Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Numerical Optimization of Computer Models
Numerical Optimization of Computer Models
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
A Variant of Evolution Strategies for Vector Optimization
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
Multi-Objective Optimization by Means of the Thermodynamical Genetic Algorithm
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
An overview of evolutionary algorithms in multiobjective optimization
Evolutionary Computation
A Short Tutorial on Evolutionary Multiobjective Optimization
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Proceedings of the 9th annual conference on Genetic and evolutionary computation
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
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Since the 60s, several approaches (genetic algorithms, evolution strategies etc.) have been developed which apply evolutionary concepts for simulation and optimization purposes. Also in the area of multiobjective programming, such approaches (mainly genetic algorithms) have already been used (Evolutionary Computation 3(1), 1–16).In our presentation, we consider a generalization of common approaches like evolution strategies: a multiobjective evolutionary algorithm (MOEA) for analyzing decision problems with alternatives taken from a real-valued vector space and evaluated according to several objective functions. The algorithm is implemented within the Learning Object-Oriented Problem Solver (LOOPS) framework developed by the author. Various test problems are analyzed using the MOEA: (multiobjective) linear programming, convex programming, and global programming. Especially for ‘hard’ problems with disconnected or local efficient regions, the algorithms seems to be a useful tool.