Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Computational Optimization and Applications
Scalable parallel Benders decomposition for stochastic linear programming
Parallel Computing
Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue on uniform random number generation
A case study in the performance and scalability of optimization algorithms
ACM Transactions on Mathematical Software (TOMS)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
From Recombination of Genes to the Estimation of Distributions I. Binary Parameters
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
A matrix approach for finding extrema: problems with modularity, hierarchy, and overlap
A matrix approach for finding extrema: problems with modularity, hierarchy, and overlap
The gambler's ruin problem, genetic algorithms, and the sizing of populations
Evolutionary Computation
Direct solution of linear systems of size 109 arising in optimization with interior point methods
PPAM'05 Proceedings of the 6th international conference on Parallel Processing and Applied Mathematics
Improving the efficiency of the extended compact genetic algorithm
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Pattern identification in pareto-set approximations
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Speeding online synthesis via enforced selecto-recombination
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Enhancing the Efficiency of the ECGA
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Large-Scale Optimization of Non-separable Building-Block Problems
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Correlation guided model building
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Quantum-inspired evolutionary algorithm: a multimodel EDA
IEEE Transactions on Evolutionary Computation - Special issue on evolutionary algorithms based on probabilistic models
Parallel probabilistic model-building genetic algorithms with elitism
ISCIT'09 Proceedings of the 9th international conference on Communications and information technologies
Network crossover performance on NK landscapes and deceptive problems
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Shared memory genetic algorithms in a multi-agent context
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Graph clustering based model building
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Hierarchical allelic pairwise independent functions
Proceedings of the 13th annual conference on Genetic and evolutionary computation
A memory efficient and continuous-valued compact EDA for large scale problems
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Information Sciences: an International Journal
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This paper presents a highly efficient, fully parallelized implementation of the compact genetic algorithm (cGA) to solve very large scale problems with millions to billions of variables. The paper presents principled results demonstrating the scalable solution of a difficult test function on instances over a billion variables using a parallel implementation of cGA. The problem addressed is a noisy, blind problem over a vector of binary decision variables. Noise is added equaling up to a tenth of the deterministic objective function variance of the problem, thereby making it difficult for simple hillclimbers to find the optimal solution. The compact GA, on the other hand, is able to find the optimum in the presence of noise quickly, reliably, and accurately, and the solution scalability follows known convergence theories. These results on noisy problem together with other results on problems involving varying modularity, hierarchy, and overlap foreshadow routine solution of billion-variable problems across the landscape of search problems.