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Alife Digest Number 062
Alife Digest, Number 062
Tuesday, September 17th 1991
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~ Artificial Life Distribution List ~
~ ~
~ All submissions for distribution to: alife@cognet.ucla.edu ~
~ All list subscriber additions, deletions, or administrative details to: ~
~ alife-request@cognet.ucla.edu ~
~ All software, tech reports to Alife depository through ~
~ anonymous ftp at polaris.cognet.ucla.edu in ~ftp/pub/alife ~
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~ List maintainers: Liane Gabora and Rob Collins ~
~ Artificial Life Research Group, UCLA ~
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Today's Topics:
Positions in Bioinformatics
ALife/GA Bibliography
Connectionist Navigation
Evolution Machine Software Available
Graduate Assistantship in Ecological Modelling
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CALENDAR OF ALIFE-RELATED ACTIVITIES:
First European Conference on Artificial Life Dec 11-13, 1991
ECAI 92, 10th European Conference on AI Aug 3-7, 1992
Parallel Problem Solving from Nature, Brussels Sep 28-30, 1992
10th National Conference on AI, San Jose Jul 12-17, 1992
Canadian AI Conference, Vancouver May 11-15, 1992
(Send announcements of other activities to alife@cognet.ucla.edu)
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Date: Mon, 9 Sep 1991 14:09 MET
From: HESPER@rulcri.leidenuniv.nl
Subject: Positions in Bioinformatics
Dear Colleague,
I am happy to announce the availability of two (tenured) positions
(Universitair Docent (UD) equivalent to assistant/associate professor) in:
BIOINFORMATICS.
at the
Theoretical Biology/Bioinformatics Group,
Faculty of Biology, University of Utrecht,
the Netherlands
We are looking for scientists (with several years of post-doc experience)
with a broad interest in the fundamental aspects of informatic processes
in biotic systems.
Their duties include: Research and Teaching.
Their research should focus on topics like informatic processing, coding,
micro-macro transitions, selfstructuring, pattern generation etc. in
biotic systems. Relevant biotic systems include (the evolutionary dynamics of)
e.g. metabolic/genetic regulatory systems, behavioral systems, ecosystems,
developmental systems, neuronal systems, immune systems, DNA coding structure,
etc.
The complexity of these processes requires versatile use of the
methods of 'experimental mathematics'; our primary goal is to obtain
many particular insights rather than formal proofs.
We believe that ultimately we can understand biotic systems only by also
comparing them to other, not necessarily similar, systems. Therefore, research
in what has become known as 'artificial life' may fit into this profile.
The following courses are scheduled: Introduction to Theoretical Biology
(compulsory for biology majors), Bioinformatics, Nonlinear systems,
Pattern analysis and Theoretical Ecology.
This teaching load will be shared by the two new staff members and myself.
Supervising the research of graduate students is expected; funding for
PhD students should be obtained through grant proposals.
At present the group consists of myself, one post-doc, 3 promovendi
(PhD students) and 4 doctoral students (Master's students).
We offer a relatively undisturbed atmosphere dedicated to fundamental
research and teaching in which this new and promising inroad into
understanding biotic systems can flourish.
Please bring this notice to the attention of anyone who may fit
this profile.
For further information please contact me at the adress below.
Applications, including curriculum vitae, list of publications,
current research, and long(er) term research interest, should be
received before October 1 1991.
Prof. Dr. Pauline Hogeweg
Theoretical Biology/Bioinformatics
Padualaan 8 3584CH Utrecht
the Netherlands
tel 31-30-533692. Fax 31-30-513655
EMAIL Bioinf@cc.ruu.nl
------------------------------
Subject: ALife/GA bibliography
Date: Wed, 21 Aug 91 15:28:09 PDT
From: todd%galadriel@Forsythe.Stanford.EDU
By popular demand, we are now distributing our short provisional ALife/GA/
evolution/psychology/other-neat-stuff bibliography to the whole list. As
we've indicated, this bibliography is purposefully short and idiosyncratic; we
have not tried to make it complete, but rather, useful, with pointers to
important works in a variety of areas. We hope it will lead people down
interesting paths as they pursue some of these topics. We do not intend to
keep a complete and definitive *public* list of papers and books in these
areas, but perhaps someone else would like to volunteer on behalf of the ALife
mailing list.... (And we certainly welcome comments and suggestions on things
we've missed and things to include in *potential* future lists!)
enjoy--
Peter Todd
Geoffrey Miller
(this list was originally intended to fit on two sides of a page when printed
sideways in two column, 8-pt type--hence the run-overs when viewed in normal
80-column mode--but there's no guarantees it'll fit that any more....)
