Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Dynamic Parameter Encoding for Genetic Algorithms
Machine Learning
Handbook of Evolutionary Computation
Handbook of Evolutionary Computation
Genetic Algorithms for Tracking Changing Environments
Proceedings of the 5th International Conference on Genetic Algorithms
Diversity-Guided Evolutionary Algorithms
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
An Informed Operator Approach to Tackle Diversity Constraints in Evolutionary Search
ITCC '04 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'04) Volume 2 - Volume 2
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
On the Explorative Behavior of MAX---MIN Ant System
SLS '09 Proceedings of the Second International Workshop on Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics
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One of the major causes of premature convergence in Evolutionary Algorithm (EA) is loss of population diversity, which pushes the search space to a homogeneous or a near-homogeneous configuration. In particular, this can be a more complicated issue in case of high dimensional complex problem domains. In [13, 14], we presented two novel EA frameworks to curb premature convergence by maintaining constructive diversity in the population. The COMMUNITY_GA or COUNTER_NICHING_GA in [13] uses an informed exploration technique to maintain constructive diversity. In addition to this, the POPULATION_GA model in [14] balances exploration and exploitation using a hierarchical multi-population approach. The current research presents further investigation on the later model which synergistically uses an exploration controlling mechanism through informed genetic operators along with a multi-tier hierarchical dynamic population architecture, which allows initially less fit individuals a fair chance to survive and evolve. Simulations using a set of popular benchmark test functions showed promising results.