Parallel Genetic Algorithm to Solve the Satisfiability Problem

This Paper Offers a Parallel Genetic Algorithm Solution to the Satisfiability Problem. It Combines Components of the Davis-Putnam Method and Genetic Algorithms for the Solution. This Solution is Useful in the Areas of Theorem Proving, Constraint Satisfaction Programming, and VLSI Design. the Algorithm is Implemented and Run on a Paragon. the Results Show Performance Improvement by Increasing the Number of Nodes. Two Parallel Methods Are Compared: One that Implements Interprocessor Communication and One that Does Not. the Results Show Performance Improvement with the Method that Uses Interprocessor Communication

Pipelined genetic propagation

© 2015 IEEE.Genetic Algorithms (GAs) are a class of numerical and combinatorial optimisers which are especially useful for solving complex non-linear and non-convex problems. However, the required execution time often limits their application to small-scale or latency-insensitive problems, so techniques to increase the computational efficiency of GAs are needed. FPGA-based acceleration has significant potential for speeding up genetic algorithms, but existing FPGA GAs are limited by the generational approaches inherited from software GAs. Many parts of the generational approach do not map well to hardware, such as the large shared population memory and intrinsic loop-carried dependency. To address this problem, this paper proposes a new hardware-oriented approach to GAs, called Pipelined Genetic Propagation (PGP), which is intrinsically distributed and pipelined. PGP represents a GA solver as a graph of loosely coupled genetic operators, which allows the solution to be scaled to the available resources, and also to dynamically change topology at run-time to explore different solution strategies. Experiments show that pipelined genetic propagation is effective in solving seven different applications. Our PGP design is 5 times faster than a recent FPGA-based GA system, and 90 times faster than a CPU-based GA system