7th International Conference on Genetic Algorithms

Workshop Information

For questions concering the Workshop schedule, please contact the Workshop Coordinator David Levine, Workshops, Boeing, levine@redwood.rt.cs.boeing.com

Information on workshops (those available at the moment) are at the end of this document. You can find the appropriate abstract by clicking on the abstract title below.

Sunday 7/20, 7:30PM - 10:30PM

Evolutionary Computation with Variable Size Representation

Genetic Algorithms for Finance
Brian Aldershof, Lafayette College & Graham Capital Management

Monday 7/21, 3:40PM - 5:30PM

Exploring Non-Coding Segments and Genetic-Based Encodings

Test Problem Generators for EAs

Tuesday 7/22, 3:40PM - 5:30PM

Evolutionary Robotics

Workshop on Evolutionary Programming
Peter Angeline, Natural Selection, Inc.

Abstracts of Workshops

Evolutionary Computation with Variable Size Representation

Natural evolution is open-ended with respect to the complexity of created life forms. Evolutionary computation applications that use variable length solutions share this advantage of open ended complexity. They have more expressive power and freedom to solve problems where the structure and size of a satisfactory solution is unknown in advance.

Genetic Programming (GP) is a widely known evolutionary technique that exploits genotype representations of variable size, namely programs. In addition, various versions of Genetic Algorithms (GA), Evolutionary Programming (EP), and Evolution Strategies (ES) also use variable length representations.

The variable size of representations makes analysis and understanding of evolutionary algorithms even more challenging than for the standard versions with fixed length genotypes. However, if variable size representations could be understood with respect to the evolutionary search process, the power of evolutionary algorithms would be greatly extended.

An intuitive advantage of variable length representations in evolutionary algorithms is that a larger, fitter genotype will evolve as a result of improvements being selected, promoted and combined. Ideally, a larger genotype should be due to selection of improvements instead of genetic operations with no effect. However, in practice this is not always the case. For example, in GP the size of evolved structures tends to grow indefinitely and is often not accompanied by improvements in fitness. Instead of the ideal scenario where evolution allows and favors the transmission of chunks of useful genetic information, a complicated process occurs: genetic operations tend to bloat the programs with unexpressed genetic material.

Why don't our algorithms work in the ideal manner? How can the evolution of a more complex solution be guided by modifying either the algorithm or representation? Can innovative practitioners offer specific representational choices that have proven useful when combined with specific algorithms?

More precisely this workshop invites discussions of:

  1. Analysis and understanding of the interplay between variable complexity and evolution.
    1. What is the implicit bias of genetic search with a variable size representation?
    2. How can the bias be explicitized so it can be monitored, adapted or controlled?
    3. Does variable size allow for or favor the transmission of chunks of useful genetic information? If not, how and why?
    4. What is the role of non-coding regions or introns and of neutral variation? Are they necessary?
    5. How can we avoid evolving incomprehensible solutions yet not constrain the power of the algorithm?

  2. Techniques for controlling complexity and evolution.

    Researchers already apply various techniques for controlling size by means of parsimony penalties to fitness. However, the penalty component changes the fitness landscape. Important questions arise about explicit or implicit, predefined or adaptive control of size growth, adaptativity of operators and representations. On this topic we expect contributions from participants who are jointly attending the intron workshop at ICGA on the following day.

    Alternatively, a solution may lie in refining and more explicitly defining the genotype to phenotype mapping. Then the genetic operators which blindly manipulate genotypes may have greater odds of producing fitter, more modular and less bloated solutions.

  3. Innovative applications outlining tradeoffs or issues related to variable size.

    We encourage the highlighting of tricks for controlling evolution in complex application domains. How well do such tricks work? How general are they? Are there theoretical or heuristic explanations why a particular trick works?

Those interested in participating are invited to submit position papers of 1-3 pages in length. The author can include any of:

Please submit your paper electronically to:


Deadline for Submission: JUNE 8, 1997
Notification of Schedule: JULY 4, 1997
Workshop Date: SUNDAY, July 20: 7:30PM - 10:30PM


We plan an active participatory workshop. This will not be a series of short presentations of results with even shorter question periods!

Instead, we will open with a short overview presentation amalgamated from submitted papers that puts forth key issues and approaches to using variable length representations in evolutionary algorithms. This will be followed by a chaired discussion on various topics. The chair will start by soliciting participants whose submission is relevent to the topic and then open the floor to general discussion. When participants leave, our goal is that they have a better understanding of various avenues of approach as relayed by other participants' experience, and ideas for new projects.

