Old School Genetic Algorithms News On Google News Archive

Interesting feature this one from Google News. You can browse old newspapers and read old news.

Looking for genetic algorithms, Google News Archive gives you a nice amount of news dating back to 1980s.

For example, see this simulated annealing news on the excellent magazine Dr. Dobbs -- genetic algorithms and other optimization approaches are cited and/or briefly explained. It's from 1989.

This other one is a brief summary of students and researchers work on engineering mentioning genetic algorithms and the early work of David E. Goldberg during his good times at The Clearing House For Genetic Algorithms. The excerpt:

James Gleick wrote an article on artificial life during its early steps, citing genetic algorithms along the way.

And this another one suggests someone was, as early as 1983, already using genetic algorithms to do stock trading or something related.

An Old School Genetic Algorithm News

Interesting finding this one! Just click on the picture above and read the news! It's from 1986 and published on Tuscaloosa News. I think it summarizes some aspects of the good times at The Clearing House for Genetic Algorithms. Very old school, indeed.

Evolving Missile Trajectory Through Simulated Evolution

I have found an old school evolution strategy article, from 1980: Optimization of missile trajectories by means of evolution strategy. You can read the first page here.

From the abstract:

Evolution Strategy is an optimum seeking method which attempts to apply the rules of biological evolution - mutation and selection - as closely as possible to technical optimization problems.
In the paper results are presented showing the successful application of evolution strategy to missile trajectory optimization problems like

- Range optimization of a ballistic rocket and e boost-glide missile

- Optimization of miss distance due to lateral wind of a Short range antitank missile

- Optimal trajectory shaping A comparison with other optimization methods is given to show the efficiency of evolution strategy.

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From the introduction we can understand a little of the academic Zeitgeist along that period, see below:

"The paper deals with solutions of optimization problems which we have gained by the application of evolution strategy to missile engineering and development. Evolution strategy is an imitation of nature's way of optimization, a game of mutation and selection. Since nature has been man's teacher in many ways it might appear quite obvious to apply the laws of biological evolution to problems of engineering and technology.

On the Other hand there are also objections to that method, the most important ones being that "cut and try" is a sign of poor engineer and craftsmanship and that we cannot afford nature's lavishness in means and in time to wait for the result. Based on a number of number of encouraging results [1], [2] we have tried to gather our own experience with the matter and will present the results we have achieved.

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It seems that the author worked to the famous German aircraft manufacturer Messerschmitt, which gave birth to the world's first operational turbojet fighter aircraft, the so-called Messerschmitt Me 262.

Below the old Messerschmitt logo.

1984

No, this it not an allusion to the famous Orwellian work, but, let's say, the first time that someone posted on Usenet (now Google Groups) something that deals with genetic algorithms, see it here.

It is an announcement about the 1984 AAAI Conference, held in Texas, USA. Guess what? Who did speech on genetic algorithms? Let me give you a tip: JH... That's it! John Holland!

There were, I suppose, just two genetic algorithm presentations:

Michael L. Mauldin, Carnegie-Mellon University: Maintaining Diversity in Genetic Search

ABSTRACT
Genetic adaptive algorithms provide an efficient way to search large function spaces, and are increasingly being used in learning systems. One problem plaguing genetic learning algorithms is premature convergence, or convergence of the pool of active structures to a sub-optimal point in the space being searched. An improvement to the standard genetic adaptive algorithm is presented which guarantees diversity of the gene pool throughout the search. Maintaining genetic diversity is shown to improve off-line (or best) performance of these algorithms at the expense of poorer on-line (or average) performance, and to retard or prevent premature convergence..

And a machine learning tutorial session in which John Holland was involved in, see below:

9:00 a.m. to 10:30 a.m.

TUTORIAL AND PANEL: PARADIGMS FOR MACHINE LEARNING
Concert Hall in the Performing Arts

The session will be divided into two parts:

Center

Part 1. Tutorial.
A single presentation defining and outlining each
major approachto Machine Learning, and contrasting them with each other on the basis of objectives, techniques, limitations, and applications.

The role of the tutorial is to:

- Introduce each paradigm and the contrastive dimensions listed above.
- Present some meaningful comparative analysis.
- Raise potentially controversial issues to be addressed in the ensuing panel discussion.

Tutorial presenter: Jaime Carbonell

Part 2. Panel discussion.

Each Machine Learning paradigm will be represented by a panelist advocating that particular approach. The panelists are active researchers with considerable experience in ML in general and their approach in particular.

Discussion Leader: Patrick Winston

Panelists:

Tom Mitchell, Rutgers University (Analytical Generalization)
Ryzsard Michalski, University of Illinois (Empirical Induction)
John Holland (Genetic Algorithms)
Doug Lenat, Stanford University (Discovery Systems)
Jaime Carbonell, Carnegie-Mellon University (Learning by Analogy)

The panel discussion will center on addressing specific issues raised in the preceding tutorial (the panelists will be informed ahead of time of these issues). We are explicitly disallowing prepared statements by the panel -- we hope to have a real discussion focused around a few burning issues.

The picture above is Usenet circa 1985/1986.