The Effect of Bloat on the Efficiency of Incremental Evolution of Simulated Snake-Like Robot
We present the effect of bloat on the efficiency of incremental evolution of locomotion of simulated snake-like robot (Snakebot) situated in a challenging environment. In the proposed incremental genetic programming (IGP), the task of coevolving the locomotion gaits and sensing of the bot in a challenging environment is decomposed into two subtasks, implemented as two consecutive evolutionary stages. In the first stage we use genetic programming (GP) to evolve a pool of morphologically simple, sensorless Snakebots that move fast in a smooth, open terrain. Then, during the second stage, we use this pool to seed the initial population of Snakebots that are further subjected to coevolution of their locomotion control and sensing morphology in a challenging environment. The empirical results suggest that the bloat no immediate effect on the efficiency of the first stage of IGP. However, the bloated seed contributes to a much faster second stage of evolution. In average, the second stage with bloated seed reaches the best fitness values of the parsimony seeds about five times faster. We assume that this speedup is attributed to the neutral code that is used by IGP as an evolutionary playground to experiment with developing novel sensory abilities, without damaging the already evolved, fast locomotion of the bot.
KeywordsIncremetal genetic programming Bloat Neutrality
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