
Genetic Algorithms Digest   Thursday, April 1, 1993   Volume 7 : Issue 6

 - Send submissions to GA-List@AIC.NRL.NAVY.MIL
 - Send administrative requests to GA-List-Request@AIC.NRL.NAVY.MIL
 - anonymous ftp archive: FTP.AIC.NRL.NAVY.MIL (Info in /pub/galist/FTP)

Today's Topics:
	- Genetic Algorithms vs Tailored Heuristics
	- Request for information - Genetic Algorithms applied to Neural Nets
	- Request for Information on Evolutionary/Genetic Programming
	- GAs and route / tactical planning
	- New TCGA Report
	- Genetic Neural Network Research Report
	- Evolutionary Robotics Tech Reports(GA used to develop NN controllers)

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CALENDAR OF GA-RELATED ACTIVITIES: (with GA-List issue reference)

ECML-93, European Conf. on Machine Learning, Vienna (v6n26)	Apr 05-07, 93
Intl. Conf. on Neural Networks and GAs, Innsbruck (v6n22)       Apr 13-16, 93
ECAL-93, 2nd European Conference on A-Life, Brussels (v6n31)    May 24-26, 93
CSCS93, 9th Int Conf on control systems & CS, Romania (v7n3)    May 24-27, 93
ANN93, IEE Intl Conf on Artificial Neural Nets, Brighton        May 25-27, 93
ICGA-93, Fifth Intl. Conf. on GAs, Urbana-Champaign (v6n29)     Jul 17-22, 93
COLT93, ACM Conf on Computational Learning Theory, UCSC (v6n34) Jul 26-28, 93
Machine Learning & Knowledge Acq. Workshop (IJCAI), France (v7n1)  Aug 29, 93
IEE/IEEE Workshop on Nat Alg in Signal Processing, Essex (v7n5) Nov 15-16, 93
ISEC-94 Int. Symp. on Evolutionary Computation, Orlando (v6n40) Jun 25-30, 94

(Send announcements of other activities to GA-List@aic.nrl.navy.mil)

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------------------------------

From: "James P. Ignizio" <jpi6g@fulton.seas.virginia.edu>
Date: Tue, 9 Feb 93 09:51:10 -0500
Subject: Genetic Algorithms vs Tailored Heuristics

   GENETIC ALGORITHMS VS TAILORED HEURISTICS

   James P. Ignizio
   University of Virginia
   ignizio@virginia.edu

   For the past several years I have been conducting, albeit on an
   admittedly limited scale, an evaluation of genetic algorithms ---
   particularly those GA adaptations used and/or proposed for the
   solution of problems of combinatorial optimization (e.g.,
   problems of scheduling, layout, location, configuration,
   knapsack, etc.). This endeavor was motivated by the numerous
   reports in the literature (as well as in this digest) of the
   implementation of the GA approach for such problem types ----
   coupled with the fact that the vast majority of papers on genetic
   algorithms appear to simply ignore (and certainly fail to cite)
   alternative heuristic approaches.

   I have found that virtually every effort (i.e., for such specific
   problem types) for which I have been able to obtain details
   sufficient for replication could have been approached via means
   of existing, tailored heuristics (i.e., specifically designed
   heuristic methods that have appeared, over the years, in the open
   literature; i.e., in the Operations Research/Management Science
   and Industrial Engineering journals and textbooks in particular).
   As a consequence of this observation, I have been comparing the
   computational performance of these tailored heuristics with that
   of genetic algorithms on the basis of such measures as:

   Solution Value (i.e., which approach provides the best solution
   in terms of the measure, or measures of solution performance)

   Computation Time

   Computational Resources Required

   Solution Replication

   I have thus far been able to compare the performance of the two
   approaches on more than 30 problems of combinatorial
   optimization. To date, I have found that the performance of the
   tailored heuristics has either dominated or at least equaled that
   of the genetic algorithms in every instance.

   Based upon this study, I would like to offer two (possibly
   unpopular) suggestions to the GA community:

   1. Before proceeding directly to the use of genetic algorithm,
   consider doing a simple literature review so as to establish
   whether or not there already exists an efficient tailored
   heuristic approach for the problem at hand. While a literature
   survey may sound like a truly radical concept (particularly when
   it involves the search of material outside the AI/GA community),
   it could possibly result in a significant savings in time --- as
   well as improved results.

   2. Realize that, while the term *genetic algorithms* has a
   certain seductive quality, GAs are but one representation of
   heuristic solution methods.

