1. Bäck, T., Hammel, U., Schwefel, H.P.: Evolutionary computation: Comments on the history and current state. IEEE Trans. on Evolutionary Comp. 1, 3–17 (1997)

   Article  Google Scholar 

2. Chen, M., Zalzala, A.M.S.: A genetic approach to motion planning of redundant mobile manipulator systems considering safety and configuration. Journal Robotic Systems 14, 529–544 (1997)

   Article MATH  Google Scholar 

3. Davidor, Y.: Genetic Algorithms and Robotics, a Heuristic Strategy for Optimization. World Scientific, Singapore (1991)

   MATH  Google Scholar 

4. Kubota, N., Arakawa, T., Fukuda, T.: Trajectory generation for redundant manipulator using virus evolutionary genetic algorithm. In: IEEE Int. Conf. on Robotics and Automation, Albuquerque, New Mexico, pp. 205–210 (1997)

   Google Scholar 

5. Rana, A., Zalzala, A.: An evolutionary planner for near time-optimal collisionfree motion of multi-arm robotic manipulators. In: UKACC International Conference on Control, vol. 1, pp. 29–35 (1996)

   Google Scholar 

6. Wang, Q., Zalzala, A.M.S.: Genetic control of near time-optimal motion for an industrial robot arm. In: IEEE Int. Conf. On Robotics and Automation, Minneapolis, Minnesota, pp. 2592–2597 (1996)

   Google Scholar 

7. Pires, E.S., Machado, J.T.: Trajectory optimization for redundant robots using genetic algorithms. In: Proc. of the Genetic and Evolutionary Computation Conference (GECCO-2000), Las Vegas, USA, vol. 967, Morgan Kaufmann, San Francisco (2000)

   Google Scholar 

8. Pires, E.J.S., Machado, J.A.T.: A GA perspective of the energy requirements for manipulators maneuvering in a workspace with obstacles. In: Proc. of the 2000 Congress on Evolutionary Computation, Piscataway, NJ, pp. 1110–1116 (2000)

   Google Scholar 

9. Chocron, O., Bidaud, P.: Evolutionary algorithms in kinematic design of robotic system. In: IEEE/RSJ Int. Conf. on Intelligent Robotics and Systems, Grenoble, France, pp. 279–286 (1997)

   Google Scholar 

10. Kim, J.O., Khosla, P.K.: A multi-population genetic algorithm and its application to design of manipulators. In: IEEE/RSJ Int. Conf. on Intelligent Robotics and Systems, Raleight, North Caroline, pp. 279–286 (1992)

    Google Scholar 

11. Han, J., Chung, W.K., Youm, Y., Kim, S.H.: Task based design of modular robotic manipulator using efficient genetic algorithm. In: IEEE Int. Conf. on Robotics and Automation, Albuquerque, New Mexico, pp. 507–512 (1997)

    Google Scholar 

12. Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison – Wesley, Reading (1989)

    MATH  Google Scholar 

13. Fonseca, C.M., Fleming, P.J.: An overview of evolutionary algorithms in multiobjective optimization. Evolutionary Computation Journal 3, 1–16 (1995)

    Article  Google Scholar 

14. Deb, K.: Multi-Objective Optimization using Evolutionary Algorithms. Wiley-Interscience Series in Systems and Optimization (2001)

    Google Scholar 

15. Horn, J., Nafploitis, N., Goldberg, D.: A niched pareto genetic algorithm for multi-objective optimization. In: Proc. of the First IEEE Conf. on Evolutionary Computation, pp. 82–87 (1994)

    Google Scholar 

16. Coello, C., Carlos, A.: A comprehensive survey of evolutionary-based M.-O. optimization techniques. Knowledge and Information Systems 1, 269–308 (1999)

    Google Scholar 

Download references