shared memory genetic algorithms in a multi-agent context
In this paper we present a concurrent implementation of genetic algorithms designed for shared memory architectures, intended to take advantage of multi-core processor platforms. Our algorithm divides the problems into sub-problems along the chromosome as opposed to the usual approach of dividing the population into niches. We show tests for timing and performance on a variety of platforms
shared memory multi-agents parallel genetic algorithms