我不理解数组变量（最佳和向量）的Ruby引用关系以及函数的返回（适应性或 bitstring 例如）。
结束这个案子：
best[:fitness]
最佳是数组，适合度是一个函数，变量需要什么？以及该函数将引用什么？
查看完整代码：
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：此代码是Memetic算法的实现，而Memetic算法又是进化算法的变体。 Memetic Altorithms从技术上搜索解决了许多解决方案中问题的最佳解决方案，从集成的全局搜索结果到每个周期/代的优化，从本地搜索结果中选择优秀解决方案的变体。
我的目标是将代码转换为Matlab编程。