Check this URL for updates: www.ai.mit.edu/people/unamay/icga-ws.html

Workshop on Exploring Non-Coding Segments and Genetic-Based Encodings

Researchers in the areas of genetic algorithms (GAs) and genetic programming (GP) have recently begun to examine the utility of non-coding segments in chromosome encodings. Non-coding segments, which have also been called introns in GA/GP literature, refer to regions on a chromosome that do not contribute to the overall fitness of the chromosome. These structures are derived from non-coding DNA which exists in abundance in natural systems. The fact that non-coding DNA has not been selected out in spite of the extra energy required to maintain it suggests that there must be some advantage to having it in the genome. Research suggests that computational non-coding segments may guard against the disruptive effects of crossover, provide natural backups for the coding regions, and possibly expedite the evolution of solutions.

We seek participation from researchers who are interested in the emergence and effects of non-coding segments, the use of non-coding segments in practical systems, and the relationship between biological non-coding DNA and computational non-coding segments.

The workshop will consist of brief presentations of research followed by an open discussion session. Researchers who are interested in participating should send an extended abstract or position paper (please limit to three pages) to one of the workshop organizers by June 9, 1997.

Additional workshop information will be available at: http://www.aic.nrl.navy.mil/~aswu/icga97.ws/

Workshop on Evolutionary Programming

Evolutionary Programming was first defined in 1959 as a method for inducing complex behaviors by evolving finite state machines. Since then, Evolutionary Programming has expanded to include a number of related topics including many of interest in the GA and GP communities. Although EP and other evolutionary computations share many similarities, their differences make each more suited for solving certain problems and manipulating specific representations. The purpose of this workshop is to explore what makes EP distinct as an evolutionary computation and demonstrate how those differences provide advantages when solving certain problems.

In essence, Evolutionary Programming models evolution at a more abstract level than Genetic Algorithms. In particular, Evolutionary Programming models evolution at the level of non-mixing species competing for a common pool of limited resources. Because of the commitment to competing non-mixing species, EP applications rarely if ever use recombination when creating offspring but instead employ a wide variety of representation specific mutations. This approach has been shown to provide significant advantages for a variety of problems. EP has been shown to be effective as a non-linear optimization technique and has a long history of evolving complex behaviors using finite state machines, neural networks and most recently programs represented as parse trees (i.e. crossoverless genetic programming).

This workshop will give an overview of the state-of-the-art in Evolutionary Programming and present a selection of topics currently under investigation by researchers active in the EP field. Attendees are encourgaed to participate through questions and discussions held after each presentation. Please check the URL http://www.natural-selection.com/eps/icga97.html for more details.

Workshop on Test Problem Generators for EAs

In comparing and contrasting the performance of evolutionary algorithms (EAs), there is currently an over-reliance on individual problems and/or rigid test suites of problems. An alternative approach is to create test problem generators in which random problems with certain characteristics can be generated automatically and methodically. Some example characteristics would be multimodality, epistasis, the degree of deception, and problem size. Since problems are randomly created within a certain class, it is often easier to draw general conclusions about the behavior of an EA. Furthermore, the strengths and weaknesses of the algorithms can be tied to specific problem characteristics.

The goal of this workshop is to provide many more test problem generators to the EA community. To accomplish this, we need your help! If you are interested in participating in or contributing to this workshop, please send email either of the workshop organizers.

For more information, see http://www.cs.gmu.edu/~mpotter/icga97-workshop.html.

Workshop on Evolutionary Robotics

Overlapping and complementary approaches to using evolutionary algorithms in the areas of robotics and highly autonomous systems have been seen in recent years. At the workshop, we would like to explore some of these approaches, and hopefully better understand similarities in and differences between these approaches.

We seek participation from researchers working in robotics and evolutionary algorithms (GA, ES, GP, EP, etc). The format of the workshop will be an overview of the field, followed by brief synopses of current research, and then an open discussion session.

Topics of interest include (but are not limited to) the use of evolutionary algorithms in:

Researchers interested in participating should send a description of their research (please limit this to 300 to 500 words) to one of the workshop organizers by 9 June.

The abstracts, and additional workshop information will be available at: http://www.aic.nrl.navy.mil/~schultz/evolv-robots/

Bill Punch, MSU GARAGe (Genetic Algorithms Research and Application Group), punch@cps.msu.edu
Last modified: Thu Jun 26 11:08:48 EDT 1997