Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
Dynamic programming: deterministic and stochastic models
Dynamic programming: deterministic and stochastic models
Adapting operator probabilities in genetic algorithms
Proceedings of the third international conference on Genetic algorithms
Time-dependent utility and action under uncertainty
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Computation and action under bounded resources
Computation and action under bounded resources
Deliberation scheduling for problem solving in time-constrained environments
Artificial Intelligence
Operational rationality through compilation of anytime algorithms
Operational rationality through compilation of anytime algorithms
Timing driven placement for large standard cell circuits
DAC '95 Proceedings of the 32nd annual ACM/IEEE Design Automation Conference
Design-to-time scheduling and anytime algorithms
ACM SIGART Bulletin
Adaptive operator probabilities in a genetic algorithm that applies three operators
SAC '97 Proceedings of the 1997 ACM symposium on Applied computing
Artificial Intelligence - special issue on computational tradeoffs under bounded resources
Optimal schedules for monitoring anytime algorithms
Artificial Intelligence - special issue on computational tradeoffs under bounded resources
Monitoring and control of anytime algorithms: a dynamic programming approach
Artificial Intelligence - special issue on computational tradeoffs under bounded resources
Dragon2000: standard-cell placement tool for large industry circuits
Proceedings of the 2000 IEEE/ACM international conference on Computer-aided design
Proceedings of the 3rd International Conference on Genetic Algorithms
Dynamic Control of Genetic Algorithms Using Fuzzy Logic Techniques
Proceedings of the 5th International Conference on Genetic Algorithms
Adaptive crossover in genetic algorithms using statistics mechanism
ICAL 2003 Proceedings of the eighth international conference on Artificial life
Improving the Performance of CAD Optimization Algorithms Using On-Line Meta-Level Control
VLSID '06 Proceedings of the 19th International Conference on VLSI Design held jointly with 5th International Conference on Embedded Systems Design
Using performance profile trees to improve deliberation control
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
An adaptive framework for solving multiple hard problems under time constraints
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
A genetic approach to standard cell placement using meta-genetic parameter optimization
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
On-the-fly calibrating strategies for evolutionary algorithms
Information Sciences: an International Journal
Digital IIR filter design using multi-objective optimization evolutionary algorithm
Applied Soft Computing
A new evolutionary algorithm using shadow price guided operators
Applied Soft Computing
Handling boundary constraints for particle swarm optimization in high-dimensional search space
Information Sciences: an International Journal
| Hi-index | 0.00 |
Parameter control of evolutionary algorithms (EAs) poses special challenges as EA uses a population and requires many parameters to be controlled for an effective search. Quality improvement is dependent on several factors, such as, fitness estimation, population diversity and convergence rate. A widely practiced approach to identify a good set of parameters for a particular class of problem is through experimentation. Ideally, the parameter selection should depend on the resource availability, and thus, a rigid choice may not be suitable. In this work, we propose an automated framework for parameter selection, which can adapt according to the constraints specified. To condition the parameter choice through resource constraint/utilization, we consider two typical scenarios, one where maximum available run-time is pre-specified and the other in which a utility function modeling the quality-time trade-off is used instead of a rigid deadline. We present static and dynamic parameter selection strategies based on a probabilistic profiling method. Experiments performed with traveling salesman problem (TSP) and standard cell placement problem show that an informed adaptive parameter control mechanism can yield better results than a static selection.