
Genetic Algorithms Digest    Thursday, 16 August 1990    Volume 4 : Issue 16

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Today's Topics:

	**** SPECIAL ISSUE OF WORKSHOP ABSTRACTS ****

	- Message from moderator
	- The Classifier System Concept Description Language
		Lashon Booker
	- A Study of Reproduction in Generational and Steady State GAs
		Gilbert Syswerda
	- Spurious Correlations and Premature Convergence in Genetic Algorithms
		J. David Schaffer, Larry J. Eshelman, and Daniel Offutt
	- The CHC Adaptive Search Algorith:  How to have Safe Search
	   When Engaging in Nontraditional Genetic Recombination
		Larry Eshelman
	- Weakest Conditions for Implicit Parallelism
		John Grefenstette
	- An Analysis of Multi-point Crossover
		K. De Jong & Wm. Spears

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

Conference on Simulation of Adaptive Behavior, Paris (v3n21)  Sep 24-28, 1990
Workshop Parallel Prob Solving from Nature, W Germany (v4n5)  Oct 1-3,   1990
2nd Intl Conf on Tools for AI, Washington, DC (v4n6)          Nov 6-9,   1990
4th Intl. Conference on Genetic Algorithms (v4n9)             Jul 14-17, 1991

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

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Date: Wed, 15 Aug 1990
From: Alan C. Schultz (GA-List Moderator)
Subject: Special issue

	This issue is comprised of abstracts from talks presented
	at the Foundations of Genetic Algorithms Workshop held
	July 15-18, 1990, at Indiana University.  The workshop
	was organized by Gregory Rawlins.

	This is, hopefully, the first of several such issues; which
	is my way of saying that we would like to get abstracts
	from those who have not yet sent them.

	--Alan

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

Date: Tue, 24 Jul 90 09:08:45 EDT
From: booker@AIC.NRL.Navy.Mil
Subject: Workshop abstract


	  The Classifier System Concept Description Language
		      by Lashon B. Booker

	 Legitimate concerns have been raised about the expressive
      adequacy of the classifier language. This paper shows that
      many of those concerns stem from the inadequacies of the binary
      encodings typically used with classifier systems, not the
      classifier language per se. In particular, we describe some
      straightforward binary encodings for attribute-based instance spaces.
      These encodings give classifier systems the ability to represent
      ordinal and nominal attributes as expressively as most symbolic
      machine learning systems, without sacrificing the building blocks
      required by the genetic algorithm.

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

Date:     Wed, 1 Aug 90 19:56:18 EDT
From: Gilbert Syswerda <syswerda@BBN.COM>
Subject:  Workshop Abstract

   A Study of Reproduction in Generational and Steady State Genetic Algorithms

   Two techniques of population control are currently used in the field of
   genetic algorithms: generational and steady state. It has become apparent
   that the two techniques are actually quite different and that steady state
   in general performs much better than generational. In this paper, I study
   the performance of each with regard to reproduction.

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

Date: Mon, 30 Jul 90 07:41:34 EDT
From: ds1@philabs.Philips.Com  (Dave Schaffer)
Subject: workshop abstract

    Spurious Correlations and Premature Convergence in Genetic Algorithms

    J. David Schaffer
    Larry J. Eshelman
    Daniel Offutt

    Philips Laboratories,
    North American Philips Corporation,
    345 Scarborough Road,
    Briarcliff Manor, New York 10510

    ABSTRACT

    What distinguishes genetic algorithms (GAs) from other search
    methods is their inherent exploitive 
    sampling ability known as implicit parallelism.
    We argue, however, that this exploitive behavior makes GAs sensitive to 
    spurious correlations between schemata that contribute 
    to performance and schemata that are parasitic.
    If not combatted, this can lead to premature convergence.
    Among crossover operators, some are more disruptive than
    others, and traditional arguments have held that less
    disruption is better for implicit parallelism.
    To explore this issue we examine the behavior of two crossover operators,
    two-point and uniform crossover, on a
    problem contrived to contain specific 
    spurious correlations.
    The more disruptive operator, uniform crossover, is more
    effective at combatting the spurious correlations at the
    expense of also more disruption of the effective schemata.
    Elitist selection procedures are shown to be able to
    ameliorate this somewhat, suggesting that research into
    ways of dealing with the effects of the inevitable sampling
    errors may lead to generally more robust algorithms.

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

Date: Tue, 7 Aug 90 09:03:12 EDT
From: lje@philabs.Philips.Com  (Larry Eshelman)
Subject: Abstract, GA Workshop

   ABSTRACT

   This paper describes and analyzes CHC, a nontraditional genetic algorithm
   which combines a conservative selection strategy that always preserves
   the best individuals found so far with a radical (highly disruptive)
   recombination operator that produces offspring that are maximally different
   from both parents.  It is argued that the traditional reasons for preferring 
   a recombination operator with a low probability of disrupting schemata do
   not hold when such a conservative selection strategy is used, and that, on
   the contrary, at least some highly disruptive crossover operators provide
   more effective search.  Empirical evidence is provided to support these
   claims.

   --Larry Eshelman

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

Date: Wed, 15 Aug 90 14:45:02 EDT
From: gref@AIC.NRL.Navy.Mil
Subject: FOGA-90 Abstract

	      Weakest Conditions for Implicit Parallelism
			   John Grefenstette

    Many interesting varieties of genetic algorithms have been designed and
    implemented in the last fifteen years.  One way to improve our
    understanding of genetic algorithms is to identify properties that are
    invariant across these seemingly different versions.  This talk focuses
    on invariances among GAs that differ along two dimensions: (1) the way
    user-defined objective function is mapped to a fitness measure, and (2)
    the way the the fitness measure is used to assign offspring to parents.
    A GA is called admissible if it meets what seem to be the weakest
    reasonable requirements along these dimensions.  It is shown that any
    admissible GA exhibits a form of implicit parallelism.

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

Date: Thu, 16 Aug 90 12:38:44 EDT
From: dejong@AIC.NRL.Navy.Mil
Subject: abstract

	An Analysis of Multi-point Crossover

		K. De Jong & Wm. Spears


   At the FOGA Workshop, we presented some theoretical
   results on n-point and uniform crossover. This
   analysis extended the work from De Jong's thesis, which
   dealt with disruption of n-point crossover on 2nd order
   schemata. We have made various extensions to this theory:

	   1) An analysis of the disruption of n-point crossover
	   on kth order schemata.

	   2) The computation of tighter bounds on the disruption
	   caused by n-point crossover, by examining the cases
	   in which both parents are members of the schema (i.e., the
	   values of the defining bits are the same).

	   3) An analysis of the disruption caused by uniform crossover
	   on kth order schemata.

   Disruption of schemata is important for understanding
   the effects of crossover when populations are diverse (typically
   early in the evolutionary process). If a population becomes quite
   homogeneous, another factor becomes important: the probability that
   the offspring produced by crossover will be different than their
   parents in some way (thus generating a new sample). These probability
   computations turn out to be the same as the above disruption computations.
   In other words, those operators that are more disruptive are also
   more likely to create new individuals from parents with nearly
   identical genetic material.

   These theoretical results appear to explain many of the conflicting
   experimental results concerning the advantages/disadvantages of
   1-pt, 2-pt, and uniform crossover and suggest an interplay with population
   size.  With a small population, more disruptive crossover operators such
   as uniform or n-pt ( n>2) yield better results because they help overcome
   the limited carrying capacity of smaller populations and the tendency for
   more homogeneity.  However, with larger populations, less disruptive
   crossover operators (2-pt) work better, as suggested by Holland's original 
   analysis. 

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End of Genetic Algorithms Digest
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