******************************************************************************
A Short, Selective, and Provisional
GA, Evolution, and Artificial Life Bibliography
prepared by Geoffrey F. Miller (geoffrey@psych.stanford.edu)
and Peter M. Todd (todd@psych.stanford.edu)
References organized by topic; * = highly recommended and topical.
This list is also available by email from the addresses above.
BASIC EVOLUTIONARY THEORY
Dawkins, R. (1986). The blind watchmaker. W.W. Norton.
Dawkins, R. (1982). The extended phenotype. W.H. Freeman.
Dawkins, R. (1976). The selfish gene. Oxford U. Press.
Dupre, J. (Ed.). (1987). The latest on the best: Essays on evolution and
optimality. MIT Press.
* Eldredge, N. (1989). Macroevolutionary dynamics: Species, niches, and
adaptive peaks. McGraw-Hill.
Futuyama, D.J. (1986). Evolutionary biology, 2nd Edition. Sinauer Associates.
* Richards, R.J. (1987). Darwin and the emergence of evolutionary
theories of mind and behavior. U. Chicago Press.
Williams, G.C. (1966). Adaptation and natural selection. Princeton U. Press.
EVOLUTIONARY COMPARATIVE PSYCHOLOGY
Alcock, J. (1989). Animal behavior: An evolutionary approach (4th Ed.).
Sinauer Assocociates.
Camhi, J.M. (1984). Neuroethology. Sinauer Associates.
Driver, P.M., & D.A. Humphries. (1988). Protean behavior: The biology of
unpredictability. Clarendon Press.
Ewert, J.P. (1980). Neuroethology. Springer-Verlag.
Gould, J.J. (1982). Ethology: The mechanisms and evolution of behavior. W.W. Norton.
Hoyle, G. (1984). The scope of neuroethology. Behavioral and Brain Sciences 7: 367-412.
Krebs, J.R., & Davies, N.B. (Eds.). (1984). Behavioral ecology: An
evolutionary approach. Blackwell Scientific.
MacKay, D.G. (1987). The organization of perception and action. Springer-Verlag.
McFarland, D., & A. Houston (1981). Quantitative ethology: The state-space
approach. Pitman Books.
Pearce, J.M. (1987). An introduction to animal cognition. Lawrence Erlbaum.
Roitblat, H.L. (1987). Introduction to comparative cognition. W.H. Freeman.
Slater, P.J.B. (1985). An introduction to ethology. Cambridge U. Press.
Stephens, D.W., & J.R. Krebs (1986). Foraging Theory. Princeton U. Press.
EVOLUTION AND MOTIVATION
Colgan, P. (1989). Animal Motivation. Chapman & Hall.
Gallistel, C.R. (1980). The organization of action. Lawrence Erlbaum.
Maes, P. (1990). How to do the right thing. Connection Science 1(3).
Tinbergen, N. (1951). The study of instinct. Oxford U. Press.
Toates, F. (1986). Motivational systems. Cambridge U. Press.
Tolman, E.C. (1932). Purposive behavior in animals and men. Appleton-Century-Crofts.
EVOLUTION AND LEARNING
Belew, R.K. (1990). Evolution, learning, and culture: Computational metaphors
for adaptive search. Complex Systems 4: 11-49.
Bolles, R.C., & Beecher, M.D. (Eds.). (1988). Evolution and learning. Lawrence Erlbaum.
Davey, G. (1989). Ecological learning theory. Routledge.
Gallistel, C.R. (1991). The organization of learning. Lawrence Erlbaum.
Gardner, R.A., & B.T. Gardner (1988). Feedforward vs. feedback: An ethological
alternative to the law of effect. Behavioral and Brain Sciences 11: 429-493.
