Tag Archives: evolution
Darkly Wise and Rudely Great
Imagine you are a great historical scientist and discover a disturbing truth about nature. Perhaps you discover that the world is not flat like a map, but in fact round like a tomato. Or, maybe you discover that instead of the Earth having a special place at the center of the universe, it is a just an ordinary rock floating in space – distasteful and troubling revelations.
The naturalist, Charles Darwin, was in such a situation in 1859. Although a devout Christian, Darwin’s pursuits led him to discover the mechanism by which life on Earth developed from a common ancestor, leading to the obvious conclusion that humans evolved from their closest relatives, the great apes. It was the most disgusting idea of the century, and he was personally troubled by it for the rest of his life.
The path Darwin’s life took made him a likely candidate to discover the mechanism of evolution. He had an obsessive-compulsive personality, avidly collecting and cataloging beetles and other insects around his home. And, his own grandfather, Erasmus Darwin, had already put forth the idea of common ancestry in a poem 60 years earlier:
“The Temple of Nature”
Organic life beneath the shoreless waves
was born and nors’d in ocean’s pearly caves;
First forms minuk, unsceen by spheric glass,
move on the mud, or pierce the watery mass.
These, as successive generations bloom
New powers arquire and larger limbs assume
whence countless groups of vegatation spring.
And breathing realms of fin and feet and wing.-Erasmus Darwin 1802
Most auspiciously, his family was affluent. His father, frustrated with Charles’ unending curiosity and lack of direction, eventually agreed to let him travel abroad with the HMS Beagle even though he considered it to be a complete waste of time. Darwin’s writings of the journey solidified his position as a well respected and well connected member of the scientific community.
He became close friends with Charles Lyell and John Dalton Hooker, the most influential geologist and botanist of his time respectively. Through Lyell, he came to see the natural world as a gradually changing process. Hooker was a trusted confidante with whom he could share some of his troubling revelations. After writing his first essay on natural selection, Darwin told Hooker it was like “confessing a murder”. Hooker gave him the calm, critical feedback Darwin needed to proceed.
The final push came from the naturalist Alfred Russel Wallace who wrote his own version of natural selection in 1858, ahead of Darwin. Both Darwin and Wallace had been inspired by the ideas of the Reverend Thomas Malthus who promoted the idea that population outstrips food supply, and both came to similar conclusions about how this creates selection pressure in nature. Hooker and Lyell arranged to have some of Darwin and Wallace’s ideas presented at a conference in 1858. This inspired Darwin to kick into high gear and finish “The Origin of Species” within a year, unleashing the most consistent and comprehensive explanation of natural diversity ever conceived, then and since.
Darwin did not doubt the literal translation of the bible as a young man, but bravely came to question the nature of the Christian God after observing nature in detail. His attitude was well summarized by his observations of the Ichneumonidae wasp. He wrote:
“I cannot persuade myself that a beneficent and omnipotent God would have designedly created the Ichneumonidae with the express intention of their feeding within the living bodies of Caterpillars, or that a cat should play with mice.”
-Charles Darwin 1860
Like Copernicus’ realization 350 years earlier that the Earth was not at the center of the universe, Darwin’s theory of natural selection pressed heavily against the religious views of his time. Though the religious conflict was never fully resolved in his own mind, Darwin lived to witness his theory gain acceptance in the scientific world and among much of the general public before his death in 1882.
Darwin might have found some solace in the poet Alexander Pope who foresaw the impingement of science upon religion. In “An Essay on Man”, Pope describes Man as passionate and ignorant, existing between God and beast, and that science is a valuable tool for understanding our nature, but not useful in answering religious questions:
Know then thyself, presume not God to scan,
The proper study of mankind is Man.
Placed on this isthmus of a middle state,
A being darkly wise and rudely great:
With too much knowledge for the Sceptic side,
With too much weakness for the Stoic’s pride,
He hangs between, in doubt to act or rest;
In doubt to deem himself a God or Beast;
In doubt his mind or body to prefer;
Born but to die, and reas’ning but to err;
Alike in ignorance, his reason such,
Whether he thinks too little or too much;
Chaos of thought and passion, all confused;
Still by himself abused or disabused;
Created half to rise, and half to fall:
Great lord of all things, yet a prey to all;
Sole judge of truth, in endless error hurl’d;
The glory, jest, and riddle of the world.- Alexander Pope 1734
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Avida
Self-replicating programs have been around for a while. The idea was first conceived by John Von Neumann in 1948 as a thought experiment. The first real instance was a game called Core War developed in the early 80s where programmers would write code to compete for sections of the computer’s memory. The best strategies were those that copied themselves, but these programs were fragile – change one piece of code and they would would cease to function.
