On the Average Number of Maxima in a Set of Vectors and Applications
Journal of the ACM (JACM)
Pareto OptimalityGA-Easiness and Deception (Extended Abstract)
Proceedings of the 5th International Conference on Genetic Algorithms
Fitness Landscapes Based on Sorting and Shortest Paths Problems
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Effects of diversity control in single-objective and multi-objective genetic algorithms
Journal of Heuristics
Multiobjective Optimization in Bioinformatics and Computational Biology
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Optimal antenna placement using a new multi-objective chc algorithm
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Optimal Triangulation in 3D Computer Vision Using a Multi-objective Evolutionary Algorithm
Proceedings of the 2007 EvoWorkshops 2007 on EvoCoMnet, EvoFIN, EvoIASP,EvoINTERACTION, EvoMUSART, EvoSTOC and EvoTransLog: Applications of Evolutionary Computing
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Triangulation using differential evolution
Evo'08 Proceedings of the 2008 conference on Applications of evolutionary computing
Deterministic helper-objective sequences applied to job-shop scheduling
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Helper-objective optimization strategies for the Job-Shop Scheduling Problem
Applied Soft Computing
Multimodal optimization using a bi-objective evolutionary algorithm
Evolutionary Computation
ICIRA'11 Proceedings of the 4th international conference on Intelligent Robotics and Applications - Volume Part II
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Encouraging reactivity to create robust machines
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
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This paper investigates the possibility of using multi-objective methods to guide the search when solving single-objective optimization problems with genetic algorithms. Using the job shop scheduling problem as an example, experiments demonstrate that by using helper-objectives (additional objectives guiding the search), the average performance of a standard GA can be significantly improved. The helper-objectives guide the search towards solutions containing good building blocks and helps the algorithm avoid local optima. The experiments reveal that the approach only works if the number of helper-objectives used simultaneously is low. However, a high number of helper-objectives can be used in the same run by changing the helper-objectives dynamically.