* Hinton, G.E., & Nowlan, S.J. (1987). How learning can guide evolution.
Complex systems 1: 495-502.
Lee, Y.C. (Ed.). (1988). Evolution, learning, and cognition. World Scientific.
Marler, P., & H.S. Terrace. (Eds.). (1984). The biology of learning. Springer-Verlag.
Maynard Smith, J. (1987). When learning guides evolution. Nature 329: 761-762.
Staddon, J.E.R. (1983). Adaptive behavior and learning. Oxford U. Press.
Todd, P. M., & Miller, G. F. (1991). Exploring adaptive agency II: Simulating
the evolution of associative learning. In Meyer and Wilson (Animals to Animats).
Todd, P. M., & Miller, G. F. (1991). Exploring adaptive agency III: Simulating
the evolution of habituation and sensitization. In Schwefel and Manner (PPSN).
Also see many other papers in Meyer and Wilson (Animals to Animats) and
Schwefel and Manner (PPSN).
EVOLUTIONARY HUMAN PSYCHOLOGY
Cosmides, L. (1989). The logic of social exchange: Has natural selection
shaped how humans reason? Cognition 31: 187-276.
Rozin, P. (1976). The evolution of intelligence and access to the cognitive
unconscious. In Sprague, J.M., & Epstein, A.N. (Eds.), Progress in
psychobiology and physiological psychology. Academic Press.
Shepard, R.N. (1987). Evolution of a mesh between principles of the mind and
regularities of the world. In Dupre, J. (Ed.), The latest on the best:
Essays on evolution and optimality. MIT Press.
Shepard, R.N. (1989). Internal representation of universal regularities:
A challenge for connectionism. In Nadel, L. et al. (Eds.),
Neural connections and mental computations. MIT Press.
Tooby, J., & Cosmides, L. (1990). The past explains the present: Emotional
adaptations and the structure of the ancestral environment. Ethology and
Sociobiology 11: 375-424.
* Tooby, J., Cosmides, L., and Barkow, J. (Eds.). (In Press). The adapted
mind. Oxford U. Press.
ARTIFICIAL LIFE
Beer, R.D. (1990). Intelligence as adaptive behavior: An experiment in
computational neuroethology. Academic Press.
Braitenberg, V. (1984). Vehicles. MIT Press.
Brooks, R.A. (1987). Intelligence without representations. In Proceedings
of the Workshop on Foundations of Intelligence. MIT Press.
Forrest, S.J. (Ed.). (1990). Emergent computation: Self-organizing,
collective, and cooperative computing networks. MIT Press.
* Langton, C.L. (1989). Artificial Life. MIT Press. [ALife I]
* Langton, C.L., J.D. Farmer, S. Rasmussen, & C. Taylor (Eds.). (1991).
Artificial Life II. Addison-Wesley. [ALife II]
Simon, H.A. (1982). The sciences of the artificial. MIT Press.
Toffoli, T., & N. Margolus (1987). Cellular automata machines. MIT Press.
GENETIC ALGORITHMS
Davis, L. (Ed.). (1987). Genetic algorithms and simulated annealing. Pitman Press.
* Goldberg, D.E. (1989). Genetic algorithms in search, optimization, and
machine learning. Addison-Wesley.
Grefenstette, J.J. (1985). Proceedings of the First International Conference on
Genetic Algorithms. Lawrence Erlbaum.
Grefenstette, J.J. (1987). Proceedings of the Second International Conference on
Genetic Algorithms. Lawrence Erlbaum.
Holland, J. (1975). Adaptation in natural and artificial systems.
University of Michigan Press.
Schaffer, J.D. (1989). Proceedings of the Third International Conference on Genetic
Algorithms. Morgan Kaufmann. [ICGA-3]
OTHER EVOLUTIONARY MODELING APPROACHES AND METHODS
Axelrod, R. (1984). The evolution of cooperation. Basic Books.
Fogel, L.J., A.J. Owens, & M.J. Walsh (1966). Artificial intelligence through
simulated evolution. Wiley.
Kauffman, S., & S. Levin. (1987). Towards a general theory of adaptive walks
on rugged landscapes. J. Theoretical Biology 128: 11-45.
