Conditions for Outperformance of Recombination in Online Evolution of Swarm Robots

Genetic recombination is commonly used in evolutionary algorithms and yet its benefits are an open question in evolutionary biology. We investigate when recombination is actually beneficial in the evolutionary adaptation of swarm robot behaviour in dynamic environments. In this scenario, artificial evolution has to deal with challenges that are similar to natural evolution: it must run online, distributed and evolve the genome structure. These requirements could diminish the benefit of recombination due to disruptive crossover. Using neural networks as robot controllers, we reduce this disruptiveness with an adaptive mate choice that evolves the probability of recombination and the genetic similarity of mates. In two experiments with a multi-agent simulation, we compare the adaptive performance of this approach with random recombination and pure mutation. Whereas both recombination treatments naturally outperform at low mutation rates, pure mutation achieves its best performance with high rates, where it also outperforms random recombination. The adaptive mate choice, however, achieves the same performance as pure mutation at high rates and outperforms when the network size is increased. We also found that treatments with recombination evolved smaller neural networks with fewer links.