"The genetic algorithm is a method for solving both constrained and unconstrained optimization problems that is based on natural selection, the process that drives biological evolution. The genetic algorithm repeatedly modifies a population of individual solutions"
The evolution of the peppered moth is an evolutionary instance of directional colour change in the moth population as a consequence of air pollution during the Industrial Revolution. The frequency of dark-coloured moths increased at that time, an example of industrial melanism. Later, when pollution was reduced, the light-coloured form again predominated. Industrial melanism in the peppered moth was an early test of Charles Darwin's natural selection in action, and it remains a classic example in the teaching of evolution. In 1978, Sewall Wright described it as "the clearest case in which a conspicuous evolutionary process has actually been observed." (Wikipedia).
In this project, the moths that survive the longest pass their characteristics on to their children.
The evolution of genetic algorithms for locomotion learning and obstacle overcoming is an example of directional change in the traits of virtual agents, resulting from the process of natural selection during simulation. The frequency of virtual agents capable of moving in the correct direction and overcoming obstacles increases over time, reflecting a case of genetic adaptation in challenging environments.
In this project, virtual agents that can survive longer and achieve their goals pass on their traits to future generations. Standout virtual agents in the simulation are more likely to transmit their skills and strategies to subsequent generations.