A connectionist machine for genetic hillclimbing
A connectionist machine for genetic hillclimbing
Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Niching methods for genetic algorithms
Niching methods for genetic algorithms
Learning gene linkage to efficiently solve problems of bounded difficulty using genetic algorithms
Learning gene linkage to efficiently solve problems of bounded difficulty using genetic algorithms
Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
A Survey of Optimization by Building and Using Probabilistic Models
Computational Optimization and Applications
Improving Genetic Algorithms with Sharing through Cluster Analysis
Proceedings of the 5th International Conference on Genetic Algorithms
RapidAccurate Optimization of Difficult Problems Using Fast Messy Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
Finding Multimodal Solutions Using Restricted Tournament Selection
Proceedings of the 6th International Conference on Genetic Algorithms
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
The nature of niching: genetic algorithms and the evolution of optimal, cooperative populations
The nature of niching: genetic algorithms and the evolution of optimal, cooperative populations
Linkage Problem, Distribution Estimation, and Bayesian Networks
Evolutionary Computation
Linkage identification by non-monotonicity detection for overlapping functions
Evolutionary Computation
Sub-structural niching in non-stationary environments
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Evaluation relaxation using substructural information and linear estimation
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Do not match, inherit: fitness surrogates for genetics-based machine learning techniques
Proceedings of the 9th annual conference on Genetic and evolutionary computation
UMDAs for dynamic optimization problems
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Investigating restricted tournament replacement in ECGA for non-stationary environments
Proceedings of the 10th annual conference on Genetic and evolutionary computation
The crowding approach to niching in genetic algorithms
Evolutionary Computation
Linkage Learning, Rule Representation, and the Χ-Ary Extended Compact Classifier System
Learning Classifier Systems
Ensemble of niching algorithms
Information Sciences: an International Journal
Sensibility of linkage information and effectiveness of estimated distributions
Evolutionary Computation
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We propose a sub-structural niching method that fully exploits the problem decomposition capability of linkage-learning methods such as the estimation distribution algorithms and concentrate on maintaining diversity at the sub-structural level. The proposed method consists of three key components: (1) Problem decomposition and sub-structure identification, (2) sub-structure fitness estimation, and (3) sub-structural niche preservation. The sub-structural niching method is compared to restricted tournament selection (RTS)---a niching method used in hierarchical Bayesian optimization algorithm---with special emphasis on sustained preservation of multiple global solutions of a class of boundedly-difficult, additively-separable multimodal problems. The results show that sub-structural niching successfully maintains multiple global optima over large number of generations and does so with significantly less population than RTS. Additionally, the market share of each of the niche is much closer to the expected level in sub-structural niching when compared to RTS.