GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Convergence of stochastic search algorithms to gap-free pareto front approximations
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Combining low-level features for semantic extraction in image retrieval
EURASIP Journal on Advances in Signal Processing
Convergence of stochastic search algorithms to finite size pareto set approximations
Journal of Global Optimization
A Novel Multi-objective Evolutionary Algorithm
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007
New Model for Multi-objective Evolutionary Algorithms
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007
Journal of Global Optimization
Improving NSGA-II Algorithm Based on Minimum Spanning Tree
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
Combine LHS with MOEA to Optimize Complex Pareto Set MOPs
ISICA '08 Proceedings of the 3rd International Symposium on Advances in Computation and Intelligence
Theory of the hypervolume indicator: optimal μ-distributions and the choice of the reference point
Proceedings of the tenth ACM SIGEVO workshop on Foundations of genetic algorithms
Spread Assessment for Evolutionary Multi-Objective Optimization
EMO '09 Proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization
Multiplicative approximations and the hypervolume indicator
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Parallel multi-objective evolutionary algorithms on graphics processing units
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
A multi-objective approach for robust airline scheduling
Computers and Operations Research
On the complexity of computing the hypervolume indicator
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Computing gap free pareto front approximations with stochastic search algorithms
Evolutionary Computation
Approximating the Ɛ-efficient set of an MOP with stochastic search algorithms
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
Exploiting molecular dynamics for multi-objective optimization
Expert Systems with Applications: An International Journal
ICNC'09 Proceedings of the 5th international conference on Natural computation
On set-based multiobjective optimization
IEEE Transactions on Evolutionary Computation
A grid-based fitness strategy for evolutionary many-objective optimization
Proceedings of the 12th annual conference on Genetic and evolutionary computation
An archived-based stochastic ranking evolutionary algorithm (asrea) for multi-objective optimization
Proceedings of the 12th annual conference on Genetic and evolutionary computation
The maximum hypervolume set yields near-optimal approximation
Proceedings of the 12th annual conference on Genetic and evolutionary computation
A novel diversification strategy for multi-objective evolutionary algorithms
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Adaptive, convergent, and diversified archiving strategy for multiobjective evolutionary algorithms
Expert Systems with Applications: An International Journal
Tight bounds for the approximation ratio of the hypervolume indicator
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Enhancing diversity for average ranking method in evolutionary many-objective optimization
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
The logarithmic hypervolume indicator
Proceedings of the 11th workshop proceedings on Foundations of genetic algorithms
Hype: An algorithm for fast hypervolume-based many-objective optimization
Evolutionary Computation
On sequential online archiving of objective vectors
EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization
Information Sciences: an International Journal
MO-TRIBES, an adaptive multiobjective particle swarm optimization algorithm
Computational Optimization and Applications
A multi-objective particle swarm optimizer based on decomposition
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Convergence of hypervolume-based archiving algorithms I: effectiveness
Proceedings of the 13th annual conference on Genetic and evolutionary computation
MCPR'11 Proceedings of the Third Mexican conference on Pattern recognition
Achieving balance between proximity and diversity in multi-objective evolutionary algorithm
Information Sciences: an International Journal
Improving the anytime behavior of two-phase local search
Annals of Mathematics and Artificial Intelligence
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
An EMO algorithm using the hypervolume measure as selection criterion
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
The combative accretion model – multiobjective optimisation without explicit pareto ranking
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Hypervolume-based multiobjective optimization: Theoretical foundations and practical implications
Theoretical Computer Science
Locality-based multiobjectivization for the HP model of protein structure prediction
Proceedings of the 14th annual conference on Genetic and evolutionary computation
A new multi-objective evolutionary algorithm based on a performance assessment indicator
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Convergence of set-based multi-objective optimization, indicators and deteriorative cycles
Theoretical Computer Science
On the performance metrics of multiobjective optimization
ICSI'12 Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part I
Elitist archiving for multi-objective evolutionary algorithms: to adapt or not to adapt
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part II
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
Approximation quality of the hypervolume indicator
Artificial Intelligence
Information Sciences: an International Journal
A Modified micro Genetic Algorithm for undertaking Multi-Objective Optimization Problems
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Recent Advances in Soft Computing: Theories and Applications
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Search algorithms for Pareto optimization are designed to obtain multiple solutions, each offering a different trade-off of the problem objectives. To make the different solutions available at the end of an algorithm run, procedures are needed for storing them, one by one, as they are found. In a simple case, this may be achieved by placing each point that is found into an "archive" which maintains only nondominated points and discards all others. However, even a set of mutually nondominated points is potentially very large, necessitating a bound on the archive's capacity. But with such a bound in place, it is no longer obvious which points should be maintained and which discarded; we would like the archive to maintain a representative and well-distributed subset of the points generated by the search algorithm, and also that this set converges. To achieve these objectives, we propose an adaptive archiving algorithm, suitable for use with any Pareto optimization algorithm, which has various useful properties as follows. It maintains an archive of bounded size, encourages an even distribution of points across the Pareto front, is computationally efficient, and we are able to prove a form of convergence. The method proposed here maintains evenness, efficiency, and cardinality, and provably converges under certain conditions but not all. Finally, the notions underlying our convergence proofs support a new way to rigorously define what is meant by "good spread of points" across a Pareto front, in the context of grid-based archiving schemes. This leads to proofs and conjectures applicable to archive sizing and grid sizing in any Pareto optimization algorithm maintaining a grid-based archive.