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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
Multi-objected Optimization in Evolutionary Algorithms Using Satisfiability Classes
Proceedings of the 6th International Conference on Computational Intelligence, Theory and Applications: Fuzzy Days
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
An overview of evolutionary algorithms in multiobjective optimization
Evolutionary Computation
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
Distance Based Ranking in Many-Objective Particle Swarm Optimization
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Ranking Methods for Many-Objective Optimization
MICAI '09 Proceedings of the 8th Mexican International Conference on Artificial Intelligence
A grid-based fitness strategy for evolutionary many-objective optimization
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Alternative fitness assignment methods for many-objective optimization problems
EA'09 Proceedings of the 9th international conference on Artificial evolution
Many-Objective optimization: an engineering design perspective
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Alleviate the hypervolume degeneration problem of NSGA-II
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
An approach based on grid-value for selection of parents in multi-objective genetic algorithm
SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part I
On the effect of selection and archiving operators in many-objective particle swarm optimisation
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Refined ranking relations for multi objective optimization andapplication to P-ACO
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Borg: An auto-adaptive many-objective evolutionary computing framework
Evolutionary Computation
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Many optimisation problems in circuit design, in the following also refereed to as VLSI CAD, consist of mutually dependent sub-problems, where the resulting solutions must satisfy several requirements. Recently, a new model for Multi-Objective Optimisation (MOO) for applications in Evolutionary Algorithms (EAs) has been proposed. The search space is partitioned into so-called Satisfiability Classes (SCs), where each region represents the quality of the optimisation 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 optimisation objectives can be modelled. In this paper, the model is studied in further detail. Various properties are shown and advantages and disadvantages are discussed. The relations to other techniques are presented and experimental results are given to demonstrate the efficiency of the model.