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...

In this article we describe a methodology for evolving neurocontrollers of autonomous mobile robots without human intervention. The presentation, which spans from technological and methodological issues to several experimental results on evolution of physical mobile robots, covers both previous and recent work in the attempt to provide a uni ed picture within which the reader can compare the effects of systematic variations on the experimental settings. After describing some key principles for building mobile robots and tools suitable for experiments in adaptive robotics, we give an overview of different approaches to evolutionary robotics and present our methodology. We start reviewing two basic experiments showing that different environments can shape very different behaviors and neural mechanisms under very similar selection criteria. We then address the issue of incremental evolution in two different experiments from the perspective of changing environments and robot morphologies. Finally, we investigate the possibility of evolving plastic neurocontrollers and analyze an evolved neurocontroller that relies on fast and continuously changes synapses characterized by dynamic stability. We conclude by reviewing the implications of this methodology for engineering, biology, cognitive science, and artificial life, and point at future directions of research.

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This article describes past and current research efforts in evolutionary robotics that have been carried out at the AnimatLab, Paris. Such approaches entail using an artificial selection process to automatically generate developmental programs for neural networks that control rolling, walking, swimming and flying animats or robots. Basically, they complement the underlying evolutionary process with a developmental procedure – in order hopefully to reduce the size of the genotypic space that is explored – and they occasionally call on an incremental approach, in order to capitalize upon solutions to simpler problems so as to devise solutions to more complex problems. This article successively outlines the historical background of our research, the evolutionary paradigm on which it relies, and the various results obtained so far. It also discusses the potentialities and limitations of the approach and indicates directions for future work.

A computer language is a very general form of representing and specifying an autonomous agent's behavior. The task of planning feasible actions could then simply be reduced to an instance of automatic programming. We have evaluated the use of an evolutionary technique for automatic programming called Genetic Programming (GP) to directly control a miniature robot. To our knowledge, this is the first attempt to control a real robot with a GP based learning method. Two schemes are presented. The objective of the GP system in our first approach is to evolve real-time obstacle avoiding behavior. This technique enables real-time learning with a real robot using genetic programming. It has, however, the drawback that the learning time is limited by the response dynamics of the environment. To overcome this problems we have devised a second method, learning from past experiences which are stored in memory. This new system allows a speed-up of the algorithm by a factor of more than 2000. Obstac...

A large training set of fitness cases can critically slow down genetic programming, if no appropriate subset selection method is applied. Such a method allows an individual to be evaluated on a smaller subset of fitness cases. In this paper we suggest a new subset selection method that takes the problem structure into account, while being problem independent at the same time. In order to achieve this, information about the problem structure is acquired during evolutionary search by creating a topology (relationship) on the set of fitness cases. The topology is induced by individuals of the evolving population. This is done by increasing the strength of the relation between two fitness cases, if an individual of the population is able to solve both of them. Our new topology–based subset selection method chooses a subset, such that fitness cases in this subset are as distantly related as is possible with respect to the induced topology. We compare topology–based selection of fitness cases with dynamic subset selection and stochastic subset sampling on four different problems. On average, runs with topology–based selection show faster progress than the others.

We have used an automatic programming method called Genetic Programming (GP) for control of a miniature robot. Our earlier work on real-time learning suffered from the drawback of the learning time being limited by the response dynamics of the robot's environment. In order to overcome this problem we have devised a new technique which allows learning from past experiences that are stored in memory. The new method shows its advantage when perfect behavior emerges in experiments quickly and reliably. It is tested on two control tasks, obstacle avoiding and wall following behavior, both in simulation and on the real robot platform Khepera.

. Recently there has been a great interest in the design and study of evolvable and autonomous systems in order to control the behavior of physically embedded systems such as a mobile robot. This paper studies an evolutionary navigation system for a mobile robot using an evolvable hardware (EHW) approach. This approach is unique in that it combines learning and evolution, which was usually realized by software, with hardware. It can be regarded as an attempt to make hardware "softer". The task of the mobile robot is to reach a goal represented by a colored ball while avoiding obstacles during its motion. We show that our approach can evolve a set of rules to perform the task successfully. We also show that the evolvable hardware system learned off-line is robust and able to perform the desired behaviors in a more complex environment which is not seen in the learning stage. 1 Introduction Robotics has until recently developed system able to automate mostly simple, repetitive and fixed...

We demonstrate the first instance of a real on-line robot learning to develop feasible flying (flapping) behavior, using evolution. Here we present the experiments and results of the first use of evolutionary methods for a flying robot. With nature's own method, evolution, we address the highly non-linear fluid dynamics of flying. The flying robot is constrained in a test bench where timing and movement of wing flapping is evolved to give maximal lifting force. The robot is assembled with standard off-the-shelf R/C servomotors as actuators. The implementation is a conventional steady-state linear evolutionary algorithm.

this paper, I shall present a survey of some work done in Evolutionary Robotics by myself and in collaboration with some colleagues, mainly Francesco Mondada and Stefano Nolfi. This paper intends to be a gentle introduction to Evolutionary Robotics and, therefore, it will not include much technical detail which interested readers will find in cited articles. The presentation will be structured according to two issues which I feel are important for practical use and application of Evolutionary Robotics: