Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Niching methods for genetic algorithms
Niching methods for genetic algorithms
GAS, a concept on modeling species in genetic algorithms
Artificial Intelligence
A species conserving genetic algorithm for multimodal function optimization
Evolutionary Computation
An Investigation of Niche and Species Formation in Genetic Function Optimization
Proceedings of the 3rd International Conference on Genetic Algorithms
Selected Papers from AISB Workshop on Evolutionary Computing
Niche identification techniques in multimodal genetic search with sharing scheme
Advances in Engineering Software
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
A comprehensive survey of fitness approximation in evolutionary computation
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Sub-structural niching in estimation of distribution algorithms
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
The Hierarchical Fair Competition (HFC) Framework for Sustainable Evolutionary Algorithms
Evolutionary Computation
Comparison of multi-modal optimization algorithms based on evolutionary algorithms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
A sequential niche technique for multimodal function optimization
Evolutionary Computation
Particle swarm optimization with preference order ranking for multi-objective optimization
Information Sciences: an International Journal
A hybrid coevolutionary algorithm for designing fuzzy classifiers
Information Sciences: an International Journal
An evolutionary algorithm with species-specific explosion for multimodal optimization
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Differential evolution algorithm with strategy adaptation for global numerical optimization
IEEE Transactions on Evolutionary Computation
A harmony search algorithm with ensemble of parameter sets
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Kernel-induced fuzzy clustering of image pixels with an improved differential evolution algorithm
Information Sciences: an International Journal
Ensemble strategies with adaptive evolutionary programming
Information Sciences: an International Journal
Ensemble of constraint handling techniques
IEEE Transactions on Evolutionary Computation
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
IEEE Transactions on Evolutionary Computation
Where Are the Niches? Dynamic Fitness Sharing
IEEE Transactions on Evolutionary Computation
A weighted sum validity function for clustering with a hybrid niching genetic algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Information Sciences: an International Journal
Disturbed Exploitation compact Differential Evolution for limited memory optimization problems
Information Sciences: an International Journal
Rosenbrock artificial bee colony algorithm for accurate global optimization of numerical functions
Information Sciences: an International Journal
Money in trees: How memes, trees, and isolation can optimize financial portfolios
Information Sciences: an International Journal
Eigenclassifiers for combining correlated classifiers
Information Sciences: an International Journal
Niching particle swarm optimization with local search for multi-modal optimization
Information Sciences: an International Journal
Evolutionary multimodal optimization using the principle of locality
Information Sciences: an International Journal
Information Sciences: an International Journal
Evaluating coevolution on a multimodal problem
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Ensemble of clearing differential evolution for multi-modal optimization
ICSI'12 Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part I
Environmental framework to visualize emergent artificial forest ecosystems
Information Sciences: an International Journal
Differential evolution algorithm: recent advances
TPNC'12 Proceedings of the First international conference on Theory and Practice of Natural Computing
Information Sciences: an International Journal
A multimodal problem for competitive coevolution
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
A multiset genetic algorithm for the optimization of deceptive problems
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Exploration and exploitation in evolutionary algorithms: A survey
ACM Computing Surveys (CSUR)
Computers and Operations Research
A Review of Niching Genetic Algorithms for Multimodal Function Optimization
Cybernetics and Systems Analysis
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Although niching algorithms have been investigated for almost four decades as effective procedures to obtain several good and diverse solutions of an optimization problem, no effort has been reported on combining different niching algorithms to form an effective ensemble of niching algorithms. In this paper, we propose an ensemble of niching algorithms (ENA) and illustrate the concept by an instantiation which is realized using four different parallel populations. The offspring of each population is considered by all parallel populations. The instantiation is tested on a set of 16 real and binary problems and compared against the single niching methods with respect to searching ability and computation time. Results confirm that ENA method is as good as or better than the best single method in it on every test problem. Moreover, comparison with other state-of-the-art niching algorithms demonstrates the competitiveness of our proposed ENA.