Lecture Notes in Artificial Intelligence

Subseries of Lecture Notes in Computer Science

Edited by J. G. Carbonell and J. Siekmann

Lecture Notes in Computer Science Edited by G. Goos, J. Hartmanis and J. van Leeuwen

929

E Morfin A. Moreno J.J. Merelo E Chac6n (Eds.)

Advances m Artificial Life

Third European Conference on Artificial Life Granada, Spain, June 4-6, 1995 Proceedings

Springer

Series Editors

Jaime G. Carbonell School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213-3891, USA

J6rg Siekmann University of Saarland German Research Center forArtificial Intelligence (DFKI) Stuhlsatzenhausweg 3, D-66123 SaarbriJcken, Germany

Volume Editors

Federico Mor~in Departamento de Bioqufmica, Universidad Complutense de Madrid E-28040 Madrid, Spain

Alvaro Moreno Departamento de L6gica y Filosofia de la Ciencia, Universidad del Pals Vasco Apdo. 1249, E-20080 San Sebasti~in, Spain

Juan Juli~in Merelo Departamento de Electr6nica, Universidad de Granada Campus Fuentenueva, E-18071 Granada, Spain

Pablo Chac6n Centro de Investigaciones Biol6gicas, CSIC Vehizquez 144, E-28006 Madrid, Spain

CR Subject Classification (1991): 1.2, J.3, E l . l - 2 , G.2, H.5.1, 1.5.1, J.4, J.6

ISBN 3-540-59496-5 Springer-Verlag Berlin Heidelberg New York

CIP data applied for

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, re-use of illustrations, recitation, broadcasting, reproduction on microfilms 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 for prosecution under the German Copyright Law.

�9 Springer-Verlag Berlin Heidelberg 1995 Printed in Germany

Typesetting: Camera ready by author SPIN: 10486232 06/3142 - 543210 - Printed on acid-free paper

~Qu~ es h vida? Un frens[. LQud es la vida? Una ilusi6n,

una sombra, una ficci6n, el mayor bien es peque~o;

que toda vida es sue~o, y los sueffos, suer~os son.

(Calder6n de la Barca. La Vida es Sue~o. 1635)

... La vida es siempre un jugarse la vida al naipe de unos ciertos valores, y, po t eso, toda vida tiene estilo - bueno o malo, personal o vulgar,

creado originalmente o recibido del contorno -. (Josd Ortega y Gasset. IntroducciSn a Veldzquez. San Sebastian, 1947)

Pre face

Despite its short history, Artificial Life (ALife) is already becoming a mature scientific field. By trying to discover the rules of life and extract its essence so that it can be implemented and investigated in artificial media in different media, ALife research is leading us to a better understanding of a large set of interesting biology-related problems, such as self-organization, emergence, the origins of life, self-reproduction, computer viruses, learning, growth and development, animal behavior, ecosystems, autonomous agents, adaptive robotics, etc.

Understanding biological phenomena is a complex task and, like all disci- plines of the sciences of complexity, it needs a multidisciplinary approach. From its origins, ALife has been oriented in two main directions: applying of biologi- cally inspired solutions to the development of new techniques and methods; and the use of artificial media (particularly computation) to model, replicate, and investigate life processes.

ALife is now turning into a consolidated field. The number of online (WWW, USENET newsgroup, ftp sites, and forums in services like CompuServe or Amer- ica Online) and offtine (books and journals) is already huge and, besides, is in- creasing every day. ALife is slowly slipping into the mainstream of technology. More and more companies use biologically inspired techniques to solve prob- lems, optimize solutions, simulate or model real-life situations, etc. Autonomous robots with biologically-based or evolved behavior are becoming more widely studied and used.

On the other hand, theoretical biology and ALife are coming closer and closer. Although, at this point, only a level of awareness has been reached between them, soon these fields will interact to a greater extent and probably profit from this interaction. It is our opinion, and that of many others, that the future survival of ALife is highly related to the presence of people from biology in the field. As stated above, the big success of the biologically inspired development of new techniques and methods could strongly influence and bias the future direction

vI

of ALife. the big success of biologically-inspired applications in technology could bias ALife excessively in this direction. We are convinced that the approaches and methods of ALife are important in understanding life, and we should work to maintain the interest and involvement of biologists, biochemists, geneticist, ecologists, and many other naluralscientists in the exciting and important field of ALife.