Tom Ray’s Tierra in 1991 added some stability to the concept of mutation in a digital environment and showed that evolutionary processes previously only seen in nature can take place in the digital world. This eventually led to the “killer app” of self-replicating program research environments, “Avida”, developed by Charles Ofria at Michigan State University’s Digital Evolution Lab in 2000. Below is an animation showing successive generations of Avida organisms (Avidians) taking over the population as a sample evolution progresses:
At the 2010 Artificial Life XII conference in Odense, Denmark, the MSU team presented a paper describing some of their most recent work, including digital creatures that evolved the ability to follow paths along a grid. The environment was strewn with clues, signposts indicating which way the path would go. After tens of thousands of generations of evolution, the Avidians evolved reflex actions which successfully interpreted the signs as either “turn left” or “turn right”, giving them a survival advantage.
A program with just reflex actions can do quite a lot in a complex environment. What about using “volatile memory”, not just knee-jerk responses to the environment, but ones that depend on context? To encourage the evolution of volatile memory, the researchers put sign posts on the grid that symbolized “repeat last action”. The MSU team showed the Avidians were indeed able to take advantage of this information by evolving the ability to remember their last action.
The ability to remember and recall a single variable in the environment appears trivial, especially for a computer program, but the significance of this research is that no one programmed this behavior. The code which navigates the path and uses volatile memory to its advantage bubbled up from the raw evolutionary stew acting against a carefully crafted artificial environment that allowed such evolutionary pressure to exist.
There are many interesting questions here: Under what circumstances does evolutionary pressure tend to favor the development of micro brain structures? How can we configure artificial environments to evolve more complex bottom-up brains? How does the evolution of such structures manifest in nature?
Avida is a robust open-ended research tool and it is likely we will see many more groundbreaking projects coming from this platform for some time.
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Tierra
Tom Ray was a tropical biologist who conducted research in the Costa Rican rain forest from 1974 to 1989. His research focused on the ecologies and evolution of various species living there. Eventually he realized he there was a problem with studying evolution in the wild: it occurs far too slowly to actually observe it. He decided therefore to study evolution in a much faster medium, the digital computer. In 1991, he joined forces with the Santa Fe Institute in New Mexico to develop an evolutionary software platform called Tierra.
Genetic Algorithms, programs that simulate evolution to solve a specific problem, had already been well established, but Tierra was different. It wasn’t optimizing anything in particular. Small chunks of machine code were simply left on their own to replicate and compete for access to the CPU, and that was all. Occasional mutations in the copying process allowed evolution to take place. But, this wasn’t a simulation of evolution, these entities were actually evolving. What emerged from Tierra surprised Tom and most of the Santa Fe research team.
The first thing Tom noticed was that these replicating programs became smaller and smaller. A smaller program could replicate faster and so had an advantage over others. Some became so small that they evolved into parasites, tricking other programs into doing the copying for them. The hosts then evolved mechanisms to resist parasites. Some of the host programs were even able to trick the parasites into helping them. Eventually, a form of cooperation emerged where programs helped each other replicate. Then, free-riders emerged who took advantage of this group trust. All of this robust behavior, previously only observed in nature, emerged from Tierra automatically.
Tierra was groundbreaking for the field of Artificial Life, and inspired many systems like it afterwards, including the very robust evolutionary platform, Avida. Most importantly, it gave a demonstration of real evolution occuring in a medium other than nature.
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Resilient Robots
Using artificial evolution to design robots is a powerful technique. The difficult part is writing a simulation of the robot and its physical environment. A simulation is required so tens of thousands of design variations can be automatically tested as the evolution progresses.
What if you didn’t need to write a simulation? Imagine the robot could figure out how to simulate itself. Not only have you saved yourself a lot of programming, but the robot is now very resilient. You could snap off one of its legs, for example, and it would be able to re-simulate itself, and re-evolve a walking strategy.
Hod Lipson from Cornell University’s Computational Synthesis Lab has built such a robotic system. His robot first squirms around on the floor, testing theories about how its own motors work and what its own shape is. This simulation of itself is then used to evolve a walking strategy.
Such a system would be very useful for planetary exploration, where robot engineers may not be readily available to fix problems. If the machine breaks a leg, no problem. Re-simulate, re-evolve, and move on.
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Evolution: Creativity Engine
Evolution is an inherently creative process. Left to its own devices, it will automatically generate complex and beautiful forms.
