Pre-Grant Publication Number: 20080016013
Filing Date: July 12, 2006
Inventors: Rajesh Venkat Subbu, Stefano Romoli Bonissone
Assignee: General Electric Company
Current U.S. Classification: 706, 706/013000
View Prior Art for Claim 00001
A system for implementing a multi objective evolutionary algorithm (MOEA) on a programmable hardware device, the system comprising:
a random number generator, configured to generate a sequence of pseudo random numbers;
a population generator; configured to generate a population of solutions based on the output from the random number generator;
a crossover/mutation module, configured to adapt the population of solutions to generate an adapted population of solutions;
a fitness evaluator configured to evaluate each member comprising the population of solutions and the adapted population of solutions, wherein the fitness evaluator is implemented on the programmable hardware device;
a dominance filter configured to select a subset of members from the population of solutions and the adapted population of solutions and generate a filtered population of solutions; and
an archive configured to store populations of solutions.
Title Computational Intelligence and Multimedia Applications, 1999. ICCIMA apos;99. Proceedings. Third Int
ISBN
Description
In claim 1, the inventors claim a particular decomposition of a MOEA that mirrors the generally understood structure of the algorithm. Object-oriented software systems typically use the same or very similar decompositions. The decomposition described in this paper is identical except that it groups the fitness evaluator and the dominance filter into one component (see figure 1). The random number generator is included by not illustrated. The paper also allows archiving of fitness evaluations according to user specifications.
Title Genetic Programming and Evolvable Machines, Volume 2
ISBN
Description
This paper is one of many (do a Google on "fpga fitness function" for more) that describes the use of a programmable hardware device (fpga) for fitness evaluation in a genetic algorithm.
