Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Evolutionary computation: toward a new philosophy of machine intelligence
Evolutionary computation: toward a new philosophy of machine intelligence
Chain growth algorithms for HP-type lattice proteins
RECOMB '97 Proceedings of the first annual international conference on Computational molecular biology
Practical genetic algorithms
Schema processing, proportional selection, and the misallocation of trials in genetic algorithms
Information Sciences: an International Journal - Special issue on frontiers in evolutionary algorithms
An updated survey of GA-based multiobjective optimization techniques
ACM Computing Surveys (CSUR)
The Simple Genetic Algorithm: Foundations and Theory
The Simple Genetic Algorithm: Foundations and Theory
Genetic Algorithms Plus Data Structures Equals Evolution Programs
Genetic Algorithms Plus Data Structures Equals Evolution Programs
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A complete and effective move set for simplified protein folding
RECOMB '03 Proceedings of the seventh annual international conference on Research in computational molecular biology
An Investigation of Niche and Species Formation in Genetic Function Optimization
Proceedings of the 3rd International Conference on Genetic Algorithms
Genetic drift in genetic algorithm selection schemes
IEEE Transactions on Evolutionary Computation
DFS Based Partial Pathways in GA for Protein Structure Prediction
PRIB '08 Proceedings of the Third IAPR International Conference on Pattern Recognition in Bioinformatics
Twin Removal in Genetic Algorithms for Protein Structure Prediction Using Low-Resolution Model
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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The schemata theorem, on which the working of Genetic Algorithm (GA) is based in its current form, has a fallacious selection procedure and incomplete crossover operation. In this paper, generalization of the schemata theorem has been provided by correcting and removing these limitations. The analysis shows that similarity growth within GA population is inherent due to its stochastic nature. While the stochastic property helps in GA's convergence. The similarity growth is responsible for stalling and becomes more prevalent for hard optimization problem like protein structure prediction (PSP). While it is very essential that GA should explore the vast and complicated search landscape, in reality, it is often stuck in local minima. This paper shows that, removal of members of population having certain percentage of similarity would keep GA perform better, balancing and maintaining convergence property intact as well as avoids stalling.