Scalability of an MPI-based fast messy genetic algorithm

The fast messy genetic algorithm (fmGA) belongs to a class of algorithms inspired by the principles of evolution, known appropriately as "evolutionary algorithms" (EAs). These techniques operate by applying biologically-inspired operators, such as recombination, mutation, and selection, to a population of individuals. EAs are frequently applied as optimum seeking techniques, by way of analogy to the principle of "survival of the fittest." In contrast to many EAs, the fmGA consists of several evolutionary phases, each with distinct characteristics of local/global computation. These are explained in the paper. 1998 ACM 0-89791-969-6/98/0002 Previous scalability analyses of island-model EAs have been based on either fixed global population size or fixed subpopulation size. Recently developed population sizing theory enables scalability analysis based on fixed expected solution quality. Parallel computational experiments are performed to determine the effectiveness and...

• Type: Conference
• Year: 1998
• Where: SAC
• Authors: Laurence D. Merkle, George H. Gates Jr., Gary B. Lamont
• Keywords: Applied Computing, Island-model Eas, Messy Genetic Algorithm, Optimum Seeking Techniques, SAC 1998