Knowledge and population swarms in cultural algorithms for dynamic environments
Knowledge and population swarms in cultural algorithms for dynamic environments
Knowledge-Inducing interactive genetic algorithms based on multi-agent
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
Interactive genetic algorithms based on implicit knowledge model
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
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In complex ground environment, different regions have different road conditions. Path planning for robots in such environment is an open problem, which lacks effective methods. A novel global path planning method based on common sense and evolution knowledge is proposed by adopting dual evolution structure in culture algorithms. Common sense describes ground information and feasibility of environment, which is used to evaluate and select the paths. Evolution knowledge describes the angle relationship between the path and the obstacles, or the common segments of paths, which is used to judge and repair infeasible individuals. Taken two types of environments with different obstacles and road conditions as examples, simulation results indicate that the algorithm can effectively solve path planning problem in complex ground environment and decrease the computation complexity for judgment and repair of infeasible individuals. It also can improve the convergence speed and have better computation stability.