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What is your knowledge/experience level with respect to genetic algorithms, genetic programming, evolutionary strategies, evolutionary programming, particle swarm optimization, BOA, hBOA, grammatical evolution or any other Evolutionary Algorithm? Responses might be one of expert, knowledgeble, ignorant and one of develop, use, don't use for each of these.

• Knowledgeable on GA's, have had some introduction to genetic and evolutionary programming, not heard of particle swarm optimisation/BOA/hBOA, and I'm not sure what you mean by grammatical evolution - I study the evolution of language but I get the feeling you may be referring to something outwith explaining human syntax here.

• For all of the above the answers are either ignorant or don’t use. As a sociologist I have little knowledge of evolutionary theory or algorithms.

• Ignorant, and as a result don't use, but eager to learn (x 12 votes)

• I am familiar with the general concepts of evolutionary algorithms but ignorant of the specifics: software, programming, and particular applications.

• I am familiar with general concepts - basic definitions and background from population genetics and statistical mechanics. I would be glad to see analytic approaches and insights why does a particular system of rules perform and what are the most viable systems of rules. So, in one word, I am between ignorant and knowledgeable.

• My areas of research are evolutionary computation and artificial life. I have

experience with GA's, GP, and ES's.

• My experience with Evolutionary Algorithms is mostly from lectures, and less from practice. I know more about evolutionary strategies than actual genetic programming.

• one person:
  • genetic algorithms - knowledgeable to expert, develop and use
  • genetic programming - knowledgeable, don't use
  • evolutionary strategies - knowledgeable, use
  • evolutionary programming - knowledgeable, don't use
  • particle swarm optimization - ignorant, don't use
  • BOA, hBOA - heard of it, don't use
  • grammatical evolution - ignorant, don't use

• one person
  • genetic algorithms - knowledgeable, have used
  • genetic programming - knowledgeable, have used
  • evolutionary strategies - somewhat knowledgeable, don't use
  • evolutionary programming, BOA, hBOA, Gr-Ev - ignorant, don't use
  • particle swarm optimization - fairly ignorant, know of

How you have used or anticipate using evolutionary computation in your field of research?

• I've been modeling the evolution of human language for some time now, although not the GENETIC evolution of language, hence I've had no real need to use GA's or crossover/mutation processes in my current work (although I am familiar with work that's gone on down this route within my own field, less so outside it). I would generally like more knowledge on how we can apply EC to socially/culturally transmitted systems, and possibly look at how genetic and cultural systems can coevolve together. Perhaps a tad specialist for a general audience however. I'd be happy with a good refresh/grounding on anything really.

• I am very interested in the conceptualization of social communities as systems that evolve over time. I am especially interested in thinking of ways to utilize evolutionary computation concepts and methods over social networks as a means to understand the processes that underscore the social evolution of groups, cultures and technologies.

• Don't use, but anticipate it would be a useful tool in general (x 3 votes)

• Social Scientist / Philosopher. Hard to say, maybe search for strategies in large games

• I am interested in economics, particularly in developing countries. My hope is to come out of this program with some rudimentary skills for developing models of specific economic problems.

• I think this could be a powerful tool for looking at ecological questions that are of interest to me, especially in making sense of data on multi-scale habitat selection in animals.

• Have not used. I deal with stochastic self-assembly in Biology. I certainly anticipate using the models as toy models of cellular processes. Moreover, evolutionary comp. is an important concept that I would want to master.

• I know only a little bit about EDA's and would love to know more. I am also very interested in learning more about grammatical evolution, particle swarm optimization (this stuff is real cool), and/or ACO.

• I anticipate that I will have use for these computational algorithms in my future research (archeology).

• Did use GAs during my Masters thesis, it was basically based on that and CAs to evolve network topologies with interesting properties (part of the work presented in Budapest last year). Currently planning on using some more GAs and evolutionary strategies to explore healing of RBNs that where some nodes suffered from failure.

• I am interested in agent-based modeling and artificial societies. I am also

interested in exploratory modeling and the search for robust strategies.

• i guess i could use them in my area (political science) in models where agents learn/invent new rules as they cooperate, unless it sounds more like AI than EA.

• I would be interested in using evolutionary techniques to optimise overlay networks to benefit a collective of users, elicit robust behaviour under dynamic/harsh conditions ( i.e. network failures)...

Are there any topics or concepts in evolutionary algorithms that are burning a hole in your head that you'd like me to cover?

• I'm just wondering if any work has gone into modeling cultural evolution distinct from biological evolution.

• I am a novice to the field and would like to cover as much ground as possible with a view to concepts that can be applicable to fields outside evolutionary computation per se.

• no, but I'm hoping for an interesting introduction and to get an idea how it is used.

• Just a general introduction would be great.

• I don't know enough to have specific questions. I'd be especially interested in hearing about any special issues associated with modeling hierarchical processes or understanding processes occurring at multiple scales.

• My background is in ecological and molecular biology with little programming experience (mostly just using numerical solvers in MATLAB). I have a strong background in genetics and evolutionary biology, and I will be doing some genome comparisons and looking for conserved promoter elements. I would like to know how I can implement the most efficient and statistically rigorous algorithms to make cases for homology and divergence between genomic sequence data.

• still unsure - but i am curious!

• I'd really be interested in two topics:
  • what are the latest strategies that have been developed, are there still new GAs that are "discovered"?
  • what are the latest, hot button topics to which GAs are applied, possibly with real life/industrial/business applications and examples.

• All! I'd appreciate any recommendations for small, practical self-teaching

exercises for learning how to do program and employ these methods.

• Realistic applications of EAs whereby they are perhaps used at run-time constantly evolving so as to ensure an adaptive system.

Other Comments

• I think you will find two groups of responses: one from students with programming experience, and another from those without.

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