   Please appreciate that this is not a call to abandon research in
   genetic algorithms. Believe it or not, I am an advocate of the
   approach ---- if used by the right people, on the right problem,
   in the right manner.

------------------------------

From: James Robertson <jamesr@extro.ucc.su.OZ.AU>
Date: Tue, 2 Mar 93 12:25:03 EST
Subject: Request for information

   I am a 4th year (honours) Applied Mathematics student doing a project in
   Genetic Algorithms applied to Neural Networks. However, I've been having
   problems finding references for this field, and I would prefer not to
   duplicate existing work. Does anyone know any references (papers, books,
   etc) about this that they have come across.

   Please send anything (at all) to:

   Internet: jamesr@minnie.cs.su.oz.au
       Real: James Robertson
	     4th year honours
	     Department of Applied Mathematics
	     Sydney University  NSW  2006
	     Australia

   Thanks in advance.

------------------------------


From: Steve Greig <steve@computer-studies.napier.ac.uk>
Date: Wed, 3 Mar 93 17:11:04 GMT
Subject: Request for Information on Evolutionary/Genetic Programming

   Currently, a colleague and I are trying to initiate a pan-european ESPRIT 
   funded project in the area of evolutionary/genetic programming, and have 
   decided to turn to the GA community for some assistance (should you want 
   to give it).

   In referring to evolutionary/genetic programming, we are more inclined
   towards the use of GAs in the breeding of computer programs (*) as opposed
   to GENNETs or GANNETs which are used to produce neural networks.

   * For further information, refer to J.R. Kosa's "Genetic Programming", MIT
     Press, 1991.

   We are looking for a "few" things:

       (a) recent references on evolutionary/genetic programming (inc. if these
	   can be obtained via an anonymous ftp site).  We shall, of course,
           make the completed bibliography available to anyone when it is
           completed, probably in BibTeX format.

       (b) examples of any work that is going on at the moment, both individual
	   projects and large-scale projects.  We don't want people to give
           away any closely guarded secrets, but we're just trying to avoid 
	   duplication.

       (c) people who are interested in getting involved.  We are very keen to 
	   develop this project into a full international collaboration, and 
	   would welcome any input from others (who probably know a lot more
           than we do).

   Finally, other than making the bibliography available, we aim also to
   produce a survey report detailing the current state-of-the-art in
   evolutionary/genetic programming.  This will also be made available.

   Thanks in advance,

	   Steve Greig <steve@uk.ac.napier.cs>
	   Pete Barclay <pete@uk.ac.napier.cs>

	   Computer Studies Department,
	   Napier University, Edinburgh

------------------------------

From: pellazar@lava.nrtc.northrop.com
Date: Mon, 1 Mar 93 9:21:56 PST
Subject: GAs and route / tactical planning

This is a request for bibliography references or in-progress information on 
the application of genetic algorithms or classifier systems to:

	* single- or multi-air-vehicle route planning (optimization)
	* multi-agent coordinated tactical planning
	* aircraft mission planning (and dynamic in-flight replanning)

	Your help would be greatly appreciated,
		mpellazar@lava.nrtc.northrop.com

------------------------------

From: Robert Elliott Smith <rob@comec4.mh.ua.edu>
Date: Tue, 09 Feb 93 09:44:54 -0600
Subject: New TCGA Report

   The Clearinghouse for Genetic Algorithms at the University of Alabama
   announces the availability of the following technical report:


   Adaptively Resizing Populations: An Algorithm and Analysis
   TCGA Report #93001
   Robert E. Smith
   Department of Engineering Mechanics

   Deciding on an appropriate population size for a given GA application can
   often be critical to the algorithm's success. Too small, and the GA can
   fall victim to sampling error, effecting the efficacy of its search. Too
   large, and the GA wastes computational resources. Although advice exists
   for sizing GA populations, much of this advice involves theoretical aspects
   that are not accessible to the novice user. This paper suggests an algorithm
   for adaptively resizing GA populations. This algorithm is based on recent
   theoretical developments that relate population size to schema fitness
   variance. The suggested algorithm is described and simulated with expected
   value equations. The simulations suggest that the algorithm may be a viable
   way to eliminate the population sizing decision from the application of GAs.

