GENOCOP: a genetic algorithm for numerical optimization problems with linear constraints
Communications of the ACM - Electronic supplement to the December issue
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
Multiplicity and Local Search in Evolutionary Algorithms to Build the Pareto Front
SCCC '00 Proceedings of the XX International Conference of the Chilean Computer Science Society
The Evolutionary Design and Synthesis of Non-Linear Digital VLSI Systems
EH '03 Proceedings of the 2003 NASA/DoD Conference on Evolvable Hardware
Adding learning to the cellular development of neural networks: Evolution and the baldwin effect
Evolutionary Computation
Comparison between lamarckian and baldwinian repair on multiobjective 0/1 knapsack problems
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
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Recent advances in quantum cascade lasers (QCLs) have enabled their use as (tunable) emission sources for chemical and biological spectroscopy, as well as allowed their demonstration in applications in medical diagnostics and potential homeland security systems. Finding the optimal design solution can be challenging, especially for lasers that operate in the terahertz region. The production process is prohibitive, so an optimization algorithm is needed to find high quality QCL designs. Past research attempts using multiobjective evolutionary algorithms (MOEAs) have found good solutions, but lacked a local search element that could enable them to find more effective solutions. This research looks at two memetic MOEAs that use a neighborhood search. Our baseline memetic MOEA used a simple neighborhood search, which is similar to other MOEA neighborhood searches found in the literature. Alternatively, our innovative multi-tiered memetic MOEA uses problem domain knowledge to change the temporal focus of the neighborhood search based on the generation. It is empirically shown that the multitiered memetic MOEA is able to find solutions that dominate the base-line memetic algorithm. Additional experiments suggest that using local search on only non-dominated individuals improves the effectiveness and efficiency of the algorithm versus applying the local search to dominated individuals as well. This research validates the importance of using multi-objective problem (MOP) domain knowledge in order to obtain the best results for a real world solution. It also introduces a new multitiered local search procedure that is able to focus the local search on specific critical elements of the problem at different stages in the optimization process.