Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Intelligent Mutation Rate Control in Canonical Genetic Algorithms
ISMIS '96 Proceedings of the 9th International Symposium on Foundations of Intelligent Systems
Chemical genetic algorithms: coevolution between codes and code translation
ICAL 2003 Proceedings of the eighth international conference on Artificial life
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Rigorous Runtime Analysis of Inversely Fitness Proportional Mutation Rates
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Dynamic Crowding Distance?A New Diversity Maintenance Strategy for MOEAs
ICNC '08 Proceedings of the 2008 Fourth International Conference on Natural Computation - Volume 01
Improving NSGA-II Algorithm Based on Minimum Spanning Tree
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
Design and comparison of two evolutionary approaches for solving the Rubik's cube
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II
Effects of the existence of highly correlated objectives on the behavior of MOEA/D
EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization
Proceedings of the 13th annual conference on Genetic and evolutionary computation
How crossover helps in pseudo-boolean optimization
Proceedings of the 13th annual conference on Genetic and evolutionary computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
An analysis of the behavior of simplified evolutionary algorithms on trap functions
IEEE Transactions on Evolutionary Computation
Impact of different recombination methods in a mutation-specific MOEA for a biochemical application
EvoBIO'13 Proceedings of the 11th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
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In many physiochemical and biological phenomena, molecules have to comply with multiple optimized biophysical feature constraints. Mathematical modeling of these biochemical problems consequently results in multi-objective optimization. This study presents a special fast non-dominated sorting genetic algorithm (GA) incorporating different types of mutation (referred to as MSNSGA-II) for resolving multiple diverse requirements for molecule bioactivity with an early convergence in a comparable low number of generations. Hence, MSNSGA-II is based on a character codification and its performance is benchmarked via a specific three-dimensional optimization problem. Three objective functions are provided by the BioJava library: Needleman Wunsch algorithm, hydrophilicity and molecular weight. The performance of our proposed algorithm is tested using several mutation operators: A deterministic dynamic, a self-adaptive, a dynamic adaptive and two further mutation schemes with mutation rates based on the Gaussian distribution. Furthermore, we expose the comparison of MSNSGA-II with the classic NSGA-II in performance.