Efficient and Accurate Parallel Genetic Algorithms
As genetic algorithms (GAs) become increasingly popular, they are applied to difficult problems that may require considerable computations. In such cases, parallel implementations of GAs become necessary to reach high-quality solutions in reasonable times. But, even though their mechanics are simple, parallel GAs are complex non-linear algorithms that are controlled by many parameters, which are not well understood.
Efficient and Accurate Parallel Genetic Algorithms is about the design of parallel GAs. It presents theoretical developments that improve our understanding of the effect of the algorithm's parameters on its search for quality and efficiency. These developments are used to formulate guidelines on how to choose the parameter values that minimize the execution time while consistently reaching solutions of high quality.
Efficient and Accurate Parallel Genetic Algorithms can be read in several ways, depending on the readers' interests and their previous knowledge about these algorithms. Newcomers to the field will find the background material in each chapter useful to become acquainted with previous work, and to understand the problems that must be faced to design efficient and reliable algorithms. Potential users of parallel GAs that may have doubts about their practicality or reliability may be more confident after reading this book and understanding the algorithms better. Those who are ready to try a parallel GA on their applications may choose to skim through the background material, and use the results directly without following the derivations in detail. These readers will find that using the results can help them to choose the type of parallel GA that best suits their needs, without having to invest the time to implement and test various options. Once that is settled, even the most experienced users dread the long and frustrating experience of configuring their algorithms by trial and error. The guidelines contained herein will shorten dramatically the time spent tweaking the algorithm, although some experimentation may still be needed for fine-tuning.
Efficient and Accurate Parallel Genetic Algorithms is suitable as a secondary text for a graduate level course, and as a reference for researchers and practitioners in industry.
What people are saying - Write a review
We haven't found any reviews in the usual places.
1 AN INTRODUCTION TO GENETIC ALGORITHMS
2 A CLASSIFICATION OF PARALLEL GAs
THE GAMBLERS RUIN PROBLEM AND POPULATION SIZING
2 DECIDING WELL BETWEEN TWO BBs
3 THE GAMBLERS RUIN MODEL
3 ARBITRARY TOPOLOGIES
MIGRATION RATES AND OPTIMAL TOPOLOGIES
1 DEGREE OF CONNECTIVITY
2 MULTIPLE EPOCHS AND CHOOSING A TOPOLOGY
3 PARALLEL DEMES IN THE LONG RUN
4 EXPERIMENTAL VERIFICATION
5 NOISE AND POPULATION SIZING
6 THE EFFECT OF SELECTION PRESSURE
MASTERSLAVE PARALLEL GENETIC ALGORITHMS
2 SYNCHRONOUS MASTERSLAVES
4 ASYNCHRONOUS MASTERSLAVES
5 A DISTRIBUTED PANMICTIC POPULATION
BOUNDING CASES OF GENETIC ALGORITHMS WITH MULTIPLE DEMES
2 PARALLEL SPEEDUPS
3 ISOLATED DEMES
4 FULLYCONNECTED DEMES
MARKOV CHAIN MODELS OF MULTIPLE DEMES
1 FULLYCONNECTED DEMES WITH MAXIMUM MIGRATION RATES
2 ARBITRARY MIGRATION RATES
MIGRATION SELECTION PRESSURE AND SUPERLINEAR SPEEDUPS
1 SELECTION PRESSURE
2 TAKEOVER TIMES
3 SELECTION INTENSITY
5 SUPERLINEAR SPEEDUPS
6 VARIANCE AND THE HIGHER CUMULANTS
FINEGRAINED AND HIERARCHICAL PARALLEL GENETIC ALGORITHMS
1 FINEGRAINED PARALLEL GAs
2 HIERARCHICAL PARALLEL GAs
3 OPTIMAL HIERARCHICAL PARALLEL GAs
4 AN EXAMPLE OF OPTIMAL DESIGN
SUMMARY EXTENSIONS AND CONCLUSIONS
analysis approximated asynchronous average fitness best individuals bi-directional ring bound calculations Cantu-Paz closed-form expressions communications consider converged correctly correct BB crossover deme count deme size demes converge distribution efficiency epochs Equation evaluation evolution strategies evolutionary algorithms Evolutionary Computation example experimental results experiments extended neighborhood Figure fitness function fully-connected demes fully-connected topology gambler's ruin model Goldberg Gorges-Schleuter Harik hypercube implementation increases isolated demes Manderick Markov chains master-slave GAs master-slave parallel migrants replace migration policy migration rate Miihlenbein minimize the execution Morgan Kaufmann multi-deme multiple demes multiple epochs multiple-deme mutation normal distribution number of demes number of epochs number of processors number of slaves optimal number panmictic parallel GAs parallel genetic algorithms parameters partition population sizing probability Proportion BBs random randomly reach receiving deme replace the worst schemata second epoch selection intensity selection methods selection pressure serial shows solution quality solve superlinear speedups takeover tion tournament selection transputers variance Whitley
Page 149 - Sizing populations for serial and parallel genetic algorithms. In Schaffer, JD (Ed.), Proceedings of the Third International Conference on Genetic Algorithms (pp.
Page 145 - A Parallel Genetic Algorithm for Solving the School Timetabling Problem", In Proceedings of the Fifteenth Australian Computer Science Conference (ACSC-15), Volume 14, pp 1-11, 1992.