English: Method for evolving the neural network of a robot. Valid gene sequences are extracted (magnifying lens) from a binary string representing the genome of the robot. Those genes are translated into neurons of different type (colour) according to the genetic specifications, such as sensory, motor, excitatory, or inhibitory neurons. The corresponding neural network is connected to the sensors and motors of the robot and the resulting behaviour of the robot is measured according to the fitness function. The genomes of the individuals that had the worst performance are discarded from the population (symbolically thrown in a dustbin) whereas the genomes of the best individuals are paired and crossed over with small random mutations to generate new offspring (the process of selective reproduction is symbolically shown to occur in a “mother robot”). After several generations of selective reproductions with mutations, robots display better or novel behaviours. (7.82 MB MOV)