ACORN—A new method for generating sequences of uniformly distributed Pseudo-random numbers
Journal of Computational Physics
Global optimization and simulated annealing
Mathematical Programming: Series A and B
Random number generation and quasi-Monte Carlo methods
Random number generation and quasi-Monte Carlo methods
Recent advances in global optimization
Recent advances in global optimization
Implementation and tests of low-discrepancy sequences
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Quasi-random sequences and their discrepancies
SIAM Journal on Scientific Computing
On the crude multidimensional search
Journal of Computational and Applied Mathematics
Algorithm 659: Implementing Sobol's quasirandom sequence generator
ACM Transactions on Mathematical Software (TOMS)
Distribution properties of multiply-with-carry random number generators
Mathematics of Computation
Accuracy estimation for quasi-Monte Carlo simulations
Mathematics and Computers in Simulation
On selection criteria for lattice rules and other quasi-Monte Carlo point sets
Mathematics and Computers in Simulation - IMACS sponsored Special issue on the second IMACS seminar on Monte Carlo methods
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A novel metaheuristics approach for continuous global optimization
Journal of Global Optimization
Quasi-random initial population for genetic algorithms
Computers & Mathematics with Applications
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
A variance reducing multiplier for Monte Carlo integrations
Mathematical and Computer Modelling: An International Journal
Path planning on a cuboid using genetic algorithms
Information Sciences: an International Journal
Is "best-so-far" a good algorithmic performance metric?
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Center-based initialization for large-scale black-box problems
AIKED'09 Proceedings of the 8th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases
Toward effective initialization for large-scale search spaces
WSEAS TRANSACTIONS on SYSTEMS
Optimal contraction theorem for exploration-exploitation tradeoff in search and optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Center-based sampling for population-based algorithms
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Journal of Global Optimization
A two-stage algorithm in evolutionary product unit neural networks for classification
Expert Systems with Applications: An International Journal
Journal of Global Optimization
Interpolated differential evolution for global optimisation problems
International Journal of Computing Science and Mathematics
Structural and Multidisciplinary Optimization
Information Sciences: an International Journal
Evolutionary algorithms for the design of grid-connected PV-systems
Expert Systems with Applications: An International Journal
Optimization of a pumping ship trajectory to clean oil contamination in the open sea
Mathematical and Computer Modelling: An International Journal
An efficient dynamic load balancing algorithm
Computational Mechanics
Impact of static and adaptive mutation techniques on the performance of Genetic Algorithm
International Journal of Hybrid Intelligent Systems
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Genetic algorithms are commonly used metaheuristics for global optimization, but there has been very little research done on the generation of their initial population. In this paper, we look for an answer to the question whether the initial population plays a role in the performance of genetic algorithms and if so, how it should be generated. We show with a simple example that initial populations may have an effect on the best objective function value found for several generations. Traditionally, initial populations are generated using pseudo random numbers, but there are many alternative ways. We study the properties of different point generators using four main criteria: the uniform coverage and the genetic diversity of the points as well as the speed and the usability of the generator. We use the point generators to generate initial populations for a genetic algorithm and study what effects the uniform coverage and the genetic diversity have on the convergence and on the final objective function values. For our tests, we have selected one pseudo and one quasi random sequence generator and two spatial point processes: simple sequential inhibition process and nonaligned systematic sampling. In numerical experiments, we solve a set of 52 continuous test functions from 16 different function families, and analyze and discuss the results.