This book contains the 71 papers, including oral presentations and invited lectures, presented at the 3rd European Conference on Artificial Life, ECAL'95, held in Granada (Spain) from 4 to 6 June, 1995. ECAL'95 was organized by the Universidad Complutense de Madrid, Universidad del Pais Vasco, and Univer- sidad de Granada. More than 160 papers were received for oral presentation at the Conference. All submitted papers were rigorously reviewd by members of the Program Committee, and only those with a minimum of two positive referee reports were accepted for oral presentation and included in this publication. This process is not perfect, but it is some guarantee of the scientific quality of the papers included in this volume, and that it therefore represents some of the best work currently being done in the field of ALife.

The book is organised according to the eight scientific sessions of ECAL'95, preceeded by the opening invited lecturer given by Prof Peter Schuster. In ad- dition to the classical topics of ALife we have paid special attention to issues related to biology. Topics like the origins of life, the evolution of metabolism, protein design, or ecosystem behavior(are good examples of this.

We would like to thank very much all the authors for their contributions, and for their patience and efforts in re-formating their manuscripts in time to produce this book version of the proceedings for the Confereilce. We also wish to express our gratitude to all members of the Organization, Local, and Program Committee. Most of them had between 8 and 12 submitted papers to review and all of whom worked very hard to do a careful and thorough job.

We would also like to thank all the sponsors of ECAL'95: the International Society for the Study of the Origin of Life (ISSOL), the Spanish RIG IEEE Neural Networks Council, Silicon Graphics Spain, IBM Spain, the Parque de las Ciencias de Granada, and in particular the organisations which have contributed towards the costs of the Conference, the EC-DG-XII Biotechnology, the DGI- CYT (MEC, Spain), the CICYT (MEC, Spain), the Junta de Andalucia, and the Universidad de Granada. Our special thanks to all the unamed people who have worked these Organisations to help make ECAL'95 a success. Finally, but not least, we are very grateful for the all support and hard work of the Technical Secretariat, GESTAC, during the organisation and running of the Conference.

Madrid, April 1995

Federico Morfin Alvaro Moreno

Juan Julis Merelo Pablo Chac6n

Organization Committee

Federico Mors Alvaro Moreno Juan J. Merelo Pablo Chac6n Arantza Etxeberria Julio Fernandez Tim Smithers Carme Torras

U. Complutense Madrid (E) U. Pa/s Vasco, San Sebastis (E) U. Granada (E) U. Complutense, Madrid (E) U. Pals Vasco, San Sebasti~hl (E) U. Peds Vasco, San Sebastign (E) U. Pa/s Vaseo, San Sebastis (E) U. Politec. Catalunya, Barcelona (E)

Chair Chair Secretary

Program Committee

Francisco Varela Riceardo Antonini Michael Arbib Randall D. Beer Wolfgang Banzhaf George Bekey Hugues Bersini Paul Bourgine Rodney Brooks Scott Camazine Peter Cariani Michael Conrad Jaques Demongeot Jean-L. Deneubourg Michael Dyer Claus Emmeche Walter Fontana Brian C. Goodwin Ricard Guerrero Reinhart Heinrich Pauline Hogeweg Philip Husbands George Kampis Kunhiko Kaneko Hiroaki Kitano John Koza Chris Langton Antonio Lazcano Pier L. Luisi Pattie Maes Pedro C. Mariju~n Maja J. Mataric

CNRS/CREA, Paris (F) U. Rome (I) USC, Los Angeles, CA (USA) CWRU, Cleveland, OH (USA) Dortmund University (D) USC, Los Angeles, CA (USA) ULB, Brussels (B) CEMAGREF, Antony (F) MIT, Cambridge, MA (USA) Wissenschaftskolleg, Berlin (D) MEEI, Boston, MA (USA) Wayne State U., Detroit, MI (USA) U. J. Fourier, La Tronche (F) ULB, Brussels (B) UCLA, Los Angeles, CA (USA) U. of Rosekilde, (DK) U. of Vienna, (A) Open U., Milton Keynes (UK) U, Barcelona (E) Humbolt Univ. Berlin (D). U. of Utrecht, (NL) U. of Sussex, Brighton (UK) ELTE Univ. Budapest (H) University of Tokyo (JP) Sony Comp. Sci. Lab., Tokyo (JP) Stanford U., CA (USA) Santa Fe Institute, NM (USA) UNAM, M~xico (MX) ETtIZ, Zurich (CH) MIT, Cambridge, MA (USA) U. Zaragoza, (E) MIT, Cambridge, MA (USA)