   You can obtain this paper from the following contact points:

   <<U.S. Mail>>
   Robert Elliott Smith
       Department of Engineering of Mechanics
       Room 210 Hardaway Hall
       The University of Alabama
       Box 870278
       Tuscaloosa, Alabama 35487
   <<email>> 
   rob@comec4.mh.ua.edu
   <<phone>> (205) 348-1618      <<fax>> (205) 348-6419    

------------------------------

From: Frederic Gruau <gruau@drfmc.ceng.cea.fr>
Date: 11 Mar 93 09:02:30+0000
Subject: Genetic Neural Network

   The Laboratory of Computer Science For Parallelism is
   pleased to announce the availability of the following
   Research Report:

	   The cellular developmental of neural networks:   
	   the interaction of learning and evolution

			   Authors: 

   Fr\'ed\'eric Gruau                       Darrell Whitley   
   C.E.N.G. DRFMC SP2M PSC                  Computer Science Department   
   BP 85X 38041 Grenoble, France            Colorado State University   
     LIP, ENS-Lyon   46 Allee d'Italie      Fort Collins, CO 80523   
     69007 Lyon, France                     USA    
   gruau@lip.ens-lyon.fr                    whitley@cs.colostate.edu   

   A grammar tree is used to encode a cellular developmental
   process that can develop whole families of Boolean neural
   networks for computing parity and symmetry.
   The development process resembles biological cell division.
   A genetic algorithm is used to find a
   grammar tree that yields both architecture and weights
   specifying a particular neural network
   for solving specific Boolean functions.
   The current study particularly focuses on the addition
   of learning to the development process and the evolution
   of grammar trees.  Three ways of adding learning to
   development process are explored.  Two of these exploit the
    Baldwin effect by changing the fitness landscape
   without using Lamarkian learning.  The third strategy
   is Lamarkian in nature.  Results for these three modes
   of combining learning with genetic search are compared
   against genetic search without learning.   Our results
   suggest that merely using learning to change the fitness
   landscape may be as effective as Lamarkian strategies
   at improving search.

   To get this report, make an anonymous FTP at: lip.ens-lyon.fr
   the file is: pub/LIP/RR/RR93/RR93-04.ps.Z

------------------------------

From: Inman Harvey <inmanh@cogs.sussex.ac.uk>
Date: Wed, 24 Mar 93 10:43:12 GMT
Subject: Evolutionary Robotics - Tech. Reports

  Evolutionary Robotics at Sussex -- Technical Reports
  ===============================

  The following six technical reports describe our recent work in using genetic
  algorithms to develop neural-network controllers for a simulated simple
  visually-guided robot.

  Currently only hard-copies are available. To request copies, mail one of:
  inmanh@cogs.susx.ac.uk or davec@cogs.susx.ac.uk or philh@cogs.susx.ac.uk
  giving a surface mail address and the CSRP numbers of the reports you want.

  or write to us at:
  School of Cognitive and Computing Sciences
  University of Sussex
  Brighton BN1 9QH
  England, UK.

  ------------ABSTRACTS--------------------

  Genetic convergence in a species of evolved robot control architectures
  I. Harvey, P. Husbands, D. Cliff
  Cognitive Science Research Paper CSRP267
  February 1993
  We analyse how the project of evolving 'neural' network controller for
  autonomous visually guided robots is significantly different from the usual
  function optimisation problems standard genetic algorithms are asked to
  tackle.  The need to have open ended increase in complexity of the
  controllers, to allow for an indefinite number of new tasks to be
  incrementally added to the robot's capabilities in the long term, means that
  genotypes of arbitrary length need to be allowed. This results in
  populations being genetically converged as new tasks are added, and needs a
  change to usual genetic algorithm practices. Results of successful runs are
  shown, and the population is analysed in terms of genetic convergence and
  movement in time across sequence space.


  Analysing recurrent dynamical networks evolved for robot control
  P. Husbands, I. Harvey, D. Cliff
  Cognitive Science Research Paper CSRP265
  January 1993
  This paper shows how a mixture of qualitative and quantitative analysis can
  be used to understand a particular brand of arbitrarily recurrent continuous
  dynamical neural network used to generate robust behaviours in autonomous
  mobile robots.  These networks have been evolved in an open-ended way using
  an extended form of genetic algorithm.  After briefly covering the background
  to our research, properties of special frequently occurring subnetworks are
  analysed mathematically. Networks evolved to control simple robots with low
  resolution sensing are then analysed, using a combination of knowledge of
  these mathematical properties and careful interpretation of time plots of
  sensor, neuron and motor activities.