Kauffman, S.A. (1990). Origins of order: Self-organization and selection in
evolution. Oxford U. Press.
Lendrem, D. (1986). Modelling in behavioral ecology: An introductory text. Timber Press.
Maynard Smith, J. (1982). Evolution and the theory of games. Cambridge U. Press.
Schwefel, H.-P., & Manner, R. (Eds.). (1991). Parallel problem solving from
nature. Springer-Verlag. [PPSN]
GENETIC ALGORITHMS AND NEURAL NETWORKS
Belew, R.K., J. McInerney, & N.N. Schraudolph (1990). Evolving networks:
Using the genetic algorithm with connectionist learning. CSE Tech. Rep.
CS90-174, UCSD.
Chalmers, D.J. (1990). The evolution of learning: An experiment in genetic
connectionism. In D.S. Touretzky et al. (Eds.), Proceedings of the
1990 Connectionist Models Summer School. Morgan Kaufmann.
Cecconi, F., & D. Parisi (1991). Evolving organisms that can reach for
objects. In Meyer and Wilson (Animals to Animats).
Hancock, P.J.B., & L.S. Smith (1991). GANNET: Genetic design of a neural
net for face recognition. In Schwefel and Manner (PPSN).
Harp, S.A., T. Samad, & A. Guha (1989). Towards the genetic synthesis of
neural networks. In Schaffer (ICGA-3).
Heistermann, J. (1991). The application of a genetic approach as an
algorithm for neural networks. In Schwefel and Manner (PPSN).
Hoffgen, K.-U., H.P. Siemon, & A. Ultsch, (1991). Genetic improvements
in feedforward nets for approximating functions. In Schwefel and Manner (PPSN).
Kitano, H. (1990). Designing neural networks using genetic algorithms with
graph generation system. Complex Systems 4: 461-476.
Miller, G.F., P.M. Todd, & S.U. Hedge (1989). Designing neural networks using
genetic algorithms. In Shaffer (ICGA-3).
Parisi, D., F. Cecconi, & S. Nolfi (1990). ECONETS: Neural networks that
learn in an environment. Network 2: 1-21.
Rudnick, M. (1990). A bibliography of the intersection of genetic search
and artificial neural networks. Tech. Rep. CS/E 90-001, Oregon Graduate
Institute, University of Oregon.
Stork, D.G., & R. Keesing (In press). Evolution and learning in neural networks.
In D.S. Touretzky (Ed.), Neural Information Processing Systems III.
Weiss, G. (1990). Combining neural and evolutionary learning: Aspects and
approaches. Institut fur Informatik Tech. Rep., Technische Universitat Munchen.
Whitley, D.W., & T. Hanson (1989). Optimizing neural networks using faster,
more accurate genetic search. In Schaffer (ICGA-3).
EVOLUTIONARY APPROACHES TO ARTIFICIAL LIFE
* Ackley, D.H., & Littman, M.L. (1991). Learning from natural selection in an
artificial environment. In Langton et al. (ALife II).
Collins, R.J., & D.R. Jefferson (1991). AntFarm: A progress report. In
Langton et al. (ALife II).
Collins, R.J., & D.R. Jefferson (1991). Representations for artificial
organisms. In Meyer and Wilson (Animals to Animats).
Harvey, I. (1991). The artificial evolution of behavior. In Meyer and Wilson
(Animals to Animats).
Jefferson, D. et al. (1991). The GeneSys System: Evolution as a theme in
artificial life. In Langton et al. (ALife II).
Koza, J.R. (1991). Evolution and co-evolution of computer programs to control
independently-acting agents. In Meyer and Wilson (Animals to Animats).
* Meyer, J.-A., & S.W. Wilson (Eds.). (1991). Proceedings of the First
International Conference on Simulation of Adaptive Behavior: From Animals
to Animats. MIT Press/Bradford Books. [Animals to Animats]
Miller, G.F. (1991). The evolution of Protean behavior strategies: An endless
arms race between prediction and evasion. Unpublished manuscript, Psychology
Dept., Stanford University.
