Cantu-Paz E. Efficient and Accurate Parallel Genetic Algorithms

Springer, 2001, -171 pp.Researchers and practitioners alike are increasingly turning to search, optimization, and machine-learning procedures based on natural selection and natural genetics to solve problems across the spectrum of human endeavor. These genetic algorithms and techniques of evolutionary computation are solving problems and inventing new hardware and software that rival human designs. Genetic Algorithms and Evolutionary Computation publishes research monographs, edited collections, and graduate-level texts in this rapidly growing field. Primary areas of coverage include the theory, implementation, and application of genetic algorithms (GAs), evolution strategies (ESs), evolutionary programming (EP), learning classifier systems (LCSs) and other variants of genetic and evolutionary computation (GEC). The series also publishes texts in related fields such as artificial life, adaptive behavior, artificial immune systems, agent-based systems, neural computing, fuzzy systems, and quantum computing as long as GEC techniques are part of or inspiration for the system being described.
Erick Cantu-Paz's monograph is the first book in this series, and in many ways it is an exemplar of the kind of book we hope to attract. The book is an elaboration of Erick's groundbreaking dissertation on the design of efficient parallel genetic algorithms. It combines facetwise and exact design theory, careful bounding empirical investigation, and a keen eye toward practice in a text that has been written in an accessible, logical, and thorough manner. In the end, it gives us critical scaling laws that accurately govern the particular parallel architectures that have been analyzed and that approximately bound many architectures that have not. I believe it is fair to say that prior to this work, the design of parallel GAs was something of an empirical black art, guided largely by ad hoc trial and error experimentation. Following this work, the solution quality, duration, connectivity, deme size, deme count, and migration rates succumb to the magic of Erick's careful analysis, experimentation, and exposition. This is not to say that parallel GAs are now completely understood. The very nature of complex systems design almost ensures that we can never get to the bottom of the subject. On the other hand, our understanding of parallel GAs is much enhanced by this work, and I urgently recommend that all readers interested in parallel genetic and evolutionary algorithms study this important book carefully and soon.The Gambler's Ruin and Population Sizing
Master-Slave Parallel GAs
Bounding Cases of GAs with Multiple Demes
Markov Chain Models of Multiple Demes
Migration Rates and Optimal Topologies
Migration and Selection Pressure
Fine-Grained and Hierarchical Parallel GAs
Summary, Extensions, and Conclusions