Table Of ContentN. Nedjah, L. M. Mourelle (Eds.)
Evolvable Machines
Studies in Fuzziness and Soft Computing, Volume 161
Editor-in-chief
Prof. Janusz Kacprzyk
Systems Research Institute
Polish Academy of Sciences
ul. Newelska 6
01-447 Warsaw
Poland
E-mail: [email protected]
Further volumes of this series Vol. 152. J. Rajapakse, L. Wang (Eds.)
Neural Information Processing: Research
can be found on our homepage:
and Development, 2004
springeronline.com
ISBN 3-540-21123-3
Vol. 144. Z. Sun, G.R. Finnie Vol. 153. J. Fulcher, L.C. Jain (Eds.)
Intelligent Techniques in E-Commerce, 2004 Applied Intelligent Systems, 2004
ISBN 3-540-20518-7 ISBN 3-540-21153-5
Vol. 145. J. Gil-Aluja Vol. 154. B. Liu
Fuzzy Sets in the Management of Uncertainty Theory, 2004
Uncertainty, 2004 ISBN 3-540-21333-3
ISBN 3-540-20341-9
Vol. 155. G. Resconi, J.L. Jain
Vol. 146. J.A. Gamez, S. Moral, A. Salmeron Intelligent Agen ts, 2004
(Eds.) ISBN 3-540-22003-8
Advances in Bayesian Networks, 2004
ISBN 3-540-20876-3 Vol. 156. R. Tadeusiewicz, M.R. Ogiela
Medical Image Understanding Technology,
Vol. 147. K. Watanabe, M.M.A. Hashem 2004
New Algorithms and their Applications to ISBN 3-540-21985-4
Evolutionary Robots, 2004
ISBN 3-540-20901-8 Vol. 157. R.A. Aliev, F. Fazlollahi, R.R. Aliev
Soft Computing and its Applications in
Vol. 148. C. Martin-Vide, V. Mitrana, Business and Economics, 2004
G. Pitun (Eds.) ISBN 3-540-22138-7
Formal Languages and Applications, 2004
ISBN 3-540-20907-7 Vol. 158. K.K. Dompere
Cost-Benefit Analysis and the Theory
of Fuzzy Decisions - Identification and
Vol. 149. J.J. Buckley Measurement Theory, 2004
Fuzzy Statistics, 2004 ISBN 3-540-22154-9
ISBN 3-540-21084-9
Vol. 159. E. Damiani, L.C. Jain, M. Madravia
Vol. 150. L. Bull (Ed.) Soft Computing in Software Engineering,
Applications ofLearning Classifier Systems, 2004
2004 ISBN 3-540-22030-5
ISBN 3-540-21109-8
Vol. 160. K.K. Dompere
Vol. 151. T. Kowalczyk, E. Pleszczyliska, Cost-Benefit Analysis and the Theory
F. Ruland (Eds.) ofFuzzy Decisions - Fuzzy Value Theory,
Grade Models and Methods for Data 2004
Analysis, 2004 ISBN 3-540-22161-1
ISBN 3-540-21120-9
Nadia Nedjah
Luiza de Macedo Mourelle (Eds.)
Evolvable Machines
Theory & Practice
a
-
Springer
Nadia Nedjah
Luiza de Macedo Mourelle
Universidade do Estado do Rio de Janeiro
Departamento de Engenharia de Sistemas e Computapio
Rua SBo Francisco Xavier, 524
MaracanH, CEP, 20550-900
Rio de Janeiro, RJ
Brazil
E-mail: [email protected]
[email protected]
ISSN 1434-9922
ISBN 3-540-22905-1 Springer Berlin Heidelberg New York
Library of Congress Control Number: 2004110948
This work is subject to copyright. All rights are reserved, whether the whole or part of the
material is concerned, specifically the rights of translation, reprinting, reuse of illustrations,
recitations, broadcasting, reproduction on microfilm or in any other way, and storage in data
banks. Duplication of this publication or parts thereof is permitted only under the provisions
of the German copyright Law of September 9, 1965, in its current version, and permission for
use must always be obtained from Springer-Verlag. Violations are liable to prosecution under
the German Copyright Law.
Springer is a part of Springer Science+Business Media
springeronline.com
0 Springer-Verlag Berlin Heidelberg 2005
Printed in Germany
The use of general descriptive names, registered names trademarks, etc. in this publication
does not imply, even in the absence of a specific statement, that such names are exempt from
the relevant protective laws and regulations and therefore free for general use.
