Genetic Programming III: Darwinian Invention & Problem Solving
Genetic Programming III: Darwinian Invention & Problem Solving
Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
A Genetic Algorithm for Multiobjective Robust Design
Applied Intelligence
A computationally efficient evolutionary algorithm for real-parameter optimization
Evolutionary Computation
Creating Robust Solutions by Means of Evolutionary Algorithms
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Designing evolutionary algorithms for dynamic optimization problems
Advances in evolutionary computing
Genetic Programming IV: Routine Human-Competitive Machine Intelligence
Genetic Programming IV: Routine Human-Competitive Machine Intelligence
The Hierarchical Fair Competition (HFC) Framework for Sustainable Evolutionary Algorithms
Evolutionary Computation
Trade-off between performance and robustness: an evolutionary multiobjective approach
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Automated synthesis of analog electrical circuits by means ofgenetic programming
IEEE Transactions on Evolutionary Computation
Genetic algorithms with a robust solution searching scheme
IEEE Transactions on Evolutionary Computation
Robust design of multilayer optical coatings by means ofevolutionary algorithms
IEEE Transactions on Evolutionary Computation
A circuit representation technique for automated circuit design
IEEE Transactions on Evolutionary Computation
ICES '08 Proceedings of the 8th international conference on Evolvable Systems: From Biology to Hardware
Avoiding the pitfalls of noisy fitness functions with genetic algorithms
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Genetic Programming and Evolvable Machines
Combining multiple evolved analog circuits for robust evolvable hardware
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Open-ended evolution to discover analogue circuits for beyond conventional applications
Genetic Programming and Evolvable Machines
Swarm intelligence: making differences in analogue circuits structure for fault-tolerance
International Journal of Computer Applications in Technology
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Most existing research on robust design using evolutionary algorithms (EA) follows the paradigm of traditional robust design, in which parameters of a design solution are tuned to improve the robustness of the system. However, the topological structure of a system may set a limit on the possible robustness achievable through parameter tuning. This paper proposes a new robust design paradigm that exploits the open-ended topological synthesis capability of genetic programming to evolve more robust systems. As a case study, a methodology for automated synthesis of dynamic systems, based on genetic programming and bond graph modeling (GPBG), is applied to evolve robust low-pass and high-pass analog filters. Compared with a traditional robust design approach based on a state-of-the-art real-parameter genetic algorithm (GA), it is shown that open-ended topology search by genetic programming with a fitness criterion rewarding robustness can evolve more robust systems with respect to parameter perturbations than what was achieved through parameter tuning alone, for our test problems.