Herself’s Artificial Intelligence

Metaheuristic algorithms such as particle swarm optimization, firefly algorithm and harmony search are now becoming powerful methods for solving many tough optimization problems. In this paper, we propose a new metaheuristic method, the Bat Algorithm, based on the echolocation behaviour of bats. We also intend to combine the advantages of existing algorithms into the new bat algorithm. After a detailed formulation and explanation of its implementation, we will then compare the proposed algorithm with other existing algorithms, including genetic algorithms and particle swarm optimization. Simulations show that the proposed algorithm seems much superior to other algorithms, and further studies are also discussed. [download the paper pdf]

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Other swarm papers by Xin-She Yang

So are we ready yet to hand over some of the control of our computers to evolving virtual creatures to do the dirty work for us? What happens when a virtual war breaks out on your home network? Do you get to watch the battles?

If we hand over this control how far are we willing to let virtual creatures evolve? Will they develop personality disorders and call work stoppages?

The future is closer than you think, turn around to look and you may get mowed down.

In the never-ending battle to protect computer networks from intruders, security experts are deploying a new defense modeled after one of nature’s hardiest creatures — the ant.

Unlike traditional security devices, which are static, these “digital ants” wander through computer networks looking for threats, such as “computer worms” — self-replicating programs designed to steal information or facilitate unauthorized use of machines. When a digital ant detects a threat, it doesn’t take long for an army of ants to converge at that location, drawing the attention of human operators who step in to investigate.

The concept, called “swarm intelligence,” promises to transform cyber security because it adapts readily to changing threats.

“In nature, we know that ants defend against threats very successfully,” explains Professor of Computer Science Errin Fulp, an expert in security and computer networks. “They can ramp up their defense rapidly, and then resume routine behavior quickly after an intruder has been stopped. We were trying to achieve that same framework in a computer system.” . . .

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Autonomic Computing freedom or threat?
EW Fulp Publications

I’ve been catching up reading AI papers on my Sony Reader, it’s very nice to have all these books and papers with you all the time, and I ran across a paper on 3d creatures that evolve and battle. The software is open source and there is extensive information explaining the project.

Evolving Virtual Creatures

I evolve virtual creatures. That is, instead of designing the creatures myself, I let Darwinian evolution do the work for me. Starting with a bunch of completely random creatures, I evaluate them, select the “best” (where the definition of “best” depends on what you are trying to obtain: distance covered, outcome of a fight, etc.), allow these “good” creatures to “reproduce” (that is, generate new creatures based on recombination and mutations of these “good” creatures), and start this cycle again with the new population. After a while, increasingly efficient behaviours appear spontaneously.

Both the morphology (body) and behaviour (neural network) of the creatures are fully evolved: I only specify the evaluation function (that is, how to determine the “score” of a creature), and evolution takes care of the design, slowly turning random creatures into highly efficient machines.

This research builds upon the seminal work of Karl Sims; in fact, this was the first successful reimplementation of Karl Sims’ system. I improved the genetic encoding and the developmental system. I also chose to use standard, McCulloch-Pitts neurons instead of the complex functional neurons used by Sims (which makes the problem harder, but more interesting: creatures can’t rely on complex neurons to directly provide useful behaviours, they need come up with their own coordinated neural systems).

I also managed to evolve “fighting” creatures: creatures evolve to smash each other out. I believe this is a first (previous attempts used simplistic forms of “combat”).