scholarly journals On Dealing with Uncertainties from Kriging Models in Offline Data-Driven Evolutionary Multiobjective Optimization

2019 ◽  
pp. 463-474 ◽  
Author(s):  
Atanu Mazumdar ◽  
Tinkle Chugh ◽  
Kaisa Miettinen ◽  
Manuel López-Ibáñez
Keyword(s):  
Multiobjective Optimization ◽  
Data Driven ◽  
Evolutionary Multiobjective Optimization

scholarly journals A repository of real-world datasets for data-driven evolutionary multiobjective optimization

2019 ◽  
Vol 6 (1) ◽  
pp. 189-197 ◽  
Author(s):  
Cheng He ◽  
Ye Tian ◽  
Handing Wang ◽  
Yaochu Jin
Keyword(s):  
Multiobjective Optimization ◽  
Real World ◽  
Optimization Problems ◽  
Data Driven ◽  
Benchmark Problems ◽  
Computational Costs ◽  
Evolutionary Multiobjective Optimization ◽  
Multiobjective Optimization Problems ◽  
Real World Applications ◽  
Real World Datasets

Abstract Many real-world optimization applications have more than one objective, which are modeled as multiobjective optimization problems. Generally, those complex objective functions are approximated by expensive simulations rather than cheap analytic functions, which have been formulated as data-driven multiobjective optimization problems. The high computational costs of those problems pose great challenges to existing evolutionary multiobjective optimization algorithms. Unfortunately, there have not been any benchmark problems reflecting those challenges yet. Therefore, we carefully select seven benchmark multiobjective optimization problems from real-world applications, aiming to promote the research on data-driven evolutionary multiobjective optimization by suggesting a set of benchmark problems extracted from various real-world optimization applications.

HYBRIDIZATION OF MULTIOBJECTIVE EVOLUTIONARY ALGORITHM WITH COEVOLUTION FOR ENEMY TEAM IN MS. PAC-MAN GAME

2014 ◽  
Vol 2 (1) ◽  
pp. 36-51
Author(s):  
Tse guan Tan ◽  
Jason Teo ◽  
On Chin Kim
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Multiobjective Optimization ◽  
Evolutionary Algorithm ◽  
Optimization Technique ◽  
Dynamic Game ◽  
Evolution Strategy ◽  
Evolutionary Multiobjective Optimization ◽  
Competitive Coevolution ◽  
Virtual Creatures

AbstrakKini, semakin ramai penyelidik telah menunjukkan minat mengkaji permainan Kecerdasan Buatan (KB).Permainan seumpama ini menyediakan tapak uji yang sangat berguna dan baik untuk mengkaji asasdan teknik-teknik KB. Teknik KB, seperti pembelajaran, pencarian dan perencanaan digunakan untukmenghasilkan agen maya yang mampu berfikir dan bertindak sewajarnya dalam persekitaran permainanyang kompleks dan dinamik. Dalam kajian ini, satu set pengawal permainan autonomi untuk pasukan hantudalam permainan Ms. Pac-man yang dicipta dengan menggunakan penghibridan Evolusi PengoptimumanMultiobjektif (EPM) dan ko-evolusi persaingan untuk menyelesaikan masalah pengoptimuman dua objektifiaitu meminimumkan mata dalam permainan dan bilangan neuron tersembunyi di dalam rangkaianneural buatan secara serentak. Arkib Pareto Evolusi Strategi (APES) digunakan, teknik pengoptimumanmultiobjektif ini telah dibuktikan secara saintifik antara yang efektif di dalam pelbagai aplikasi. Secarakeseluruhannya, keputusan eksperimen menunjukkan bahawa teknik pengoptimuman multiobjektif bolehmendapat manfaat daripada aplikasi ko-evolusi persaingan Abstract Recently, researchers have shown an increased interest in game Artificial Intelligence (AI). Gamesprovide a very useful and excellent testbed for fundamental AI research. The AI techniques, such aslearning, searching and planning are applied to generate the virtual creatures that are able to think andact appropriately in the complex and dynamic game environments. In this study, a set of autonomousgame controllers for the ghost team in the Ms. Pac-man game are created by using the hybridizationof Evolutionary Multiobjective Optimization (EMO) and competitive coevolution to solve the bi-objectiveoptimization problem of minimizing the game's score by eating Ms. Pac-man agent and the number ofhidden neurons in neural network simultaneously. The Pareto Archived Evolution Strategy (PAES) is usedthat has been proved to be an effective and efficient multiobjective optimization technique in variousapplications. Overall, the results show that multiobjective optimizer can benefit from the application ofcompetitive coevolutionary

Predicting DNA Motifs by Using Evolutionary Multiobjective Optimization

2012 ◽  
Vol 42 (6) ◽  
pp. 913-925 ◽  
Author(s):  
D. L. Gonzalez-Álvarez ◽  
M. A. Vega-Rodriguez ◽  
J. A. Gomez-Pulido ◽  
J. M. Sanchez-Pérez
Keyword(s):  
Multiobjective Optimization ◽  
Dna Motifs ◽  
Evolutionary Multiobjective Optimization
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