xor_experiment.m

这个是关于neural fuzzy 算法的工具箱, 有例子程序, 请您用winzip解压缩

%%%%%%%%%%%%%%%% XOR experiment file  (contains experiment, receives genom, decodes it, evaluates it and returns raw fitnesses) (function)

%% Neuro_Evolution_of_Augmenting_Topologies - NEAT 
%% developed by Kenneth Stanley ([e-mail]) & Risto Miikkulainen ([e-mail])
%% Coding by Christian Mayr ([e-mail])

function population_plus_fitnesses=xor_experiment(population);
population_plus_fitnesses=population;
no_change_threshold=1e-3; %threshold to judge if state of a node has changed significantly since last iteration
number_individuals=size(population,2);

input_pattern=[0 0;
               0 1; 
               1 0;
               1 1];
output_pattern=[0;
                1;
                1;
                0];
             
for index_individual=1:number_individuals   
   number_nodes=size(population(index_individual).nodegenes,2);
   number_connections=size(population(index_individual).connectiongenes,2);
   individual_fitness=0;
   output=[];
   for index_pattern=1:4
      % the following code assumes node 1 and 2 inputs, node 3 bias, node 4 output, rest arbitrary (if existent, will be hidden nodes)
      % set node input steps for first timestep
      population(index_individual).nodegenes(3,4:number_nodes)=0; %set all node input states to zero
      population(index_individual).nodegenes(3,3)=1; %bias node input state set to 1
      population(index_individual).nodegenes(3,1:2)=input_pattern(index_pattern,:); %node input states of the two input nodes are consecutively set to the XOR input pattern  
      
      %set node output states for first timestep (depending on input states)
      population(index_individual).nodegenes(4,1:3)=population(index_individual).nodegenes(3,1:3);
      population(index_individual).nodegenes(4,4:number_nodes)=1./(1+exp(-4.9*population(index_individual).nodegenes(3,4:number_nodes)));
      no_change_count=0;     
      index_loop=0;
      while (no_change_count<number_nodes) & index_loop<3*number_connections
         index_loop=index_loop+1;
         vector_node_state=population(index_individual).nodegenes(4,:);
         for index_connections=1:number_connections
            %read relevant contents of connection gene (ID of Node where connection starts, ID of Node where it ends, and connection weight)
            ID_connection_from_node=population(index_individual).connectiongenes(2,index_connections);
            ID_connection_to_node=population(index_individual).connectiongenes(3,index_connections);
            connection_weight=population(index_individual).connectiongenes(4,index_connections);
            %map node ID's (as extracted from single connection genes above) to index of corresponding node in node genes matrix
            index_connection_from_node=find((population(index_individual).nodegenes(1,:)==ID_connection_from_node));
            index_connection_to_node=find((population(index_individual).nodegenes(1,:)==ID_connection_to_node));
                        
            if population(index_individual).connectiongenes(5,index_connections)==1 %Check if Connection is enabled
               population(index_individual).nodegenes(3,index_connection_to_node)=population(index_individual).nodegenes(3,index_connection_to_node)+population(index_individual).nodegenes(4,index_connection_from_node)*connection_weight; %take output state of connection_from node, multiply with weight, add to input state of connection_to node
            end
         end
         %pass on node input states to outputs for next timestep 
         population(index_individual).nodegenes(4,4:number_nodes)=1./(1+exp(-4.9*population(index_individual).nodegenes(3,4:number_nodes)));          
         %Re-initialize node input states for next timestep
         population(index_individual).nodegenes(3,4:number_nodes)=0; %set all output and hidden node input states to zero
         no_change_count=sum(abs(population(index_individual).nodegenes(4,:)-vector_node_state)<no_change_threshold); %check for alle nodes where the node output state has changed by less than no_change_threshold since last iteration through all the connection genes
      end      
      if index_loop>=2.7*number_connections
         index_individual
         population(index_individual).connectiongenes
      end
      output=[output;population(index_individual).nodegenes(4,4)];
      individual_fitness=individual_fitness+abs(output_pattern(index_pattern,1)-population(index_individual).nodegenes(4,4)); %prevent oscillatory connections from achieving high fitness
   end
   population_plus_fitnesses(index_individual).fitness=(4-individual_fitness)^2; %Fitness function as defined by Kenneth Stanley    
   if sum(abs(round(output)-output_pattern))==0      
      population_plus_fitnesses(index_individual).fitness=16;
      output
   end
end