Science | Terra Incognita

Evolving autopilots could boost space slingshots

From the NewScientist article:

COULD space probes use genetic algorithms as autopilots to help them navigate the complexities of the solar system?

Deep-space missions such as NASA’s veteran z Voyager probes often rely on gravity assists. They use a planet’s gravitational field as a slingshot, which allows them to visit other celestial bodies without using up too much fuel. But programming a probe with its trajectory years ahead of time can be a problem, says Ian Carnelli of the European Space Agency in Noordwijk, the Netherlands.

Missed launch windows, unexpected winds and misbehaving rockets mean that probes hardly ever leave Earth in the planned position or velocity, and radiation pressure from solar flares can perturb the craft’s course in deep space. If the probe is out of position when it starts a gravity-assisted manoeuvre, the slingshot will be inefficient.

In the Journal of Guidance, Control and Dynamics (DOI: 10.2514/1.32633), Carnelli and colleagues Bernd Dachwald and Massimiliano Vasile suggest that a probe could navigate for itself using a genetic algorithm (GA).

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Carnelli likens this to hundreds of virtual pilots flying simulated spacecraft, with the GA disposing of those that waste fuel or steer a slow course, while “breeding” the best ones together, a process akin to natural selection. “After hundreds of generations of the GA you obtain a ‘pilot’ that is an extremely good performer – able to fly the assist trajectory that uses the least propellant while reaching the next target planet faster,” he says. Carnelli has run successful simulations of GA-enabled missions to Mercury via Venus, and Pluto via Jupiter.

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Read the full article.

07/05/2009 by Christian S. Perone

News, Science

‘Evolutionary Algorithms’ Mimic Natural Evolution In Silico And Lead To Innovative Solutions For Complex Problems

An interesting news article was recently published by Science Daily, it talks mainly about the use of Evolutionary Algorithms to solve some complex problems like resource management in low rainfall regions, building bricks and automotive electronics:

Extensive resource management is required in low rainfall regions, where groundwater reserves are rare and must be tapped with great care. Various factors must be taken into account: How the ground water interacts with its environment, where drilling must be performed without disadvantaging neighbours, how the ground water can be protected over a long period of time, and how the development costs can be kept as low as possible: This complex application problem was examined by Tobias Siegfried and Wolfgang Kinzelbach, professor at the Institute for Environmental Engineering at the ETH Zurich, with the help of simulated evolution (…)

Perfected tower construction with the help of Evolutionary Algorithms.
(Credit: Johannes Bader /ETH Zürich)

Read the full article.

06/05/2009 by Christian S. Perone

Genetic Algorithms, News, Science

Digital Archeology Reveals Dinosaur Details using Genetic Algorithms

From the article of LiveScience.com:

The pick and shovel can go only so far in digging up details about dinosaurs. Now supercomputers are revealing knowledge about their anatomy otherwise lost to history.

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For example, if the muscles connected to the thigh bone of a Tyrannosaurus rex were short, that would suggest it was angled vertically as in humans. However, if they were very long, it could have been angled horizontally as in birds.

Initial attempts to randomly decipher which pattern of muscle activation works best result almost always in the animal falling on its face, explained computer paleontologist Peter Falkingham at the University of Manchester. But the scientists employ “genetic algorithms,” or computer programs that can alter themselves and evolve, and so run pattern after pattern until they get improvements.

Eventually, they evolve a pattern of muscle activation with a stable gait and the dinosaur can walk, run, chase or graze, Falkingham said. Assuming natural selection evolves the best possible solution as well, the modeled animal should move similar to its now extinct counterpart. Indeed, they have achieved similar top speeds and gaits with computer versions of humans, emus and ostriches as in reality.

Read the full article.

15/04/2009 by Christian S. Perone

Article, News, Philosophy, Science

Evolutionary Computation

There is a portuguese version of this post.

Evolutionary computing (EC) has been widely used in recent years, and every year there are new applications for the techniques developed, despite being a relatively new area, few people are giving attention to that (at least in my vision and I will explain why) probably will have a promising and revolutionary future in relation to how we can generate innovation and even learn from it, especially as it relates to Evolutionary Algorithms (EAs).

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14/04/2009 by Christian S. Perone

Article, News, Philosophy, Science

Computação Evolutiva

A Computação Evolutiva (EC) tem sido sido muito utilizada nos últimos anos, e a cada ano que passa, surgem novas aplicações para as técnicas desenvolvidas; apesar de ser uma área relativamente nova, pouca gente está dando a devida atenção ao que (ao menos na minha visão, e vou explicar porque) provavelmente terá um futuro promissor e revolucionário em relação à como podemos gerar a inovação a até mesmo aprender com ela, principalmente no que se relaciona com Algoritmos Evolutivos (EAs).

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14/04/2009 by Christian S. Perone

Genetic Algorithms, News, Science

Computer Program Self-Discovers Laws of Physics

From the Wired article:

Initially, the equations generated by the program failed to explain the data, but some failures were slightly less wrong than others. Using a genetic algorithm, the program modified the most promising failures, tested them again, chose the best, and repeated the process until a set of equations evolved to describe the systems. Turns out, some of these equations were very familiar: the law of conservation of momentum, and Newton’s second law of motion.

I’m very happy with this news article, I had a similiar idea some time ago, but little different, I’m currently developing it. While in development I always asked myself if it’ll works, but with this good news, maybe it works, but I’ve many work to do yet =)

Read the full article

UPDATE 08/04: you can read the supplemental materials and the report on the Cornell CCSL site.

05/04/2009 by Christian S. Perone

Genetic Algorithms, genetic programming, News, Science

Google Search Insight for GA and GP

I have done a search for the keywords “genetic algorithm” and “genetic programming” on Google Insights for Search and I noticed that India is the region with the most interest on this search terms:

I’ve used the category “Science” to remove some terms of other search categories, but unfortunately this is only an approximation of the real interest, since is obviously that the terms “genetic algorithms” can be searches of algorithms for genetics too.

09/03/2009 by Christian S. Perone

Article, Genetic Algorithms, News, Philosophy, Science

Considerations regarding Karl Popper’s philosophy of science and Evolutionary Algorithms

There is a portuguese version of this post here.

I am a big fan of the Karl Popper’s philosophy of science, so I decided to write something about what I find interesting in his philosophy, especially in relation to rational criticism, to talk later a little bit of what I think about this in relation to Evolutionary Algorithms (EAs).

Popper, in his book “In Search of a Better World” (the original title is “Auf der Suche nach einer besseren Welt“), cites the importance of rational criticism in science and combat the dogmatism of belief in the scientific authority. For me, this idea, despite intrinsic in the thoughts of many philosophers, was not so clearly exposed as Popper did, the clarity with how Popper gives us the insight about how the science grows and improves through rational criticism is remarkable, and I’ll try to summarize here what he tried to explain for almost whole life.

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04/03/200919/01/2020 by Christian S. Perone