  Analysis of evolved sensory-motor controllers
  D. Cliff, P. Husbands, I. Harvey
  Cognitive Science Research Paper CSRP264
  December 1992
  We present results from the concurrent evolution of visual sensing
  morphologies and sensory-motor controller-networks for visually guided
  robots.  In this paper we analyse two (of many) networks which result from
  using incremental evolution with variable-length genotypes. The two networks
  come from separate populations, evolved using a common fitness function. The
  observable behaviours of the two robots are very similar, and close to the
  optimal behaviour. However, the underlying sensing morphologies and
  sensory-motor controllers are strikingly different. This is a case of
  convergent evolution at the behavioural level, coupled with divergent
  evolution at the morphological level. The action of the evolved networks is
  described. We discuss the process of analysing evolved artificial networks, a
  process which bears many similarities to analysing biological nervous systems
  in the field of neuroethology.

  Incremental evolution of neural network architectures for adaptive behaviour
  D. Cliff, I. Harvey, P. Husbands
  Cognitive Science Research Paper CSRP256
  December 1992
  This paper describes aspects of our ongoing work in evolving recurrent
  dynamical artificial neural networks which act as sensory-motor controllers,
  generating adaptive behaviour in artificial agents. We start with a
  discussion  of the rationale for our approach. Our approach involves the use
  of recurrent networks of artificial neurons with rich dynamics, resilience to
  noise (both internal and external); and separate excitation and inhibition
  channels. The networks allow artificial agents (simulated or robotic) to
  exhibit adaptive behaviour. The complexity of designing networks built from
  such units leads us to use our own extended form of genetic algorithm, which
  allows for incremental automatic evolution of controller-networks.  Finally,
  we review  some of our recent results, applying our methods to work with
  simple visually-guided robots. The genetic algorithm generates useful network
  architectures from an initial set of randomly-connected networks. During
  evolution, uniform noise was added to the activation of each neuron. After
  evolution, we studied two evolved networks, to see how their performance
  varied when the noise range was altered. Significantly, we discovered that
  when the noise was eliminated, the performance of the networks degraded: the
  networks use noise to operate efficiently.

  Evolving visually guided robots
  D. Cliff, P. Husbands, I. Harvey
  Cognitive Science Research Paper CSRP220
  July 1992
  We have developed a methodology grounded in two beliefs: that autonomous
  agents need visual processing capabilities, and that the approach of
  hand-designing control architectures for autonomous agents is likely to be
  superseded by methods involving the artificial evolution of comparable
  architectures. In this paper we present results which demonstrate that
  neural-network control architectures can be evolved for an accurate simula-
  tion model of a visually guided robot. The simulation system involves
  detailed models of the physics of a real robot built at Sussex; and the
  simulated vision involves ray-tracing computer graphics, using  models of
  optical systems which could readily be constructed from discrete components.
  The control-network architecture is entirely under genetic control, as are
  parameters governing the optical system. Significantly, we demonstrate that
  robust visually-guided control systems evolve from evaluation functions which
  do not explicitly involve monitoring visual input. The latter part of the
  paper discusses work now under development, which allows us to engage in
  long-term fundamental experiments aimed at thoroughly exploring the
  possibilities of concurrently evolving control networks and visual sensors
  for navigational tasks. This involves the construction of specialised
  visual-robotic equipment which eliminates the need for simulated sensing.

  Issues in evolutionary robotics
  I. Harvey, P. Husbands, D. Cliff
  Cognitive Science Research Paper CSRP219
  July 1992
  In this paper we propose and justify a methodology for the development of
  the control systems, or `cognitive architectures', of autonomous mobile
  robots. We argue that the design by hand of such control systems becomes
  prohibitively difficult as complexity increases. We discuss an alternative
  approach, involving artificial evolution, where the basic building blocks for
  cognitive architectures are adaptive noise-tolerant dynamical neural networks
  rather than programs. These networks may be recurrent, and should operate in
  real time. Evolution should be incremental, using an extended and modified
  version of genetic algorithms. We finally propose that, sooner rather than
  later, visual processing will be required in order for robots to engage in
  non-trivial navigation behaviours. Time constraints suggest that initial
  architecture evaluations should be largely done in simulation. The pitfalls
  of simulations compared with reality are discussed, together with the 
  importance of incorporating noise. To support our claims and proposals, we
  present results from some preliminary experiments where robots which roam
  office-like environments are evolved.

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End of Genetic Algorithms Digest
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