Chair

VIII

Barry MacMullin Robert M. May Eric Minch Melanie Mitchell AndrOs Moya Francisco Montero Jim D. Murray Juan C. Nufio Julio Ortega Domenico Parisi Mukesh Patel Howard Pattee Francisco J. Pelayo Juli Peret6 Rolf Pfeifer Alberto Prieto Steen Rasmussen Tom Ray Robert Rosen Chris Sander Peter Schuster Hans Paul Schwefel Mosher Sipper Luc Steels John Stewart Jon Um~rez Alfonso Valencia G/inter Wagner Hans V. Westerhoff William C. Winsatt Ren~ Zapata

Dublin City U., Dublin (IE) Oxford U. (UK). Stanford U., CA (USA) Santa Fe Institute, NM (USA) U. Valencia, (E) U. Cornplutense, Madrid (E) U. of Washington, Seattle, WA (USA) U. Politecnica de Madrid, (E) U. Granada (E) CNR, Roma (I) Politecnico di Milano, Milan (I) SUNY, Binghamton, NY (USA) U. Granada (E) U. Valencia, (E) U. Zurich-Irchel, Zurich (CH) U. Granada (E) LANL, Los Alamos, NM (USA) ETL (JP) Dalhousie U. Halifax (CA) EMBL, Heidelberg (D) IMB, Jena (D) Dortmud U. (D) Tel Aviv U., Tel Aviv (IL) VUB, Brussels (B) Institut Pasteur, Paris (F) SUNY, Binghamton, NY (USA) CNB/CSIC, Madrid (E). Yale U., CT (USA). U. Amsterdam (NL). U. of Chicago, (USA) LIRM, Montpellier (F)

C o n t e n t s

Openning L e c t u r e

Artificial Life and Molecular Evolutionary Biology ( Invi ted pape r ) P. Schus te r . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1. Foundations and Epistemology

Artificial Life Needs a Real Epistemology ( Invi ted pape r ) H.H. P a t t e e . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

Grounding and the Entailment Structure in Robots and Artificial Life Erich P r e m . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

Mean Field Theory of the Edge of Chaos Howard Gu towi t z and Chris Lang ton . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

Escaping from the Cartesian Mind-Set: Heidegger and Artificial Life Michae l Whee l e r . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

Semantic Closure: A Guiding Notion to Ground Artificial Life Jon Umerez . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77

The Inside and Outside Views of Life George K a m p i s . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95

2. Origins of Life and Evolution

Prebiotic Chemistry, Artificial Life, and Complexity Theory: What Do They Tell us About the Origin of Biological Systems? ( Invi ted p a p e r ) A n t o n i o Lazcano . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105

Compartimentation in Replicator Models J u a n C. Nufio, P a b l o Chacdn , Alvaro Moreno, and Feder ico Mors . . . . . . 116

Evolutionary Dynamics and Optimization: Neutral Networks as Model- Landscapes for RNA Secondary-Structure Folding-Landscapes Chr i s t i an V. Fors t , Chis t i an Reidys , and Jacquel ine Weber . . . . . . . . . . . . . 128

Population Evolution in a Single Peak Fitness Landscape - How High are the Clouds? Glenn Woodcock and Pau l G. Higgs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148

Replicators Don't. t B a r r y McMul l in . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158

RNA Viruses: a Bridge Between Life and Artificial Life Andrds Moya, Es t eban Domingo and John J. Hol land . . . . . . . . . . . . . . . . . . . 170

Complexity Analysis of a Self-organizing vs. a Template-Directed System G a d Yagil . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179

•

Tile Automaton for Evolution of Metabolism Tomoyuki Y a m a m o t o and Kunih iko Kaneko . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 188

