Physical Sciences and Mathematics Commons^™

Generalization And Parallelization Of Messy Genetic Algorithms And Communication In Parallel Genetic Algorithms., Laurence D. Merkle

Theses and Dissertations

Genetic algorithms (GA) are highly parallelizable, robust semi- optimization algorithms of polynomial complexity. The most commonly implemented GAs are 'simple' GAs (SGAs). Reproduction, crossover, and mutation operate on solution populations. Deceptive and GA-hard problems are provably difficult for simple GAs. Messy GAs (MGA) are designed to overcome these limitations. The MGA is generalized to solve permutation type optimization problems. Its performance is compared to another MGA's, an SGA's, and a permutation SGA's. Against a fully deceptive problem the generalized MGA (GMGA) consistently performs better than the simple GA. Against an NP-complete permutation problem, the GMGA performs better than the other …