Graph-Based Algorithms for Boolean Function Manipulation
IEEE Transactions on Computers
Introduction to algorithms
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Learning heuristics by genetic algorithms
ASP-DAC '95 Proceedings of the 1995 Asia and South Pacific Design Automation Conference
EXPLORER: an interactive floorplanner for design space exploration
EURO-DAC '96/EURO-VHDL '96 Proceedings of the conference on European design automation
Evolutionary algorithms for VLSI CAD
Evolutionary algorithms for VLSI CAD
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Learning Heuristics for OBDD Minimization by Evolutionary Algorithms
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Heuristic Learning Based on Genetic Programming
EuroGP '01 Proceedings of the 4th European Conference on Genetic Programming
Multi-objective Optimisation Based on Relation Favour
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Proceedings of the 23rd ACM international conference on Great lakes symposium on VLSI
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Many optimization problems consist of several mutually dependent subproblems, where the resulting solutions must satisfy all requirements. We propose a new model for Multi-Objective Optimization (MOO) in Evolutionary Algorithms (EAs). The search space is partitioned into so-called Satisfiability Classes fSCj, where each region represents the quality of the optimization criteria. Applying the SCs to individuals in a population a fitness can be assigned during the EA run. The model also allows the handling of infeasible regions and restrictions in the search space. Additionally, different priorities for optimization objectives can be modeled. Advantages of the model over previous approaches are discussed and an application is given that shows the superiority of the method for modeling MOO problems.