Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Advances in Engineering Software
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms for Tracking Changing Environments
Proceedings of the 5th 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.
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
An analysis of multi-chromosome GAs in deceptive problems
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Analysis of a triploid genetic algorithm over deceptive landscapes
Proceedings of the 27th Annual ACM Symposium on Applied Computing
International Journal of Data Mining and Bioinformatics
Analysis of a triploid genetic algorithm over deceptive and epistatic landscapes
ACM SIGAPP Applied Computing Review
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This paper investigates how genetic algorithms (GAs) can be improved to solve large-scale and complex problems more efficiently. First of all, we review premature convergence, one of the challenges confronted with when applying GAs to real-world problems. Next, some of the methods now available to prevent premature convergence and their intrinsic defects are discussed. A qualitative analysis is then done on the cause of premature convergence that is the loss of building blocks hosted in less-fit individuals during the course of evolution. Thus, we propose a new improver - GAs with Reserve Selection (GARS), where a reserved area is set up to save potential building blocks and a selection mechanism based on individual uniqueness is employed to activate the potentials. Finally, case studies are done in a few standard problems well known in the literature, where the experimental results demonstrate the effectiveness and robustness of GARS in suppressing premature convergence, and also an enhancement is found in global optimization capacity.