Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
On the approximability of trade-offs and optimal access of Web sources
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Multicriteria Optimization
A fast linear-arithmetic solver for DPLL(T)
CAV'06 Proceedings of the 18th international conference on Computer Aided Verification
On SAT modulo theories and optimization problems
SAT'06 Proceedings of the 9th international conference on Theory and Applications of Satisfiability Testing
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
Pareto efficiency and approximate pareto efficiency in routing and load balancing games
SAGT'10 Proceedings of the Third international conference on Algorithmic game theory
Parametric identification of temporal properties
RV'11 Proceedings of the Second international conference on Runtime verification
Pareto curves for probabilistic model checking
ATVA'12 Proceedings of the 10th international conference on Automated Technology for Verification and Analysis
Approximating multi-objective scheduling problems
Computers and Operations Research
As soon as probable: optimal scheduling under stochastic uncertainty
TACAS'13 Proceedings of the 19th international conference on Tools and Algorithms for the Construction and Analysis of Systems
Symmetry breaking for multi-criteria mapping and scheduling on multicores
FORMATS'13 Proceedings of the 11th international conference on Formal Modeling and Analysis of Timed Systems
Diversely enumerating system-level architectures
Proceedings of the Eleventh ACM International Conference on Embedded Software
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We propose a general methodology for approximating the Pareto front of multi-criteria optimization problems. Our search-based methodology consists of submitting queries to a constraint solver. Hence, in addition to a set of solutions, we can guarantee bounds on the distance to the actual Pareto front and use this distance to guide the search. Our implementation, which computes and updates the distance efficiently, has been tested on numerous examples.