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
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Empirical investigation of multiparent recombination operators in evolution strategies
Evolutionary Computation
Real royal road functions-where crossover provably is essential
Discrete Applied Mathematics - Special issue: Boolean and pseudo-boolean funtions
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
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
Divide and evolve driven by human strategies
SEAL'10 Proceedings of the 8th international conference on Simulated evolution and learning
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
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Peptides play a key role in the development of drug candidates and diagnostic interventions, respectively. The design of peptides is cost-intensive and difficult in general for several well-known reasons. Multi-objective evolutionary algorithms (MOEAs) introduce adequate in silico methods for finding optimal peptides sequences which optimize several molecular properties. A mutation-specific fast non-dominated sorting GA (termed MSNSGA-II) was especially designed for this purpose. In this work, an empirical study is presented about the performance of MSNSGA-II which is extended by optionally three different recombination operators. The main idea is to gain an insight into the significance of recombination for the performance of MSNSGA-II in general - and to improve the performance with these intuitive recombination methods for biochemical optimization. The benchmark test for this study is a three-dimensional optimization problem, using fitness functions provided by the BioJava library.