Paredis, J. (1991). The evolution of behavior: Some experiments. In Meyer
and Wilson (Animals to Animats).
Wood, D. (1991). A von Neumann approach to a genotype expression in a neural
animat. In Meyer and Wilson (Animals to Animats).
COMPUTATIONAL APPROACHES TO GENERATING ADAPTIVE BEHAVIOR
Agre, P.E., & Chapman, D. (1987). Pengi: An implementation of a theory of
activity. In Proceedings of AAAI-87, pp. 268-272.
Booker, L.B. (1988). Classifier systems that learn internal world models.
Machine Learning 3: 161-192.
Grossberg, S. (1988). Neural networks and natural intelligence. MIT Press/Bradford Books.
Holland, J., K.J. Holyoak, R.E. Nisbett, & P.R. Thagard. (1986). Induction:
Processes on inference, learning, and discovery. MIT Press.
Minsky, M.C. (1986). The society of mind. Simon and Schuster.
Rumelhart, D.E., & McClelland, J.L. (1986). Parallel distributed processing.
MIT Press/Bradford Books.
Sutton, R.S. (1990). Integrated architectures for learning, planning, and
reacting based on approximating dynamic programming. In Machine Learning:
Proceedings of the Seventh International Conference. Morgan Kaufmann.
Wilson, S.W. (1987). Classifier systems and the animat problem. Machine
Learning 3(2): 199-228.
THEORETICAL ISSUES
Clark, C.W. (1991). Modelling behavioral adaptations. Behavioral and Brain
Sciences 14(1): 85-117. (See also commentary by Miller and Todd.)
Cliff, D.T. (1990). Computational neuroethology: A provisional manifesto. In
Meyer and Wilson (Animals to Animats).
Graubard (Ed.). (1988). The artificial intelligence debate: False starts,
real foundations. MIT Press.
Harnad, S. (1990). The symbol grounding problem. Physica D 42: 335-346.
Hookway, C. (Ed.). (1984). Minds, machines, and evolution. Cambridge U. Press.
Lloyd, D. (1989). Simple Minds. MIT Press/Bradford Books.
Mangel, N., & Clark, C.W. (1988). Dynamic modeling in behavioral ecology.
Princeton U. Press.
* Miller, G. F., & Todd, P. M. (1990). Exploring adaptive agency I: Theory and
methods for simulating the evolution of learning. In Touretzky, D.S. et
al. (Eds.), Proceedings of the 1990 Connectionist Models Summer School.
Morgan Kaufmann.
Schull, J. (1990). Are species intelligent? Behavioral and Brain Sciences 13: 63-108.
SOME RELEVANT JOURNALS
Biology/Ethology: Animal Behavior, Behavior, Evolution, Journal of
Mathematical Biology, Journal of Theoretical Biology, Nature.
Psychology: Behavioral and Brain Sciences, Cognition and Emotion,
Cognitive Science, Ethology and Sociobiology, Evolution and Cognition,
Human Nature, Journal of Comparative Psychology, Psychological Review.
Modeling etc.: Adaptive Behavior (forthcoming), Artificial Life (forthcoming),
Complex Systems, Physica D, Neural Computation, Connection Science,
Neural Networks.
------------------------------
Date: Tue, 3 Sep 91 21:04:42 MET DST
From: Bernd Rosauer <rosauer@ira.uka.de>
Subject: connectionist navigation
I think it would be interesting -- at least to some people -- to
compile a bibliography on connectionist approaches to locomotion and
navigation. Since I started collecting references on that topic some
time ago I will take on the job.
The bibliography should include references to work on modeling spatial
orientation, cognitive mapping, piloting, and navigation; alife
simulations of animat navigation and applications of neural networks
to mobile robot obstacle avoidance and path planning. Further
suggestions are welcome.
In order to bound my own efforts in surveying the references I would
like to encourage (a) those people who know that I have some
references to their work to send me a complete list, and (b) even to
send me references to older work you are aware of. Although I do not
promise to finish the bibliography this month I expect that it could
be done this year.
So feel free to send me your references. (I know that similar
requests have come up in several mailing lists and news groups now and
then but I for myself have never seen a summary, even on direct
request.)
