A connectionist machine for genetic hillclimbing
A connectionist machine for genetic hillclimbing
Distributed evolutionary optimization, in Manifold: Rosenbrock's function case study
Information Sciences: an International Journal - Special issue on frontiers in evolutionary algorithms
A Variant of Evolution Strategies for Vector Optimization
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
jMetal: A Java framework for multi-objective optimization
Advances in Engineering Software
An EMO algorithm using the hypervolume measure as selection criterion
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A fair comparison of modern CPUs and GPUs running the genetic algorithm under the knapsack benchmark
EvoApplications'12 Proceedings of the 2012t European conference on Applications of Evolutionary Computation
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The use of genetic algorithms GAs has grown to widespread acceptance by providing an efficient way to solve complex problems lacking deterministic solvers. GAs employ a special stochastic search method based on evolutionary theory, which gives them the ability to outperform most traditional search algorithms. Also their use of independent individuals makes them an ideal candidate for parallelisation enhancing their inherently good performance even further. Their parallelisability on graphical processing units GPU had been shown multiple times, but the implementations were either single-objective GAs or just partially accelerated by GPUs, also every time they were experimental designs. The genetic algorithm library discussed in this article is the first that contains fully parallelised GPU implementations of multi-objective genetic algorithms besides the single-objective ones. Furthermore, it is organised into a ready to use framework, which provides flexible and efficient GPU accelerated GAs. Thus, enabling the user to solve complex problems faster than standard CPU-based implementations would allow and with lower overall energy cost.