen=#{gen}, f=#{best[:fitness]}, b=#{best[:bitstring]}"
end
return best
end
if __FILE__ == $0
# problem configuration
problem_size = 3
search_space = Array.new(problem_size) {|i| [-5, +5]}
# algorithm configuration
max_gens = 100
pop_size = 100
p_cross = 0.98
p_mut = 1.0/(problem_size*16).to_f
max_local_gens = 20
p_local = 0.5
# execute the algorithm
best = search(max_gens, search_space, pop_size, p_cross, p_mut, max_local_gens, p_local)
puts "done! Solution: f=#{best[:fitness]}, b=#{best[:bitstring]}, v=#{best[:vector].inspect}"
end
ps: This code is an implementation of a Memetic Algorithms , which in turn is a variation of an Evolutionary Algorithm.
A Memetic Altorithms technically search the best solution to a problem in a number of solutions from an integrated global search results to an optimization every cycle / generation that selects variations of a good solution from a local search results.
My goal is to translate the code to Matlab programming.