s: Jan. 1992 -- Dec. 1994 ffl CTI: Current Technology Index Jan./Feb. 1993 -- Jan./Feb. 1994 ffl DAI: Dissertation Abstracts International: Vol. 53 No. 1 -- Vol. 55 No. 4 (1994) ffl EEA: Electrical & Electronics Abstracts: Jan. 1991 -- Dec. 1994 ffl P: Index to Scientific & Technical Proceedings: Jan. 1986 -- Feb. 1995 (except Nov. 1994) ffl EI A: The Engineering Index Annual: 1987 -- 1992 ffl EI M: The Engineering Index Monthly: Jan. 1993 -- Dec. 1994 The following GA researchers have already kindly supplied their complete autobibliographies and/or proofread references to their papers: Dan Adler, Patrick Argos, Jarmo T. Alander, James E. Baker, Wolfgang Banzhaf, Ralf Bruns, I. L. Bukatova, Thomas Back, Yuval Davidor, Dipankar Dasgupta, Marco Dorigo, Bogdan Filipic, Terence C. Fogarty, David B. Fogel, Toshio Fukuda, Hugo de Garis, Robert C. Glen, David E. Goldberg, Martina Gorges-Schleuter, Jeffrey Horn, Aristides T. Hatjimihail, Mark J. Jakiela, Richard S. Judson, Akihiko Konaga...

We present a novel evolutionary approach to robotic control of a real robot based on genetic programming (GP). Our approach uses genetic programming techniques that manipulate machine code to evolve control programs for robots. This variant of GP has several advantages over a conventional GP system, such as higher speed, lower memory requirements and better real time properties. Previous attempts to apply GP in robotics use simulations to evaluate control programs and have difficulties with learning tasks involving a real robot. We present an on-line control method that is evaluated in two different physical environments and applied to two tasks using the Khepera robot platform: obstacle avoidance and object following. The results show fast learning and good generalization.

Genetic Programming (GP) is a machine learning technique that was not conceived to use domain knowledge for generating new candidate solutions. It has been shown that GP can benefit from domain knowledge obtained by other machine learning methods with more powerful heuristics. However, it is not obvious that a combination of GP and a knowledge intensive machine learning method can work better than the knowledge intensive method alone. In this paper we present a multi-strategy approach where an analytical and inductive approach (hamlet) and an evolutionary technique based on GP (EvoCK) are combined for the task of learning control rules for problem solving in planning. Results show that both methods complement each other, supplying to the other method what the other method lacks and obtaining better results than using each method alone. 1 INTRODUCTION Genetic Programming (GP) is a machine learning technique based on a search over a huge state space [ Koza and Rice, 1991 ] . Therefore, ...

s: July 1996 - Aug. 1997 ffl CA: Computer Abstracts: Jan. 1993 -- Feb. 1995 ffl CCA: Computer & Control Abstracts: Jan. 1992 -- Nov. 1997 (except May-95) ffl CTI: Current Technology Index Jan./Feb. 1993 -- Jan./Feb. 1994 ffl DAI: Dissertation Abstracts International: Vol. 53 No. 1 -- Vol. 56 No. 10 (Apr. 1996) ffl EEA: Electrical & Electronics Abstracts: Jan. 1991 -- Nov. 1997 ffl P: Index to Scientific & Technical Proceedings: Jan. 1986 -- Dec. 1997 (except Nov. 1994) ffl A: International Aerospace Abstracts: Jan. 1995 -- Nov. 1997 ffl N: Scientific and Technical Aerospace Reports: Jan. 1993 - Dec. 1995 (except Oct. 1995) ffl EI A: The Engineering Index Annual: 1987 -- 1992 ffl EI M: The Engineering Index Monthly: Jan. 1993 -- Nov. 1997 (except May 1997) 1.1 Your contributions erroneous or missing? The bibliography database is updated on a regular basis and certainly contains many errors and inconsistences. The editor would be glad to hear from any reader who notices any er...