Tracking the Red Queen: Measurements of Adaptive Progress in Co-Evolutionary Simulations Dave Cliff and Geoffrey F. Mil ler . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 200

The Coevolution of Mutation Rates Car lo Maley . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219

Coevolution of Machines and Tapes Takashi Ikegami and Takashi t t a s h i m o t o . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 234

Incremental Co-Evolution of Organisms: A New Approach for Optimization and Discovery of Strategies Hugues Juill~ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 246

Symbiosis and Co-Evolution in Animals Chisa to N u m a o k a . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 261

Artificial Endosymbiosis Lawence Bull, Terence C. Fogarty, and A.G. P ipe . . . . . . . . . . . . . . . . . . . . . . . 273

Mathematical Analysis of Evolutionary Process T e t s u y a Maeshi ro and Masayuk i K i m u r a . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 290

The Evolution of Hierarchical Representations Franz Oppache r and Dwight Deugo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 302

3. A d a p t i v e a n d C o g n i t i v e S y s t e m s

Adaptation and the Modular Design of Organims ( Inv i ted pape r ) Gi in te r P. Wagner . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 317

A Theory of Differentiation with Dynamic Clustering Kunih iko Kaneko and Te t suya Yomo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 329

Cell Differentiation and Neurogenesis in Evolutionary Large Scale Chaos t t i roak i K i t ano . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 341

Evolving Artificial Neural Networks that Develop in Time Stefano Nolfi and Domenico Par is i . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 353

Contextual Genetic Algorithms: Evolving Developmental Rules Luis Ma teus R o c h a . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 368

Can Development Be Designed? What we May Learn from the Cog Project Jul ie C. Ru tkowska . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 383

Emergent Organization of Interspecies Communication in Q-Learning Artificial Organisms Norihiko Ono , T. Ohi ra , and A.T . R a h m a n i . . . . . . . . . . . . . . . . . . . . . . . . . . . . 396

Self and Nonself Revisited: Lessons from Modelling the Immune Network Jorge Carne i ro and John S tewar t . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 406

x[

On Formation of Structures Jari Vaario and Katsunori Shimohara . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 421

4. A r t i f i c i a l W o r l d s

Learning in the Active Mode.(Invited paper) Domenico Parisi and Federico Cecconi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 439

Learning Subjective "Cognitive Maps" in the Presence of Sensory-Motor Errors A.G. Pipe, B. Carse, T.C. Fogarty and A. Winfield . . . . . . . . . . . . . . . . . . . . . 463

Specialization Under Social Conditions in Shared Environments Henrik t tautop Lund . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 477

Iterated Prisoner's Dilemma with Choice and Refusal of Partners: Evolutionary Results E. Ann Stanley, Dan Ashlock, and Mark D. Smucker . . . . . . . . . . . . . . . . . . . . 490

Abundance-Distributions in Artificial Life and Stochastic Models: "Age and Area" Revisited Chris Adami, C. Titus Brown, and Michael R. ttaggerty . . . . . . . . . . . . . . . . 503

Elements of a Theory of Simulation Steen Rasmussen and Christopher L. Barrett . . . . . . . . . . . . . . . . . . . . . . . . . . . . 515

To Simulate or Not to Simulate: A Problem of Minimising Functional Logic Depth Gregory R. Mulhauser . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 530

Quasi- Uniform Computation- Universal Cellular Automata Moshe Sipper . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 544

A New Self-Reproducing Cellular Automaton Capable of Construction and Computation Gianluca Tempesti . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 555

Self-Inspection Based Reproduction in Cellular Automata Jesds Ib~fiez, Daniel Anabitarte, Iker Azpeitia, Oscar Barrera, Arkaitz Barrutieta, Haritz Blanco, and Francisco Echarte . . . . . . . . . . . . . . . 564

5. R o b o t i c s a n d E m u l a t i o n o f A n i m a l B e h a v i o r

Evaluation of Learning Performance of Situated Embodied Agents Maja J. Matari5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 579

Seeing in the Dark with Artificial Bats Kourosh Teimoorzadeh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 590

Navigating with an Adaptive Light Compass Dimitrios Lambrinos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 602

