Computers and Industrial Engineering - Special issue on computational intelligence for industrial engineering
Subcost-Guided Search—Experiments with Timetabling Problems
Journal of Heuristics
Epistasis in Genetic Algorithms: An Experimental Design Perspective
Proceedings of the 6th International Conference on Genetic Algorithms
Reducing Local Optima in Single-Objective Problems by Multi-objectivization
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Do additional objectives make a problem harder?
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A tunable model for multi-objective, epistatic, rugged, and neutral fitness landscapes
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Multiobjectivization by Decomposition of Scalar Cost Functions
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Evolutionary algorithms and multi-objectivization for the travelling salesman problem
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Searching under multi-evolutionary pressures
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Guiding single-objective optimization using multi-objective methods
EvoWorkshops'03 Proceedings of the 2003 international 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
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Multiobjectivizing the HP model for protein structure prediction
EvoCOP'12 Proceedings of the 12th European conference on Evolutionary Computation in Combinatorial Optimization
Locality-based multiobjectivization for the HP model of protein structure prediction
Proceedings of the 14th annual conference on Genetic and evolutionary computation
An improved multiobjectivization strategy for HP model-based protein structure prediction
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part II
Generation of tests for programming challenge tasks using multi-objective optimization
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
A hybrid harmony search algorithm for the flexible job shop scheduling problem
Applied Soft Computing
A block-based evolutionary algorithm for flow-shop scheduling problem
Applied Soft Computing
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Multiple Objective Evolutionary Algorithms (MOEAs) applied to the Job-Shop Scheduling Problem have been shown to perform better than single objective Genetic Algorithms (GAs). Helper-objectives, representing portions of the main objective, help guide MOEAs in their search process. This paper provides additional understanding of helper-objective methods. The sequence in which helper-objectives are used is examined and we show that problem-specific knowledge can be incorporated to determine a good helper-objective sequence. Computational results demonstrate how carefully sequenced helper-objectives can improve search quality. This dismisses the established practice of picking helper sequence based upon a random order due to lack of knowledge about optimal sequencing. Explanations are provided for how helpers accelerate the search process by distinguishing between otherwise similar solutions and by partial removal of epistasis in one or more dimensions of the solution space. Helper-objective size was also explored to determine if maximal helper divisions are best for the set of problems studied. Helper-objective size appears to be important to the optimization and larger helpers are not necessarily better which implies that methods such as Multi-Objectivization via Segmentation (MOS) may benefit from smaller problem divisions. Lastly, an examination of the non-dominated front size was performed to determine if tuning front size makes sense for this type of algorithm since previous works have established tuning front size as important. No evidence was found to support tuning and the correlation between small front size and effectiveness appears to be a natural part of how helper-objective algorithms work rather than a reason for reducing front size.