Name
Abstract | ||
|---|---|---|
Significant emphasis has been placed on the use of genetic algorithms for evolving solutions to otherwise intractable problems but they also offer an opportunity to explore the process of evolution itself. Brains represent complex circuits that have arisen in an astronomically large search space of possibilities to solve a variety of problem types, most of them quite complex. To expedite the investigation of the evolution of neural circuitry, we introduce a system that makes it possible to (1) explore the constraints under which various brain functions might have evolved, (2) demonstrate (as a proof of concept) that such evolution is possible, and (3) that lays the foundation for tracking paths from simpler to more complex architectures. The system we present uses a highly flexible genome encoding scheme in conjunction with a sophisticated genetic algorithm (employing multiple operators) and a flexible neural network simulation program. We show preliminary results for a basic neural circuit and discuss implications for subsequent work. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1145/2184512.2184567 | ACM Southeast Regional Conference 2005 |
Keywords | Field | DocType |
complex circuit,genetic algorithm,astronomically large search space,flexible genome,complex architecture,neural circuitry,flexible neural network simulation,neural architecture,sophisticated genetic algorithm,basic neural circuit,intractable problem,simulation,evolution,multiplication operator,neural network,search space,proof of concept | Computer science,Neural Network Simulation,Theoretical computer science,Proof of concept,Operator (computer programming),Artificial neural network,Biological neural network,Genetic algorithm,Encoding (memory) | Conference |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Steve Donaldson | 1 | 3 | 1.84 |
| Chris Walling | 2 | 0 | 0.68 |