Typesetting: data delivered by editors
Cover design: E. Kirchner, Springer-Verlag, Heidelberg
Printed on acid free paper 6213020lM - 5 4 3 2 1 0
To the memory of my father Ali and my beloved mother Fatiha,
Nadia
To my beloved parents Neuxa and Luix,
Luiza
Preface
Evolutionary algorithms are computer-based solving systems, which use the
evolutionary computational models as key element in their design and im-
plementation. A variety of evolutionary algorithms have been proposed. The
most popular ones are genetic algorithms. They have a conceptual base of
simulating the evolution of individual structures via the Darwinian natural
selection process. The process depends on the adherence of the individual
structures as defined by its environment to the problem pre-determined con-
straints. Genetic algorithms are well suited to provide an efficient solution of
hard problems.
Methods for artificial evolution of active components, such as programs
and hardware, are rapidly developing branches of adaptive computation and
adaptive engineering. The evolutionary process can produce, as results, com-
putational expressions, e.g. algorithms, or machines, e.g. mechanical or elec-
tronic devices. The evolved components generally present creativity as well as
inventiveness. Furthermore, they are usually efficient in terms of the specified
requirements.
This book is devoted to reporting innovative and significant progress in
automatic and evolutionary methodology of applied to machine design. The-
oretical as well as practical chapters are contemplated.
The content of this book is divided into three main parts. The first part
consists of four chapters while the second and third part have three chapters.
In the following, we give a brief description of the main contribution of each
of these chapters.
Part I: Evolvable Robots
In Chapter 1, which is entitled Learning for Cooperative Transportation by
Autonomous Humanoid Robots, the authors, Yutaka Inoue, Takahiro To-
VIII Preface
hge and Hitoshi Iba, first clarify the practical difficulties we face from the
cooperative transportation task with two bodies of humanoid robots. After-
wards, we propose a solution to these difficulties and empirically show the
effectiveness both by simulation and by real robots.
In Chapter 2, which is entitled Evolution, Robustness and Adaptation of
Sidewinding Locomotion of Simulated Snake-like Robot, the authors, namely
Ivan Tanev, Thomas Ray and Andrzej Buller, inspired by the effi-
cient method of locomotion of the rattlesnake, propose an automatic design
through genetic programming, of the fastest possible sidewinding locomotion
of simulated limbless, wheelless snake-like robot or snakebot. this work can be
considered as a step forward towards building real Snakebots that are able to
perform robustly in difficult environment.
In Chapter 3, which entitled Evolution of Khepera Robotic Controllers with
Hierarchical Genetic Programming Techniques, the authors, namely Marcin
L. Pilat and Franz Oppacher, show how to evolve robotic controllers for
a miniature mobile Khepera robot. They concentrate on control tasks for ob-
stacle avoidance, wall following, and light avoidance. The robotic controllers
are evolved through canonical GP implementation, linear genome GP system,
and hierarchical GP methods (Automatically Defined Functions, Module Ac-
quisition, Adaptive Representation through Learning).
In Chapter 4, which entitled Evolving Controllers for Miniature Robots,
the author, namely Michael Botros, presents a series of experiments in
evolutionary robotics that used the miniature mobile robot Khepera. Khepera
robot is widely used in evolutionary experiments due to its small size and
light weight which simplify the setup of the environments needed for the
experiments. The controllers evolved in the presented experiments include
classical and spiking neural networks controllers, fuzzy logic controllers and
computer program obtained by Genetic Programming.
Part 11: Evolvable Hardware Synthesis
In Chapter 5, which is entitled Evolutionary Synthesis of Synchronous Fi-
nite State Machines, the authors, namely Nadia Nedjah and Luiza de
Macedo Mourelle, propose an evolutionary methodology synthesise finite
state machines. First, they optimally solve the state assignment NP-complete
problem, which is inherent to designing any synchronous finite state machines
using genetic algorithms. This is motivated by the fact that with an optimal
state assignment one can physically implement the state machine in question
using a minimal hardware area and response time. Second, with the optimal
state assignment provided, we propose to use the evolutionary methodology
to yield optimal evolvable hardware that implement the state machine con-
trol component. The evolved hardware requires a minimal hardware area and
introduces a minimal propagation delay of the machine output signals.
