Herself’s Artificial Intelligence

So many claims, it’s difficult to sort the intelligent from the educated. But at some point one or more of these claims will be true.

FOR generations, the Avidians have been cloning themselves quietly in a box. They’re not perfect, but most of their mutations go unnoticed. Then something remarkable happens. One steps forward, and that changes everything. Tens of thousands of generations down the line, some of its descendents will evolve memory.

Avidians are not microbes, or sci-fi alien life forms. They are the digital offspring of Charles Ofria and colleagues at Michigan State University (MSU) in East Lansing. They “live” in a computer world called Avida, and replicate using strings of coded computer instructions instead of DNA. But in many ways they are similar to real life: they compete with each other for resources, replicate, mutate, and evolve. They – or things like them – might eventually evolve to become artificially intelligent life forms. read more Artificial life forms evolve basic intelligence

More information:
HyperNEAT
Avida-ED
Avida-ED user manual ( pdf )
Observing mutations on the genomes of evolving Avidians (pdf)
Harnessing Digital Evolution ( $$$ )
Digital Evolution with Avidian

Cuckoos have an aggressive reproduction strategy that involves the female laying her fertilised eggs in the nest of another species so that the surrogate parents unwittingly raise her brood. Sometimes the cuckoo’s egg in the nest is discovered and the surrogate parents throw it out or abandon the nest and start their own brood elsewhere.

The team base their design search on three simple principles that emerge from the cuckoo’s strategy:

* First, each cuckoo lays one egg (a design solution) at a time, and dumps it in a randomly chosen nest.
* Second, the best nests with a high quality egg (better solution) carry over to the next generation.
* Third, the number of available host nests is fixed, and a host and there is a finite probability of the cuckoo in the nest being discovered.

The team have encapsulated these three principles in a mathematical formula that they then converted to computer software code. The various design parameters and constraints are fed to the software, which tests each “egg” discarding some based on lack of fitness and sending the successful solutions through a second round and so on until an optimal solution emerges.

The team has carried out standard mathematical design tests on their cuckoo search, which itself has now been optimised and also compared it with particle swarm optimisation and other techniques to show that it is more efficient than these other approaches to engineering design of a welded beam and a spring, two key engineering components of many structures. Read more

More information:
Engineering Optimisation by Cuckoo Search ( arXiv paper )
Other papers by Xin-She Yang

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]

More information
Other swarm papers by Xin-She Yang