Thanks in advance,
Bernd Rosauer
Research Center of Computer Science
at the University of Karlsruhe, FRG
------------------------------
Date: 9 Sep 91 13:04 +0200
From: Joachim Born <born@iir-berlin.adw.dbp.de>
Subject: Evolution Machine Software Available
Announcement
"The Evolution Machine" - v 2.1
We offer the software package "The Evolution Machine". The "Evolution
Machine" presents a collection of evolutionary algorithms (Genetic
Algorithms and Evolution Strategies) in a common framework.
The "Evolution Machine" includes extensive menu techniques. It runs on
PC's with MS-DOS.
A detailed description of the "Evolution Machine" is given by the
manual of the "Evolution Machine".The manual can be found on the
FTP-server ftp.wtza-berlin.de (141.16.244.4) .The file em-man.ps.Z
contains the complete manual.
In this manual, an introduction is given, the handling is fully
described and the included algorithms are compared with regard to
their performance.
Interested parties can order the code of the "Evolution Machine" free
of charge. A request is to send to one of the authors:
Hans-Michael Voigt Joachim Born
Email: voigt@iir-berlin.adw.dbp.de Email: born@iir-berlin.adw.dbp.de
Tel: (00372) 674 5958 Tel: (00372) 674 2484
Address:
Institute for Informatics and Computing Techniques
Rudower Chaussee 5
D - 1199 Berlin
------------------------------
Date: Thu, 12 Sep 91 13:25:37 CDT
From: jfolse@orca.tamu.edu (Joseph Folse)
Subject: Graduate Assistantship in Ecological Modelling
POSITION ANNOUNCEMENT
Graduate Research Assistant
Ph.D. Program
Spatial Simulation and Individual-Based Models
in
Wildlife & Natural Resource Management
CONTACT:
Dr. L. Joseph Folse
Department of Wildlife & Fisheries Sciences
Texas A&M University
College Station, TX 77845-2258
Phone: (409) 845-5777
FAX: (409) 845-3786
Email: jfolse@orca.tamu.edu
I am seeking one (perhaps more pending funding) highly
interested student for a Ph.D. program and to become a member of our
research team (Ecological Systems Laboratory) in developing ecological
simulation models.
Our work is computer-oriented, developing object-oriented
simulation models of ecological systems with an emphasis on spatially
explicit models. Our current research interests include: animal
movements in heterogeneous environments (deer, ticks, and cattle in
semi-arid environments); spatial dynamics of cluster formation in
arid-land plant communities; individual-based modelling of small
animal populations (breeding cycle dynamics of Chimpanzees); and
programming support for spatial simulation and analysis with interface
to Geographic Information Systems (GIS).
The Wildlife & Fisheries Sciences Department at Texas A&M
University focuses on a multidisciplinary approach to ecological
conservation and natural resource management. The Ecological Systems
Laboratory also has close working relationships with several other
laboratories on campus, including Agricultural Engineering (image
processing and spatial analysis), Industrial Engineering (Biosystems
Group), Computer Science, Entomology (Knowledge Engineering
Laboratory), Forestry (GIS/Remote Sensing Laboratory), and Range
Science.
You, as a prospective student, should have a strong background
in ecology or animal biology, a good ecological/biological intuition,
some programming experience, and be enthusiastic about the
possibilities of combining computers and ecology. You also need to
qualify for the Ph.D. program at Texas A&M University (although I will
consider an unusually qualified student for a M.S. program). We will
provide the training opportunities and encouragement for you to become
skilled in object-oriented simulation in one or more of our research
areas. Although our work is computer-oriented, you will have
opportunity to participate with others in field work if you wish.
PROGRAMMING ENVIRONMENTS: Our main work is in Common Lisp (with CLOS-
Common Lisp Object System) in X Windows on UNIX workstations. We also
work with C/C++ and FORTRAN on a variety of platforms including PCs,
UNIX workstations, VAXen, and a Cray YMP supercomputer. Our campus
computer network facilities are outstanding.
If interested, please contact me via Email or US Mail with a
general description of your background and interests (I keep odd
hours, so phone calls don't get through as often as they should!).
------------------------------
End of ALife Digest
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