Collision Avoidance Using an Egocentric Memory of Proximity R. Zapata, P. L~pinay, and P. D6planques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 614

XII

A Useful Autonomous Vehicle with a Hierarchical Behavior Control Lufs Correia and A. Steiger-Garq~o . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 625

Evolving Electronic Robot Controllers that Exploit Hardware Resources Adrian Thompson . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 640

Classification as Sensory-Motor Coordination: A Case Study on Autonomous Agents Christian Scheier and Rolf Pfeifer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 657

High-Pass Filtered Positive Feedback. Decentralized Control of Cooperation Holk Cruse, Christ ian Bartl ing and Thomas Kindermann . . . . . . . . . . . . . . . . 668

Learning and Adaptivity: Enhancing Reactive Behaviour Architectures in Real-World Interaction Systems Miles Pebody . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 679

Interactivistm: a Functional Model of Representation for Behavior-based Systems Sunil Cherian and Wade Troxell . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 691

Noise and the Reality Gap: The Us'e of Simulation in Evolutionary Robotics Nick Jakobi, Phil Husbands, and Inman Harvey . . . . . . . . . . . . . . . . . . . . . . . . . 704

Essential Dynamical Structure in Learnable Autonomous Robots Jun Tani . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 721

Optimizing the Performance of a Robot Society in Structured Environment Through Genetic Algorithms Mika Vainio, Torsten SehSnberg, Aarne Halmest, and Peter Jakubik . . . . . 733

6. S o c i e t i e s a n d C o l l e c t i v e B e h a v i o r

Spatial Games and Evolution of Cooperation(Invited paper) Robert M. May, Sebastian Bohoeffer, and Mart in A. Nowak . . . . . . . . . . . . . 749

Aggressive Signaling Meets Adaptive Receiving: Further Experiments in Synthetic Behavioural Ecology Peter de Bourcier and Michael Wheeler . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 760

Modelling Foraging Behaviour of Ant Colonies R.P. Fletcher, C. Cannings, and P.G. Blackwell . . . . . . . . . . . . . . . . . . . . . . . . . 772

The Computationally Complete Ant Colony: Global Coordination in a System with No Hierarchy Michael Lachmann and Guy Sella . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 784

Mimicry and Coevolution of Hedonie Agents Paul Bourgine and Dominique Snyers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 801

Evolution of Symbolic Grammar Systems Takashi Hashimoto and Takashi Ikegami . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 812

Driven Cellular Automata, Adaptation and the Binding Problem Will iam Sulis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 824

XIII

7. Biocomputing

The Functional Composition of Living Machines as a Design Principle for Artificial Organisms Christos Ouzounis, Alfonso Valencia, Javier Tamames, Peer Bork, and Chris Sander . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 843

Thermodynamics of RNA Folding: When is an RNA Molecule in Equilibrium Paul G. IIiggs and Steven R. Morgan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 852

An Artificial Life Model for Predicting the Tertiary Structure of Unknown Proteins that Emulates the Folding Process Raffaele Calabretta, Stefano Nolfi, and Domenico Parisi . . . . . . . . . . . . . . . . . 862

Energy Cost Evaluation of Computing Capabilities in Biomolecular and Artificial Matter R. Lahoz-Beltra and S.R. Hameroff . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 876

8. A p p l i c a t i o n s a n d C o m m o n Tools

Contemporary Evolution Strategies(Invited paper) IIans-Paul Schwefel and Gfinter Rudolph . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 893

The Usefulness of Recombination Wim IIordijk and Bernard Manderick . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 908

The Investigation of Lamarckian Inheritance with Classifier Systems in a Massively Parallel Simulation Environment Eckhard Bartscht, Jens Engel, and Christian Mfiller-Schloer . . . . . . . . . . . . . 920

Orgy in the Computer: Multi-Parent Reproduction in Genetic Algorithms A.E. Eiben, C.tt.M van Kemenade, and J.N. Kok . . . . . . . . . . . . . . . . . . . . . . . 934

A Simplification of the Theory of Neural Groups Selection for Adaptive Control S. Lobo, A.J. Garcla-Tejedor, R. Rodriguez-Gals Luis L6pez, and A. Garcia-Crespo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 946

A u t h o r s I n d e x . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 959