Add to ORKGhttp://deepblue.lib.umich.edu/bitstream/2027.42/3571/5/bab2674.0001.001.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/3571/4/bab2674.0001.001.tx
Many optimization problems have complex search space, which either increase the solving problem time...
Optimizing Boggle boards: An evaluation of parallelizable techniques i This paper’s objective is to ...
Three physical optimization methods are considered in this paper for load balancing parallel computa...
Parallel genetic algorithms are often very different from the "traditional" genetic algori...
http://deepblue.lib.umich.edu/bitstream/2027.42/3761/5/bab3251.0001.001.pdfhttp://deepblue.lib.umich...
A parallel genetic algorithm for optimization is outlined, and its performance on both mathematical ...
http://deepblue.lib.umich.edu/bitstream/2027.42/3572/5/bab2694.0001.001.pdfhttp://deepblue.lib.umich...
Evolutionary algorithms (EAs) are modern techniques for searching complex spaces for on optimum [11]...
Genetic algorithms are search or classification algorithms based on natural models. They present a h...
Most real-life data analysis problems are difficult to solve using exact methods, due to the size of...
153 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1999.Parallel implementations of g...
Genetic algorithms for mathematical function optimization are modeled on search strategies employed ...
Parallel implementations of genetic algorithms (GAs) are common, and, in most cases, they succeed to...
Genetic algorithms, a stochastic evolutionary computing technique, have demonstrated a capacity for ...
The paper introduces an optimized multicore CPU implementation of the genetic algorithm and compares...
Many optimization problems have complex search space, which either increase the solving problem time...
Optimizing Boggle boards: An evaluation of parallelizable techniques i This paper’s objective is to ...
Three physical optimization methods are considered in this paper for load balancing parallel computa...
Parallel genetic algorithms are often very different from the "traditional" genetic algori...
http://deepblue.lib.umich.edu/bitstream/2027.42/3761/5/bab3251.0001.001.pdfhttp://deepblue.lib.umich...
A parallel genetic algorithm for optimization is outlined, and its performance on both mathematical ...
http://deepblue.lib.umich.edu/bitstream/2027.42/3572/5/bab2694.0001.001.pdfhttp://deepblue.lib.umich...
Evolutionary algorithms (EAs) are modern techniques for searching complex spaces for on optimum [11]...
Genetic algorithms are search or classification algorithms based on natural models. They present a h...
Most real-life data analysis problems are difficult to solve using exact methods, due to the size of...
153 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1999.Parallel implementations of g...
Genetic algorithms for mathematical function optimization are modeled on search strategies employed ...
Parallel implementations of genetic algorithms (GAs) are common, and, in most cases, they succeed to...
Genetic algorithms, a stochastic evolutionary computing technique, have demonstrated a capacity for ...
The paper introduces an optimized multicore CPU implementation of the genetic algorithm and compares...
Many optimization problems have complex search space, which either increase the solving problem time...
Optimizing Boggle boards: An evaluation of parallelizable techniques i This paper’s objective is to ...
Three physical optimization methods are considered in this paper for load balancing parallel computa...