Preface IX
In Chapter 6, which is entitled Automating the Hierarchical Synthesis of
MEMS Using Evolutionary Approaches, the authors, namely Zhun Fan, Ji-
achuan Wang, Kisung Seo, Jianjun Hu, Ronald Rosenberg, Janis
Terpenny and Erik Goodman, first discuss the hierarchy that is involved
in a typical MEMS design. Then they move on to discuss how evolutionary
approaches can be used to automate the hierarchical design and synthesis pro-
cess for MEMS. At the system level, genetic programming, as a strong search
tool, is used to generate and search in the topologically open-ended design
space. A multiple-resonator microsystem design is taken as an example to il-
lustrate the integrated design automation idea using evolutionary approaches
at multiple levels.
In Chapter 7, which is entitled An Evolutionary Approach to Multi-FPGAs
System Synthesis, the authors, namely F. Ferndndez de Veja, J.I. Hi-
dalgo, J.M. SQnchez and J. Lanchares, explain in details a methodology
for Multi-FPGA systems design. They describe a set of techniques based on
evolutionary algorithms, and we show that they are capable of solving all
of the design tasks, which are partitioning, placement and routing. Firstly
a hybrid compact genetic algorithm is used solves the partitioning problem
and then genetic programming is exploited to evolve a solution for the two
remaining tasks.
Part 111: Evolvable Designs
In Chapter 8, which is entitled Evolutionary Computation and Parallel Pro-
cessing Applied to the Design of Multilayer Perceptrons, the authors namely,
Ana Claudia M. L. Albuquerque, Jorge D. Melo and Adriiio D.
D6ria Neto, present the use of genetic algorithms in defining the neural net-
work's architecture and in refining its synaptic weights. A different approach
of a cooperative parallel genetic algorithm with different evolution behaviors
is given. Applications on approximation of functions will be illustrated.
In Chapter 9, which is entitled Evolvable Fuzzy Hardware for Real-time
Embedded Control in Packet Switching, the authors, namely Ju Hui Li,
Meng Hiot Lim, Qi Cao, describe a scheme to implement an Evolvable
Fuzzy Hardware for real-time Packet Switching Problem. The proposed evolv-
able fuzzy hardware addresses many issues effectively. For the hardware im-
plementation of the evolvable fuzzy hardware, real-time fuzzy inference with
high-speed context switching capability is necessary. This aspect is addressed
through implementation based on a context independent reconfigurable fuzzy
inference chip.
In Chapter 10, which is entitled Improving Multi Expression Programming:
An Ascending Trail from Sea-Level Even-3-Parity Problem to Alpine Even-18-
Parity Problem, the author, namely Mihai Oltean, proposes and uses several
techniques for improving the search performed by Multi Expression Program-
ming. Some of the most important improvements are Automatically Defined
X Preface
Functions and Sub-symbolic node representation. Several experiments with
Multi Expression Programming are performed in this chapter. Numerical re-
sults show that Multi Expression programming performs very well for the
considered test problems.
Nadia Nedjah, Ph.D.
Luiza de Macedo Mourelle, Ph.D.
Department of System Engineering & Computation
Faculty of Engineering
State University of Rio de Janeiro
(nadia I ldmm) @eng .u er j .b r
http://www.eng.uerj.br
Contents
Part I Evolvable Robots
1 Learning for Cooperative Transportation by Autonomous
Humanoid Robots
Yutaka Inoue. Takahiro Tohge. Hitoshi Iba .......................... 3
1.1 Introduction ................................................. 3
1.2 Problem in cooperative Transportation by humanoid Robots ....... 4
1.3 Approach of Transportation Control ............................ 7
1.4 Learning to Correct Positioning ................................ 9
1.4.1 Learning Model ......................................... 9
1A .2 Learning in Simulator .................................... 11
1.4.3 Result of Simulator Learning .............................. 11
1.4.4 Experiments with Real Robots ............................ 13
1.4.5 Experimental results ..................................... 15
1.5 Cooperative Transportation to Target Position ................... 15
1.5.1 Experiments with Real robots ............................. 15
1.5.2 Experimental results ..................................... 17
1.6 Discussion ................................................... 17
1.7 Conclusion .................................................. 19
References ...................................................... 19
Bibliography .................................................... 19
2 Evolution. Robustness and Adaptation of Sidewinding
Locomotion of Simulated Snake-like Robot
Ivan Tanev. Thomas Ray. Andrzej Buller ........................... 21
2.1 Introduction ................................................. 22
2.2 Approach ................................................... 23
2.2.1 Representation of Snakebot ............................... 23
2.2.2 Algorithmic paradigm .................................... 24
2.3 Experimental Results ......................................... 27
2.3.1 Evolution of fastest locomotion gaits ....................... 28
