Niching methods for genetic algorithms
Niching methods for genetic algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Self-Organizing Maps
When Selection Meets Seduction
Proceedings of the 6th International Conference on Genetic Algorithms
Genetic Algorithm Visualization Using Self-organizing Maps
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Gado: a genetic algorithm for continuous design optimization
Gado: a genetic algorithm for continuous design optimization
HEMO: a sustainable multi-objective evolutionary optimization framework
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Fitness sharing and niching methods revisited
IEEE Transactions on Evolutionary Computation
A clustering entropy-driven approach for exploring and exploiting noisy functions
Proceedings of the 2007 ACM symposium on Applied computing
A synergistic approach for evolutionary optimization
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Applying self-aggregation to load balancing: experimental results
Proceedings of the 3rd International Conference on Bio-Inspired Models of Network, Information and Computing Sytems
Multi-objective genetic local search algorithm using Kohonen's neural map
Computers and Industrial Engineering
Optimal contraction theorem for exploration-exploitation tradeoff in search and optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
To explore or to exploit: An entropy-driven approach for evolutionary algorithms
International Journal of Knowledge-based and Intelligent Engineering Systems
A two-stage algorithm in evolutionary product unit neural networks for classification
Expert Systems with Applications: An International Journal
LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and simulation and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part III
Analysis of exploration and exploitation in evolutionary algorithms by ancestry trees
International Journal of Innovative Computing and Applications
Ectropy of diversity measures for populations in Euclidean space
Information Sciences: an International Journal
Implicit elitism in genetic search
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
An empirical tool for analysing the collective behaviour of population-based algorithms
EvoApplications'12 Proceedings of the 2012t European conference on Applications of Evolutionary Computation
Exploration and exploitation in evolutionary algorithms: A survey
ACM Computing Surveys (CSUR)
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Exploration vs. exploitation is a well known issue in Evolutionary Algorithms. Accordingly, an unbalanced search can lead to premature convergence. GASOM, a novel Genetic Algorithm, addresses this problem by intelligent exploration techniques. The approach uses Self-Organizing Maps to mine data from the evolution process. The information obtained is successfully utilized to enhance the search strategy and confront genetic drift. This way, local optima are avoided and exploratory power is maintained. The evaluation of GASOM on well known problems shows that it effectively prevents premature convergence and seeks the global optimum. Particularly on deceptive and misleading functions it showed outstanding performance. Additionally, representing the search history by the Self-Organizing Map provides a visually pleasing insight into the state and course of evolution.