Combining User Knowledge and Online Innovization for Faster Solution to Multi-objective Design Optimization Problems

[1]  V. D. Angelis,et al.  Microgrid Energy Storage Design for Reliability and Cost Performances , 2020, 2020 IEEE Power & Energy Society General Meeting (PESGM).

[2]  Harald Kloft,et al.  Design of prefabricated wall-floor building systems using meta-heuristic optimization algorithms , 2020 .

[3]  Sanjeev Srivastava,et al.  Battery life-cycle optimization and runtime control for commercial buildings demand side management: A New York City case study , 2018, Energy.

[4]  Kalyanmoy Deb,et al.  Effect of size and order of variables in rules for multi-objective repair-based innovization procedure , 2017, 2017 IEEE Congress on Evolutionary Computation (CEC).

[5]  Xia Wei,et al.  Regenerable Cu-intercalated MnO2 layered cathode for highly cyclable energy dense batteries , 2017, Nature Communications.

[6]  Kalyanmoy Deb,et al.  Higher and lower-level knowledge discovery from Pareto-optimal sets , 2013, J. Glob. Optim..

[7]  Pavel Burget,et al.  NETWORK TOPOLOGY DESIGN , 2011 .

[8]  Andrzej Jaszkiewicz,et al.  Pareto memetic algorithm with path relinking for bi-objective traveling salesperson problem , 2009, Eur. J. Oper. Res..

[9]  Aravind Srinivasan,et al.  Innovization: innovating design principles through optimization , 2006, GECCO.

[10]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[11]  Selen Cremaschi,et al.  Planning pharmaceutical clinical trials under outcome uncertainty , 2018 .

[12]  G. R. Milne,et al.  Provisions for Re-energizing the Eectric System of the Consolidated Edison Company of New York, Inc. , 1940, Transactions of the American Institute of Electrical Engineers.