Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
Combining convergence and diversity in evolutionary multiobjective optimization
Evolutionary Computation
A computationally efficient evolutionary algorithm for real-parameter optimization
Evolutionary Computation
Multiobjective Satisfaction within an Interactive Evolutionary Design Environment
Evolutionary Computation
Exploiting gradient information in numerical multi--objective evolutionary optimization
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Combining gradient techniques for numerical multi-objective evolutionary optimization
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Proceedings of the 8th annual conference on Genetic and evolutionary computation
A pareto following variation operator for fast-converging multiobjective evolutionary algorithms
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Gradient Based Stochastic Mutation Operators in Evolutionary Multi-objective Optimization
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
Using gradient-based information to deal with scalability in multi-objective evolutionary algorithms
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A local multiobjective optimization algorithm using neighborhood field
Structural and Multidisciplinary Optimization
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While genetically inspired approaches to multi-objective optimization have many advantages over conventional approaches, they do not explicitly exploit directional/gradient information. This paper describes how steepest-descent, multi-objective optimization theory can be combined with EC concepts to produce improved algorithms. It shows how approximate directional information can be efficiently extracted from parent individuals, and how a multi-objective gradient can be calculated, such that children individuals can be placed in appropriate, dominating search directions. The paper describes and introduces the basic theoretical concepts as well as demonstrating some of the concepts on a simple test problem.