Only three ingredients are required for a runaway evolutionary process to take hold: replication, variation, and selection. In nature, DNA is the replicator. It is able to make high-fidelity copies of itself under the right conditions. Variation comes from occasional errors during the copying process, and selection comes from the environment as some life is better suited to live and reproduce under the extant conditions of the planet than others.
Interestingly, these three ingredients are relatively easy to program into a computer. Replication is something computers do inherently. Copying data from one place to another is their most basic function. Variation is achieved by artificially adding some randomness to the copying process, again trivial for a computer. Selection requires a little more work. The programmer must decide what makes some entities more “fit” than others which usually requires a simulation of some kind. The fitter entities are then given more of a chance to copy themselves.
My masters thesis at the University of Sussex in 2005 was to design running, springy robots in simulation (called Metapets). The design task itself was too difficult to solve on my own due to the complex interaction of the springs and coordination of the limbs although I tried. Eventually, I appealed to evolution to work out the details. The fitness of each robot was determined by how far it moved forward, thus selecting robots for speed.
At the start of the evolutionary process, most robots would just fall down and go into convulsions. Others walked backwards, went in circles, or just stood still. But, after letting evolution run for several weeks, more functional designs gradually emerged, and the longer I ran the simulation, the better the designs became.
Many of the designs that evolved were quirky looking, while others dragged their legs, walked on their elbows, or moved in ways that one would not consider intuitive. Therein lies artificial evolution’s primary caveat: it may provide a means of automatically solving a specific problem in a creative way, but not necessarily the way you intended or expected. When this happens, the evolutionary pressure can be refined by adjusting the “fitness function” which determines each model’s fitness score in the selection step.
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Walking Tall
The field of Robotics constantly looks to Biology for inspiration, but it’s rare when the reverse happens. Such was the case with the fossil named “Lucy”, an ancestor of homo sapiens who lived over 3.2 million years ago and was discovered in 1974 in Ethiopia. Lucy was somewhere between ape and human, but with a large protruding jaw and a brain capacity 1/4th that of humans, she seemed more ape-like. Thus, some scientists concluded that she didn’t walk upright, and could probably only walk on two legs for brief distances, shuffling awkwardly like a chimpanzee. Others believed she walked like modern day humans. From the bone proportions alone it was difficult to tell. When footprints from the same species were uncovered in 1980, more evidence was brought to the debate. But how can one determine walking posture from just bone proportions and footprint patterns?
Robotics came to the rescue much later in 2005. The field of Evolutionary Robotics uses simulated evolution to automate the design of efficient robot bodies. Using this technique, scientists plugged in the constraints of the bone proportions and the footprint patterns, and let the simulation find the most efficient gait. Behold! Lucy walked upright, much like modern day human beings. Still, much is not known about Lucy and her kind. Their social characteristics and whether they were able to make tools remain a mystery.
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Beach Beasts
About twenty years ago, Theo Jansen had a vision — he imagined large herds of mechanical beasts wandering the beach, powered only by the wind. It sounds like the work of science fiction, but Jansen, a Dutch physicist turned artist, managed to build these creatures and bring them to life. The beach beasts (or “strandbeest”) are first evolved on a computer using a simulation of Darwinian evolution. His survival of the fittest simulation produces complex mechanical forms which are able to walk, some with dozens of legs, using flapping wings to gather and store wind power. Jansen then takes the most highly evolved models and, following the computer’s blueprint, builds the strandbeest in the real-world using plastic tubes. With evolution as the driving creative force, the results are striking.
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Evolved Virtual Creatures
The Connection Machine was a supercomputer designed by Daniel Hillis who came out of the MIT Artificial Intelligence lab in the early 80′s and started Thinking Machines Corp in Cambridge, Massachusetts. The Connection Machine used a unique parallel computer design with over 64,000 small processors all linked together, following a more brain-like architecture (plus it had lots of cool blinking red lights on the front). The ingenuity of the computer design attracted the attention of DARPA from which Thinking Machines received significant funding.
Karl Sims was an artistic consultant whose job was to come up with ways to show off the Connection Machine. One of his projects was a simulation of Darwinian evolution that produced designs of “virtual creatures” in a 3D environment. The simulation required a massive amount of computational power, and the lifelike forms that emerged made for a compelling demonstration of Thinking Machine’s latest model, the CM-5.
Despite the inherent awesomeness of the CM-5, no market could be found for the computers and with increasing competition from more established manufacturers, Thinking Machines filed for Chapter 11 bankruptcy in August 1994. Later, Sun Microsystems and Oracle bought up the remains. And, while the Connection Machine has since been relegated to the dark recesses of the National Cryptologic Museum, Karl Sims’ experiment still represents one of the best examples of the creative power of artificial evolution to date.
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