ACM Transactions on Mathematical Software (TOMS)
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Swarm intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms: Concepts and Designs with Disk
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Constrained Test Problems for Multi-objective Evolutionary Optimization
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Archiving With Guaranteed Convergence And Diversity In Multi-objective Optimization
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Constrained genetic algorithms and their applications in nonlinear constrained optimization
ICTAI '00 Proceedings of the 12th IEEE International Conference on Tools with Artificial Intelligence
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Ant Colony Optimization
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Hybrid Wavelet Model Construction Using Orthogonal Forward Selection with Boosting Search
FSKD '07 Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 03
An overview of evolutionary algorithms in multiobjective optimization
Evolutionary Computation
Multi-objective genetic algorithms: Problem difficulties and construction of test problems
Evolutionary Computation
Hybrid wavelet model construction using orthogonal forward selection with boosting search
International Journal of Business Intelligence and Data Mining
Differential evolution algorithm with strategy adaptation for global numerical optimization
IEEE Transactions on Evolutionary Computation
Construction of tunable radial basis function networks using orthogonal forward selection
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Sparse RBF Networks with Multi-kernels
Neural Processing Letters
Evolutionary multi-objective optimization: a historical view of the field
IEEE Computational Intelligence Magazine
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
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
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Experiments with repeating weighted boosting search for optimization signal processing applications
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Genetic Algorithms and Very Fast Simulated Reannealing: A comparison
Mathematical and Computer Modelling: An International Journal
Simulated annealing: Practice versus theory
Mathematical and Computer Modelling: An International Journal
IEEE Transactions on Neural Networks
Particle swarm optimization with increasing topology connectivity
Engineering Applications of Artificial Intelligence
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Repeated weighted boosting search (RWBS) optimisation is a guided stochastic search algorithm that is capable of handling the difficult optimisation problems with non-smooth and/or multi-modal cost functions. Compared with other alternatives for global optimisation solvers, such as the genetic algorithms and adaptive simulated annealing, RWBS is easier to implement, has fewer algorithmic parameters to tune and has been shown to provide similar levels of performance on many benchmark problems. In its original form, however, RWBS is only applicable to unconstrained, single-objective problems with continuous search spaces. This contribution begins with an analysis of the performance of the original RWBS algorithm and then proceeds to develop a number of novel extensions to the algorithm which facilitate its application to a more general class of optimisation problems, including those with discrete and mixed search spaces as well as multiple objective functions. The performance of the extended or generalised RWBS algorithms are compared with other standard techniques on a range of benchmark problems, and the results obtained demonstrate that the proposed generalised RWBS algorithms offer excellent performance whilst retaining the benefits of the original RWBS algorithm.